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University of Southern California Dissertations and Theses

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Community integration of individuals with serious mental illness: a network perspective from India and United States

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Community integration of individuals with serious mental illness: a network perspective from India and United States

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i

COMMUNITY
INTEGRATION
OF
INDIVIDUALS
WITH

SERIOUS
MENTAL
ILLNESS:
A
NETWORK
PERSPECTIVE

FROM
INDIA
AND
UNITED
STATES

by

Rohini
Pahwa

A
Dissertation
Presented
to
the

FACULTY
OF
THE
USC
GRADUATE
SCHOOL

UNIVERSITY
OF
SOUTHERN
CALIFORNIA

In
Partial
Fulfillment
of
the

Requirements
for
the
Degree

DOCTOR
OF
PHILOSOPHY

(SOCIAL
WORK)

December
2013

Copyright
2013

Rohini
Pahwa

ii

DEDICATION

The
following
dissertation
is
dedicated
to
my
parents,
Saroj
and
Ashok
Pahwa,

without
whom
this
dissertation
would
not
have
been
possible.
Thank
you
for
providing
me

with
roots
to
ground
myself
in
while
also
giving
me
wings
to
fly
and
explore
who
I
could
be

and
what
I
could
achieve.
Your
unconditional
love,
support,
and
faith
have
kept
me
going.

iii

ACKNOWLEDGEMENTS

I
would
like
to
thank
everyone
instrumental
in
the
successful
completion
of
this

endeavor.
First
and
foremost,
I
would
like
to
thank
my
dissertation
chair
and
academic

mentor,
Dr.
John
Brekke,
for
his
support,
guidance,
and
encouragement.
I
appreciate
all
the

learning
opportunities
that
equipped
me
with
the
tools
to
conceptualize
and
complete
my

own
study.
Thank
you,
John,
for
your
faith
in
me,
which
has
kept
me
going
even
when
I

wanted
to
give
up.
You
have
helped
me
be
a
better
researcher
and,
more
importantly,
a

better
human
being.

I
would
also
like
to
express
my
heart-‐felt
gratitude
to
Dr.
Eric
Rice
for
guiding
me

through
the
world
of
social
networks,
for
being
my
sounding
board,
reading
my
drafts,

keeping
me
on
track,
and
giving
me
invaluable
feedback.
Eric,
I
appreciate
your
efforts
in

finding
coherence
in
my
occasional
incoherent
thoughts
and
ideas.

I
would
also
like
to
thank
Dr.
B.S.
Chavan
for
being
my
guide
and
collaborator
in

India.
I
am
deeply
grateful
to
you
for
your
help
in
navigating
through
the
mental
health

service
delivery
systems
in
India
and
all
your
support
through
the
process
of
collecting
my

dissertation
data.

I
would
also
like
to
thank
the
mental
health
cluster,
in
particular
Dr.
Concepcion

Barrio,
for
her
continual
guidance,
support,
and
encouragement.
I
also
wish
to
acknowledge

the
support
of
the
USC
School
of
Social
Work’s
doctoral
program,
Dr.
Michalle
Mor
Barak,

the
program
chair,
and
Malinda
Sampson,
the
program
coordinator,
for
all
their

educational
and
financial
support
throughout
my
doctoral
program.

This
process
would
not
have
been
possible
without
the
unyielding
support
of
my

family,
especially
my
parents,
siblings,
and
a
few
very
close
friends
for
being
there.
A
very

iv

special
thanks
to
my
husband,
Asim
Nigam,
for
being
my
emotional
rock.
I
appreciate
your

never-‐ending
patience
and
unrelenting
support
through
my
bouts
of
self-‐doubt
and
for

helping
me
through
the
dissertation
writing
process.

Last,
but
not
the
least,
I
would
like
to
thank
my
friends,
both
in
the
doctoral
program

and
outside,
for
their
encouragement
and
help.
I
am
grateful
for
the
support
I
received
from

my
cohort
members
and
other
students
in
the
mental
health
cluster,
especially
Liat
Kriegel

and
Anthony
Fulginiti,
for
helping
me
collect
data
for
this
study.

v

TABLE
OF
CONTENTS

Dedication

ii

Acknowledgements

iii

List
of
Tables
vii

List
of
Figures
ix

Abstract
x

CHAPTER
ONE:
INTRODUCTION
AND
OVERVIEW
1

Background
of
the
Problem
1

Purpose
and
Contributions
of
the
Present
Study
3

Specific
Aims
and
Hypotheses
6

Overview
of
Methodology
7

Organization
of
the
Study
7

CHAPTER
TWO:
LITERATURE
REVIEW
AND
THEORETICAL
BACKGROUND
9

The
Cultural
Context
of
Mental
Illness
9

Mental
Health
in
India,
Social
Support,
and
Stigma
11

Community
and
Theoretical
Approaches
to
Understanding
Community
13

Community
Integration
and
its
Theoretical
Frameworks
16

Social
Networks,
Social
Resources,
and
Social
Support:
How
They

Inform
Community
Integration

18

Stigma
in
Mental
Health
28

Stigma
and
Disclosure
about
Mental
Illness
30

Community
Integration:
Normalization
versus
Subcultures
31

Research
on
Community
Integration
of
the
Individuals
with
SMI
33

CHAPTER
THREE:
METHODS
36

Data
Source
36

Preliminary
Studies
36

Recruitment
of
the
Sample
38

Data
Collection
40

Data
Analysis
50

CHAPTER
FOUR:
RESULTS
57

Description
of
the
Sample
57

Results
for
Research
Aim
1:
Physical,
social,
and
psychological

integration
community
integration
of
individuals
with
SMI
in
Indian

and
U.S.
samples:

61

Results
for
Research
Aim
2:
Psychosocial
and
network
variables
78

vi

associated
with
the
three
dimensions
of
community
integration
for

individuals
with
SMI
in
India
and
United
States

Results
for
Research
Aim
3:
Multilevel
analysis
with
individual
and

network-‐level
variables
associated
with
disclosure
about
mental
illness

93

CHAPTER
FIVE:
DISCUSSION
96

Network
Differences:
India
and
the
United
States
99

Bridging
and
Bonding
Social
Capital:
A
Social
Networks
Phenomenon
in

India
and
the
United
States

102

Regression
Models
of
Community
Integration
103

Implications
for
Social
Work
Research
and
Practice
108

Limitations,
Strengths,
and
Suggestions
for
Further
Study
110

Conclusion
112

REFERENCES
113

vii

LIST
OF
TABLES

Table
1:
Demographic
Characteristics
of
the
Whole
Sample

59

Table
2:

Psychosocial
Characteristics
of
Samples
from
India
and
United

States
of
Individuals
with
SMI

61

Table
3:
Comparison
between
United
States
Regarding
their
Physical,

Psychological,
and
Social
Community
Integration
Characteristics

64

Table
4:
Comparison
between
United
States
and
India
In
Terms
of
Their

Network
Characteristics

66

Table
5:
Social
Support
Network
Characteristics
(Emotional-‐Informational

Social
Support:
Sharing
Personal
Problems)

68

Table
6:
Social
Support
Network
Characteristics
(Positive
Social
Interaction:

Do
Something
Enjoyable)

69

Table
7:
Social
Resources
Network
Characteristics:
Domestic
Resources

Type
(Finding
Bargains)

71

Table
8:
Social
Resources
Network
Characteristics
(Expert
Advice
Type:

Advice
about
Earning
Money)

73

Table
9:
Social
Resources
Network
Characteristics
(Personal
Skills
Type:

Taking
Care
of
Health)

74

Table
10:
Social
Resources
Network
Characteristics
(Problem
Solving
Type:

Fixing
Things
around
the
House)

76

Table
11:
Stigma
Network
Characteristics
(Not
Wish
to
Tell
or
Wish
the

Network
Members
Did
Not
Know
about
Their
Mental
Illness)

78

Table
12:
Correlation
between
Variables
Used
in
the
Regression
Models

80

Table
13:
Multivariate
Regression
of
Physical
Community
Integration

(Involvement
in
Community
Activities)
of
Individuals
with
SMI

83

Table
14:
Multivariate
Regression
of
Physical
Community
Integration
(Total

Social
Resources)
of
Individuals
with
SMI

84

Table
15:
Multivariate
Regression
of
Psychological
Community
Integration
in

Mental
Health
Community
of
Individuals
with
SMI

87

Table
16:
Multivariate
Regression
of
Psychological
Community
Integration
in
90

viii

Non-‐Mental
Health
Community
of
Individuals
with
SMI

Table
17:
Multivariate
Regression
of
Social
Community
Integration
(Perceived

Social
Support)
in
Individuals
with
SMI

93

Table
18:
Multilevel
Analysis
with
Individual-‐
and
Network-‐Level
Variables

Associated
with
Unwillingness
to
Disclose
about
Mental
Illness

96

ix

LIST
OF
FIGURES

Figure
1:
Wong
and
Solomon’s
model
of
community
integration
and
its

dimensions.

19

x

ABSTRACT

Community
integration
is
an
important
treatment
outcome
integral
to
the
recovery

process
for
individuals
with
severe
mental
illness.
However,
little
empirical
work
examines

the
experience
and
characteristics
of
community
integration
for
the
seriously
mentally
ill
in

community-‐based
settings.
Additionally,
cultural
factors
are
known
to
have
tremendous

impact
on
the
conception,
course,
and
outcomes
associated
with
the
illness,
along
with
the

experience
of
symptoms,
types,
and
acceptability
of
treatments
and
social
tolerance
of

mental
illnesses.
The
differential
courses
of
illness
and
prognoses
have
implications
for
the

cross-‐cultural
and
cross-‐national
generalizations
in
terms
of
research
and
practice.
Existing

empirical
evidence
on
community
integration
fails
to
consider
this
difference.
Keeping
this

in
mind,
the
primary
goal
of
this
dissertation
was
to
explore
the
concepts
of
community

integration
in
India
and
the
United
States
to
bridge
this
theoretical
and
conceptual

disconnect.

Using
the
conceptual
framework
of
Wong
and
Solomon

(2002),
we
aimed
to
explore

the
idea
of
community
and
community
integration
of
individuals
with
serious
mental

illness
who
are
receiving
treatment
in
a
public
health
care
setting
within
the
socio-‐cultural

contexts
of
India
and
United
States
and
to
examine
the
associated
network
and

psychosocial
factors.
Data
from
30
individuals
from
the
United
States
and
26
individuals

from
India
were
collected.
Community
integration
was
assessed
using
social
network

interviews
and
a
battery
of
quantitative
measures,
including
measures
of
involvement
in

community
activities,
social
resources,
social
support,
and
subjective
sense
of
integration.

Associated
psychosocial
factors
were
assessed
using
measures
of
internalized
stigma,

symptomatology,
and
psychosocial
functioning.
Overall,
the
findings
suggested
that
in

xi

terms
of
community
integration,
the
two
countries
were
similar
in
some
dimensions
and

differed
in
others.
Specifically,
the
Indian
sample,
in
general,
were
found
to
be
less

integrated
in
the
mental
health
community,
rated
by
the
interviewer
to
be
more
integrated

in
the
non-‐mental
health
community,
had
higher
levels
of
perceived
social
support,
and

expressed
that
family
primarily
populated
their
social
networks.
Conversely,
individuals

from
the
United
States
felt
more
integrated
into
the
mental
health
community,
were
rated

by
the
interviewer
to
be
more
integrated
in
the
mental
health
community,
received
more

social
resources
from
the
mental
health
community,
and
had
a
combination
of
mental

health-‐
and
non-‐mental
health-‐based
networks
in
their
lives.
Second,
the
regression
models

found
country
affiliation
was
associated
with
some
dimensions
of
community
integration

and
not
others.
Third,
various
social
network
variables
were
found
be
associated
with

different
dimensions
of
community
integration.
Fourth,
multilevel
analysis
using
network

and
individual
level
variables
was
found
to
be
a
worthy
approach
to
attain
a
complex

understanding
of
factors
associated
with
disclosure
about
mental
illness.
Mental
health

practice
and
cross-‐national
research
implications,
as
well
as
suggestions
for
future

research,
are
discussed.

1

CHAPTER
ONE:

INTRODUCTION
and
OVERVIEW

Background
of
the
Problem

Severe
mental
illness
(SMI)
is
one
of
the
leading
causes
of
disability
in
both
the

developed
and
developing
worlds
(Hopper,
2007;
National
Institutes
of
Health,
1991;

Wiley-‐Exley,
2007).
According
to
a
report
by
the
World
Health
Organization
(WHO),

published
in
2001,
11
percent
of
the
world’s
total
burden
of
disease
comes
from
mental

and
behavioral
disorders
(Campanini,
2001;
Wiley-‐Exley,
2007).
Considering
the

debilitating
nature
of
mental
illness,
both
national
organizations
(such
as
the
National

Institute
of
Mental
Health)
and
international
agencies
(such
as
WHO)
have
made
it
a

priority
to
include
a
more
nuanced
understanding
of
mental
illness
and
associating
factors

into
their
mission
statements
(Hopper,
2007;
National
Institutes
of
Health,
1991).

While
still
being
perceived
as
challenging,
there
has
been
a
shift
in
the
expectation

for
the
course
of
serious
mental
illnesses
in
the
past
decades
(J.
H.
Jenkins
&
Carpenter-‐
Song,
2005).
Mental
Health
outcomes
have
been
increasingly
linked
with
the
idea
of

recovery,
which
has
been
defined
as
the
process
where
individuals
are
able
to
live,
work,

participate,
and
contribute
in
their
communities
(Jivanjee,
Kruzich,
&
Gordon,
2008;
New

Freedom
Commission
on
Mental
Health,
2003;
Perkins,
Raines,
Tschopp,
&
Warner,
2009;

Townley,
Kloos,
&
Wright,
2009).
As
Jenkins
and
Carpenter-‐Song
stated,
“For
some

individuals,
recovery
is
the
ability
to
live
a
productive
life
despite
a
disability”
(J.
H.
Jenkins

&
Carpenter-‐Song,
2005,
p.
380).
Despite
the
growing
evidence
that
many
individuals
with

SMI
can
recover
from
mental
illness,
re-‐integrate
into
their
communities,
and
lead

productive
lives
(Bellack,
2006),
persons
with
SMI
continue
to
be
engaged
in
mental
health

2

treatment
modalities,
ranging
from
intense
community
treatments,
in-‐patient
and

outpatient
hospital
programs,
and
individual
and
group
therapy.
By
definition,
such

immersion
in
mental
health-‐focused
treatment
can
challenge
the
fundamental
notions
of

community
integration
and,
hence,
recovery.

At
the
same
time,
the
idea
of
what
comprises
a
community
and
what
is
a
“good”

outcome,
in
terms
of
integration
into
that
community,
is
as
subjective
as
the
views
about

mental
illness
(Bond,
Salyers,
Rollins,
Rapp,
&
Zipple,
2004).
Barring
the
generally
accepted

universality
of
incidence
of
mental
illness,
notions
about
mental
illness
and
its
treatment

are
often
based
in
the
normative,
social,
and
cultural
contexts
of
mental
illness
(Laungani,

1999).
The
ethnic
culture
theory
(Mirowsky
&
Ross,
1980;
Sue
&
Chu,
2003)
also
assumes

that
concepts
related
to
mental
health,
mental
illnesses,
and
mental
health
services
vary

based
on
tradition,
cultural
norms,
and
practices
and
could
impact
mental
health
diagnosis,

prognosis,
and
protective
factors
differently.
Hence,
cultural
factors,
perhaps
even

independent
of
the
initial
illness,
severity,
and
functioning
have
tremendous
impacts
on
the

course,
outcomes,
and
mental
health
services
associated
with
the
illness
(Hopper
&

Wanderling,
2000;
Lauber
&
Rossler,
2007).
India
and
the
United
States,
for
example,
have

differences
in
terms
of
resources,
target
populations,
goals
of
services,
and
barriers
to

services
(Srinivasaraghavan
et
al.,
2012).
These
differences
need
to
be
considered
before

incorporating
the
concepts
and
services
developed
in
the
United
States
into
India
in
terms

of
practice
and
research.
However,
existing
empirical
evidence
falls
short
in
accounting
for

these
subjective
differences,
across
both
individuals
and
various
ethnic
and
cultural
groups

(Townley
et
al.,
2009;
Tsang
et
al.,
2007).
Exploration
of
the
concepts
of
community
and

3

community
integration
across
individuals
and
different
societies
will
help
us
in

understanding
and
facilitating
recovery
as
a
process
and
an
outcome.

Purpose
and
Contributions
of
the
Present
Study

The
purpose
of
this
study
was
to
examine
multiple
dimensions
of
community

integration
in
United
States
and
India
using
Wong
and
Solomon’s
conceptualization
(Wong

&
Solomon,
2002).
This
study
was
built
on
and
significantly
expanded
previous
research
on

community
integration
for
individuals
with
SMI.
Our
previous
research
was
the
first
to

empirically
examine
multiple
dimensions
of
community
integration
in
the
United
States

using
Wong
and
Solomon’s
conceptualization
(Pahwa
et
al.,
2013).
The
current
study
added

a
cross-‐national
and
cross-‐cultural
context
to
the
previous
research
and
expanded
the

measurement
protocol
for
community
integration
by
refining
the
social
network

methodology
to
include
identified
sources
of
social
support,
social
resources,
and
stigma.

Theoretically,
this
study
also
expanded
the
community
integration
model
of
Wong
and

Solomon
(Wong
&
Solomon,
2002)
by
using
the
social
capital
theory
to
explain
the

importance
of
social
networks
as
a
form
of
social
community
integration.
Several
bodies
of

literature
were
central
to
this
effort,
providing
information
on
four
areas
of
interest:
(1)

theoretical
and
conceptual
approaches
to
community;
(2)
theoretical
conceptualizations
of

community
integration,
with
an
emphasis
on
Wong
and
Solomon’s
(2002)
dimensions
of

community
integration
and
the
tension
between
normalization
and
the
subculture/identity

perspective
of
community
integration
(Mandiberg,
1999);
(3)
theoretical
frameworks
of

different
dimensions
of
community
integration
within
Wong
and
Solomon’s
model
of

community
integration;
and
(4)
cross-‐cultural
and
cross-‐national
theoretical
contexts
of

mental
illness.
Specifically,
regarding
the
third
area
of
interest,
this
study
examined
the

4

theoretical
frameworks
for
social
networks
and
social
support
and
used
the
social
capital

theory
and
its
bonding
and
bridging
social
capital
perspectives
(Putnam,
2000;
Szreter
&

Woolcock,
2004)
to
expand
and
explain
the
social
dimension
of
community
integration.
The

literature
on
stigma,
because
of
its
association
with
community
integration,
was
also

examined.
These
areas
were
reviewed
to
set
the
stage
for
the
aims
and
methods
of
this

study.

The
current
study
made
some
very
important
contributions
and
additions
to

previous
work
in
the
following
ways:

1. It
included
a
cross-‐national
component
to
get
a
more
nuanced
understanding
of

community
integration
within
an
individual’s
cultural
context.

2. It
expanded
the
social
dimension
of
community
integration
using
social
resources
as

forms
of
bonding
and
bridging
social
capital
perspectives
of
the
social
capital
theory.

3. It
enhanced
the
network
methodology
to
include
relational-‐level
network
data
to

attain
more
information
about
an
individual’s
network.

4. It
added
stigma
as
a
variable
that
may
impact
the
dimensions
of
community

integration.

5. It
explored
the
use
of
multilevel
modeling
in
examining
the
effects
of
individual-‐
and

network-‐level
variables
on
disclosure
about
mental
illness.

The
present
study
aimed
to
make
a
significant
contribution
in
understanding

community
integration,
especially
because
no
other
study
has
examined
different

dimensions
of
community
integration
using
relational-‐level
network
data.
However,
at
the

same
time,
due
to
a
dearth
in
previous
research
to
draw
from,
some
of
the
aims
of
the

current
study
were
largely
exploratory.

5

The
questions
explored
within
the
context
of
each
individual’s
socio-‐cultural

environment
were
as
follows:

1. To
which
communities
do
individuals
with
SMI
feel
they
belong
(psychological

integration)?

2. From
where
do
individuals
with
SMI
access
their
resources
(physical
integration)?

3. What
are
the
network
compositions
of
individuals
with
SMI
(social
integration)?

4. How
do
different
dimensions
of
community
integration
and
relationships
vary

across
two
cultural
contexts
in
the
United
States
and
India?

5. What
and
who
are
the
important
sources
of
social
support
for
individuals
with
SMI

(social
integration)?

6. What
are
the
sources
of
social
resources,
and
stigma?

7. How
is
stigma
associated
with
community
integration
and
other
psychosocial

factors?

8. How
can
we
study
the
network-‐
and
individual-‐level
variables
simultaneously
to

understand
the
factors
associated
with
community
integration?

The
current
study,
with
the
inclusion
of
multiple
dimensions
of
community

integration
and
advanced
network
methodology,
has
a
unique
advantage
in
being
able
to

explore
the
study
questions
within
the
context
of
theories
of
community,
community

integration,
social
network,
social
capital,
social
support,
and
stigma.
This
is
the
first
study

to
use
multiple
methodologies
to
empirically
examine
community
integration
using
Wong

and
Solomon’s
conceptualization
cross-‐nationally
and
to
use
theories
of
social
capital
to

expand
the
social
community
integration
dimension.

6

Specific
Aims
and
Hypotheses

AIM
1:
To
use
network
analysis
and
Wong
and
Solomon’s
dimensions
of
community

integration
to
study
and
compare
the
degree
of
community
integration
(physical,
social,

and
psychological
integration)
of
individuals
with
SMI
in
samples
from
India
and
United

States.

Hypothesis
1a:
Samples
of
individuals
with
SMI
in
India
and
the
United
States
will

differ
in
terms
of
different
community
integration
dimensions.
Specifically,

individuals
from
India
will
be
more
integrated
in
the
non-‐mental
health
community

compared
with
individuals
from
the
United
States.

Hypothesis
1b:
Samples
of
individuals
with
SMI
in
India
and
the
United
States
will

differ
in
terms
of
their
social
network
structures
and
the
networks
from
which
they

access
social
support
and
social
resources.
Specifically,
individuals
from
India
will

have
more
non-‐mental
health
community-‐based
networks
compared
with

individuals
from
the
United
States.

AIM
2:
To
use
quantitative
and
network-‐level
variables
to
understand
community

integration
and
how
the
psychosocial
and
network
variables
are
associated
with
the
three

dimensions
of
community
integration
for
individuals
with
SMI
in
samples
from
India
and

the
United
States.

Hypothesis
2:
Samples
from
India
and
the
United
States
will
differ
in
terms
of

factors
associated
with
the
three
dimensions
of
community
integration.

AIM
3:
To
explore
the
use
of
multilevel
analysis
in
examining
the
effects
of
individual-‐
and

network-‐level
variables
on
disclosure
about
mental
illness
in
samples
from
India
and
the

United
States.

7

Overview
of
Methodology

The
present
study
uses
a
unique
combination
of
quantitative
and
network

methodologies
to
explore
the
concept
of
community
integration
in
India
and
the
United

States.
Thirty
individuals
from
the
United
States
and
26
individuals
from
India
were
asked

to
participate
in
a
network
interview,
where
they
were
asked
to
nominate
individuals
in

their
social
networks
with
whom
they
had
interacted
with
in
the
previous
two
months.

Next,
they
were
asked
specific
questions
about
their
social
networks
to
obtain
detailed

information,
including
attributes
of
their
networks
and
individuals
in
their
networks
who

they
identify
as
being
a
source
of
social
support,
social
resources,
and
stigma.
The
second

part
of
the
interview
consisted
of
a
battery
quantitative
questions
to
collect
information
on

their
demographic
characteristics,
psychosocial
characteristics,
and
different
dimensions
of

community
integration.
Data
collected
from
the
two
parts
of
the
interview
process
were

used
to
address
Aims
1–3
using
descriptive,
inferential,
and
multilevel
methodologies.

Organization
of
the
Study

The
present
dissertation
consists
of
five
chapters.
Chapter
one
presents
the

background
and
the
purpose
of
the
study
along
with
the
specific
aims.
Chapter
two

provides
the
literature
review
and
theoretical
background
of
the
study.
It
includes
a

detailed
literature
background
and
theoretical
perspectives
on
community,
community

integration,
social
support,
social
networks,
social
capital,
and
cross-‐cultural
research.

Chapter
three
includes
the
methodology
used
in
the
study,
including
specific
aims,

hypotheses,
research
design,
and
analysis
plans
for
the
three
study
aims.
Chapter
four

enlists
the
results
from
Aims
1,
2,
and
3.
The
first
part
of
Chapter
four
includes
the
results

of
the
descriptive
analysis,
followed
by
the
bivariate
analyses.
The
second
part
of
the

8

chapter
includes
regression
models
that
tested
the
association
of
different
dimensions
of

community
integration
with
different
demographic
and
psychosocial
factors.
The
third
part

of
this
chapter
includes
results
from
the
multilevel
analysis
that
tested
the
association
of

different
psychosocial,
network,
and
community
integration
variables
on
disclosure
about

mental
illness.
Chapter
five
presents
the
discussion
of
the
findings,
their
implications
for

social
work
and
cross-‐national
practice
and
research,
future
directions,
limitations
of
the

study,
and
conclusions.

9

CHAPTER
TWO:

LITERATURE
REVIEW
AND
THEORETICAL
BACKGROUND

The
Cultural
Context
of
Mental
Illness

It
has
been
suggested
that
mental
health
practices
and
ideologies
that
originate
in

developed
countries,
when
applied
to
other
countries
without
addressing
the
cultural

contexts
within
which
they
are
situated,
might
be
more
detrimental
than
beneficial

(Nizamie,
Katsu,
&
Praharaj,
2012;
Nunley,
1998).
There
are
differences
in
mental
health

perception,
prognosis,
goals
of
services,
and
the
content
of
services
in
developing
and

developed
countries
(Srinivasaraghavan
et
al.,
2012).
India,
known
for
its
culturally
rich

society,
is
also
known
for
its
pluralistic
approach
to
medicine
and,
specifically,
to
mental

illness
(Pakaslahti,
2012).
Whereas
community
mental
health
services
are
growing
rapidly

in
India,
there
is
still
a
substantial
number
of
people
who
believe
in
the
supernatural
basis

of
mental
illnesses
and
seek
indigenous
and
faith
healers
as
their
initial
treatment
modality

(Banerjee
&
Roy,
1998).
However,
although
there
is
a
tremendous
stigma
associated
with

mental
illness
(Longanathan
&
Murthy,
2012;
Longanathan
&
Murthy,
2008),
there
is
also

family
involvement
and
support
available
to
individuals
with
mental
illness
in
India

(Bhatia,
Garg,
&
Galhotra,
2012b;
Thara,
Padmavati,
Arynkran,
&
John,
2008).

Concurrently,
there
is
evidence
of
better
long-‐term
treatment
outcomes
for

individuals
with
SMI,
such
as
schizophrenia,
in
developing
counties
compared
with

developed
countries
(Hopper,
2007;
Hopper
&
Wanderling,
2000;
Jadhav
et
al.,
2007;

Kulhara,
Mattoo,
Avasthi,
&
Malhotra,
1987).
A
series
of
WHO
studies
(World
Health

Organization,
1973,
1978)
and
subsequent
follow-‐up
studies
(Hopper
&
Wanderling,
2000)

found
significant
differences
between
developing
(India,
Colombia,
and
Nigeria)
and

10

developed
(USA,
UK,
Ireland,
Denmark,
Czech
Republic,
Slovakia,
Japan,
and
Russia)

countries
in
terms
of
mental
health
treatment
outcomes,
with
developing
countries

showing
more
promising
results
(Hopper,
2007).
This
differential
course
of
illness
and

prognosis
has
implications
for
cross-‐cultural
and
cross-‐national
generalizations,
both
in

terms
of
research
and
practice.
Cultural
backgrounds
are
known
to
influence
the

conception
of
mental
illness,
experience
of
symptoms,
types
and
acceptability
of

treatments,
and
social
tolerance
of
mental
illnesses.

Other
cross-‐cultural
studies
have
identified
differences
in
the
way
mental
illnesses

are
perceived.
Kleinman
(1991)
discussed
mental
illness
in
the
context
of
“theory
of

causation”
that
dictates
how
a
mental
illness
is
conceptualized,
provided
a
meaning
and

expectations
regarding
the
course
and
outcomes
of
a
mental
illness
(Kleinman,
1991;

Swerdlow,
1992).
He
discusses
cultures
where
mental
illness
is
considered
more
acute

rather
than
chronic,
recovery
is
an
expectation,
and
mental
illness
is
not
automatically

linked
to
disability
(Kleinman,
1991;
Swerdlow,
1992),
which
is
in
contrast
to
a
society

where
mental
illness
is
attached
to
the
identity
of
an
individual
(Estroff,
1989).
More

traditional
or
socio-‐centric
societies,
like
India,
that
emphasize
social
relationships,

traditions,
and
roles
that
sustain
long-‐term
relationships
are
considered
to
have
more

extended
and
long-‐term
social
connections
that
may
provide
a
buffer
against
the
negative

effects
of
having
a
mental
illness
(K.
Lin
&
Kleinman,
1988).

In
the
light
of
these
cultural
factors,
an
abundance
of
caution
should
be
exercised

when
applying
more
westernized
conceptions
of
mental
illness
on
individuals
diagnosed

with
SMI
in
countries
such
as
India.
Consequently,
questions
about
the
community
life
of

persons
with
SMI
need
to
be
explored
within
these
unique
cultural
contexts.

11

Mental
Health
in
India,
Social
Support,
and
Stigma

Social
support
and
family
support
are
two
important
factors
that
play
predominant

roles
in
attaining
better
outcomes
for
individuals
with
SMI,
especially
in
developing

countries
(Hopper
&
Wanderling,
2000).
India,
one
of
the
most
rapidly
developing

societies,
is
rooted
in
its
rich
cultural
and
social
beliefs,
and
these
beliefs
also
extend
to
the

origination
and
course
of
SMI
(Wagner,
Duveen,
Themel,
&
Verma,
1999).
It
would
be

hazardous
to
perceive
India
as
a
culturally
and
socially
homogenous
society,
considering

the
diversity
in
terms
of
region,
language,
and
religious
practices
(Bhatia
et
al.,
2012b).

Even
so,
the
engagement
of
Indian
families
in
the
course
of
treatment
for
a
family
member

diagnosed
with
SMI,
from
the
initial
decision
to
seek
help
to
medication
adherence,

provision
of
financial
assistance,
and
logistical
support,
are
suggested
to
influence
better

course
and
illness
outcomes
(Nunley,
1998).
Family
involvement
in
the
life
and
care
of

individuals
with
SMI
have
been
linked
with
the
following
benefits:
(1)
family
involvement

means
a
voice
and
patient
advocacy
at
every
step
of
the
way;
(2)
family
and
caregivers
have

proved
to
be
a
very
good
source
of
information
for
service
providers;
(3)
inclusion
of

family
could
mean
better
adherence
to
treatment
and
medication
for
individuals
with
a

SMI;
and
(4)
in
lieu
of
no
financial
safety
net
for
individuals
with
SMI
in
India,
family

involvement
could
mean
better
economic
provision
of
care
(Bhatia,
Garg,
&
Galhotra,

2012a;
Nunley,
1998)

There
has
been
considerable
research
over
the
years
in
India
linking
family

structure,
size,
socio-‐economic
status,
education,
and
family
religious
practices
to
better

prognosis
of
various
mental
illnesses
(Bhatia
et
al.,
2012b;
Sethi,
1983);
yet,
family

entrenchment
in
the
lives
of
individuals
with
SMI
is
not
always
positive.
Studies
have

12

identified
negative
impacts
of
family
members
serving
as
the
primary
caregivers
of

individuals
with
SMI,
for
the
caretakers
and
the
care
receivers.
Personal
and
financial

burdens
associated
with
care
giving
(Rammohan,
Rao,
&
Subbakrishna,
2002),
depreciating

quality
of
life
of
the
caregiver
(Caqueo-‐Urízar,
Gutiérrez-‐Maldonado,
&
Miranda-‐Castillo,

2009),
and
expressed
emotion
(Butzlaff
&
Hooley,
1998)
are
just
as
prevalent
as
the

positive
effects
of
family
involvement
in
the
lives
of
persons
with
SMI.

Stigma
has
been
recognized
as
an
integral
part
of
the
experience
of
mental
illness,
a

key
barrier
to
accessing
mental
health
care,
and
an
important
deterrent
in
re-‐integration

into
mainstream
community.
Although
stigma
associated
with
mental
illness
is
not
a

phenomenon
of
developing
countries
alone,
studies
in
India
have
found
the
presence
of

stigma
at
multiple
levels
(Longanathan
&
Murthy,
2012),
including
perceived
stigma

internalized
by
the
individual
with
mental
illness
(Raguram,
Weiss,
Channabasavanna,
&

Devins,
1996),
at
the
level
of
family
and
caregivers
(Thara,
Kamath,
&
Kumar,
2003a,

2003b),
at
the
level
of
service
providers
(Chowdhury,
Sanyal,
Dutta,
Banerjee,
De,

Bhattacharya,
Patit,
et
al.,
2000;
Imran
&
Haider,
2007)
,
and
at
the
level
of
society

(Chowdhury,
Sanyal,
Dutta,
Banerjee,
De,
Bhattacharya,
Patit,
et
al.,
2000).
Whereas

experiencing
stigma
at
these
levels
could
be
negatively
associated
with
the
experiences
of

having
a
mental
illness
and
a
barrier
to
accessing
services,
in
India,
the
interaction
of

stigma
with
other
family-‐level
factors
makes
for
a
particularly
convoluted
picture.
As

previously
discussed,
family
plays
an
important
role
in
connecting
an
individual
with
a

mental
illness
to
mental
health
services
in
India.
Family
provides
financial
support,

advocates
for
the
consumer,
and
takes
on
a
caregiver
role
at
home.
However,
at
the
same

time,
because
of
the
structure
of
Indian
society,
the
stigma
of
having
a
mental
illness
is
not

13

only
directed
at
or
felt
by
the
individual.
It
may
also
become
a
source
of
shame
and

embarrassment
for
the
family
member
(Longanathan
&
Murthy,
2012).
The
picture
is

further
complicated
by
the
idea
of
the
marriageability
of
an
individual
if
they
are
known
to

have
a
mental
illness.
Indian
society
is
still
a
culture
that
practices
arranged
marriages,
and

any
association
of
mental
illness
within
a
family
could
imply
diminishing
marriage

prospects,
not
just
for
the
individual
with
a
mental
illness,
but
for
their
siblings
and
other

members
of
the
family,
as
well
(Thara
et
al.,
2003a,
2003b).

In
conclusion,
Indian
society
presents
an
interesting
contradiction,
where
family

support
and
social
support
are
potential
buffers
to
many
negative
aspects
involved
for

individuals
with
a
mental
illness.
If
harnessed
properly,
family
could
be
a
means
to
improve

access
and
efficacy
of
care.
However,
lack
of
awareness,
beliefs
about
mental
illnesses,
and

the
stigma
it
carries
could
also
turn
family
involvement
into
a
barrier
to
accessing
care
and

could
hinder
the
process
of
recovery.

Community
and
Theoretical
Approaches
to
Understanding
Community

Community
has
been
defined
in
terms
of
a
geographical
space,
as
well
as
a
group
of

people
who
interact
with
each
other
and
have
common
interests,
shared
goals,
and
a

collective
sense
of
identity
(Cooper,
Arber,
Fee,
&
Ginn,
1999;
Post,
1997).
On
one
level,

community
is
composed
of
the
place,
neighborhood,
or
other
various
locations
of
society

(Brennan
&
Brown,
2008;
Tyler,
2006).
On
another
level,
community
stands
for
a
sense
of

belonging,
interdependence,
identity,
and
shared
experiences
that
individuals
derive
from

the
people
with
which
they
interact
(Bhattacharyya,
2004;
Blackshaw,
2009;
A.
P.
Cohen,

1985).

14

The
term
community
originates
from
the
Latin
word
communis,
which
means

“fellowship,”
and
contains
the
attribute
of
“community
of
relations
and
feelings”
(Barrett-‐
Lennard,
1994;
Callus,
2006).
There
are
multiple
perspectives
in
the
literature
that
explore

what
community
is,
how
it
has
been
conceptualized,
and
how
it
can
be
measured.
George

Hillery,
in
1955,
gave
94
definitions
of
community,
which
illustrates
the
complexity
of
this

concept
(Hillery,
1955).
The
earliest
definitions
focused
on
community
as
an
“organized

body
of
people”
(Tyler,
2006,
p.
21).
Gradually,
with
the
development
of
trade
and,
hence,
a

society
where
its
members
were
more
dependent
on
each
other,
community
came
to
be

known
in
terms
of
relationships
between
people
(Tyler,
2006).

It
was
within
the
context
of
modern
time,
with
the
development
of
states
and

nations,
that
community
was
interpreted
in
terms
of
individuals
sharing
a
geographical

space
(Brennan
&
Brown,
2008;
Tyler,
2006).
Tönnies
(2001)
further
formalized
this

definition
of
community
by
making
a
distinction
between
Gemeinschaft
(defined
in
terms

of
significant
local
community)
and
Gesellschaft
(defined
in
terms
of
the
larger
society).

Gemeinschaft
was
conceptualized
as
a
traditional
interconnected
community,
where
family

history
and
traditions
connected
its
members,
not
unlike
a
small
village
(Blackshaw,
2009;

Tönnies,
2001).
Gesellschaft,
on
the
other
hand,
was
characterized
by
independence
and

impersonal
existence,
mainly
as
a
result
of
industrialization,
which
was
more
like
an
urban

city
(Blackshaw,
2009;
Tönnies,
2001).
This
definition
of
community
limited
the
idea
of

community
to
a
geographical
space.

The
interpretation
of
community
only
in
the
context
of
a
geographical
space
was

challenged
as
early
as
1964
and
has
been
many
times
since
by
various
theorists
who

recognized
that
defining
community
only
in
terms
of
geography
and
spatial
proximity

15

might
be
limiting
(Bhattacharyya,
2004;
Blackshaw,
2009;
Webber,
1964).
During
this

period
is
when
the
definition
of
community
via
connections
and
social
networks
came
into

being
(Bradshaw,
2008;
Calhoun,
2007).
Bradshaw
(Bradshaw,
2008,
p.
5)
defined

community
as
the
“networks
of
people
tied
together
by
solidarity,
a
shared
identity
and
set

of
norms,
that
do
not
necessarily
reside
in
a
same
place.”
In
recognition
of
the
importance

of
individual
networks,
network
ties,
and
network
features,
such
as
reciprocity,
many

community
theorists
have
pointed
to
network
analysis
as
a
key
method
for
understanding

communities
(Blackshaw,
2009).

Community
has
also
been
interpreted
in
the
context
of
an
individual’s
identity
and

sense
of
belonging.
Bhattacharyya
(2004)
identified
community
in
terms
of
solidarity
and

common
values,
social
norms,
and
attributes
that
could
lead
to
a
shared
sense
of
identity.

Cohen,
in
his
theory
of
community
(A.
P.
Cohen,
1985),
talked
about
the
symbols,
customs,

habits
and
rituals
are
used
to
communicate
within
the
context
of
a
community.
This
idea
of

community
is
based
on
a
shared
sense
of
reality
but
is
symbolically
constructed
and
a

source
of
an
individual’s
sense
of
identity
and
belonging
(Blackshaw,
2009).

The
premise
of
identity-‐based
community
also
comes
from
the
concept
of
likeness.

As
Heller
(1999)
pointed
out,
we
are
more
like
ourselves
than
anyone
else,
and
we
are

more
like
the
social
group
we
belong
to
(Bhattacharyya,
2004;
Cooper
et
al.,
1999;
Heller,

1999;
Post,
1997).
Embedded
within
this
idea
of
likeness
is
the
idea
of
distinctiveness,

whereby
an
individual’s
identity,
within
the
context
of
a
community,
is
based
on
how

different
they
are
from
the
members
of
other
communities
(Blackshaw,
2009;
R.
Jenkins,

2008).
This
idea
is
especially
relevant
to
individuals
with
mental
illness
who
may

experience
a
shift
away
from
their
primary
identity
as
a
mental
health
patient
when
they

16

become
a
well-‐integrated
member
of
the
general
community
(Bond
et
al.,
2004;
McColl,

Davies,
Carlson,
Johnston,
&
Minnes,
2001;
Pilisuk,
2001).
However,
when
individuals

within
the
general
community
have
a
sense
of
identity
that
is
perceived
to
be
different
from

an
individual
with
a
mental
illness,
stigma
may
arise,
making
it
difficult
for
individuals
with

SMI
to
develop
a
sense
of
belonging
in
this
mainstream
community
(Mandiberg,
1999).

The
current
study
takes
into
account
these
definitions
of
community,
including

community
as
geographical
space
(where
an
individual
spends
time),
community
in
terms

of
an
individual’s
social
network
connections,
and
community
based
on
an
individual’s

sense
of
identity
and
belonging.
This
definition
of
community
has
implications
for
the

notion
of
community
integration.

Community
Integration
and
Its
Theoretical
Frameworks

Community
integration
has
been
recognized
as
a
manifestation
of
the
recovery

experience
(Bond
et
al.,
2004),
as
an
important
outcome
of
mental
health
treatment,
and
as

a
challenge
for
individuals
with
SMI
(Abdallah,
Cohen,
Sanchez-‐Almira,
Reyes,
&
Ramirez,

2009;
Baumgartner
&
Herman,
2012;
Perkins
et
al.,
2009;
Townley
et
al.,
2009).
Because

the
passage
of
the
Community
Mental
Health
Centers
Act
of
1963,
it
has
been
suggested

that
individuals
with
SMI
are
better
served
when
they
integrate
into
the
general
or
non-‐
mental
health
community
(Wong,
Sands,
&
Solomon,
2010).
The
deinstitutionalization
of

persons
with
SMI
was
based
on
the
philosophy
that
all
individuals,
regardless
of
their

illness
burden,
should
be
integrated
into
society
and
fit
into
culturally
normative
activities

and
roles
(Wong
et
al.,
2010);
however,
little
work
has
been
conducted
to
identify
the

process
of
community
integration
and
associated
factors
for
persons
with
SMI
(Prince
&

17

Gerber,
2005;
Wieland,
Rosenstock,
Kelsey,
Ganguli,
&
Wisniewski,
2007;
Wong
&
Solomon,

2002).

Based
on
the
initial
definition
of
community
as
a
geographical
space,
community

integration
of
individuals
with
SMI
was
originally
only
associated
with
physical
integration,

which
implied
that
for
individuals
with
SMI
to
become
integrated
into
the
mainstream

community,
they
had
to
spend
more
of
their
time
in
the
non-‐mental
health
community
and

use
it
for
their
day-‐to-‐day
needs
(Segal
&
Aviram,
1978).
This
definition
has
since
been

expanded
to
include
multiple
dimensions
of
community
integration
(Wong
&
Solomon,

2002),
which
is
reflective
of
the
multidimensional
definition
of
community.
The
present

study
is
informed
by
the
holistic
conceptual
framework
put
forward
by
Wong
and
Solomon

(2002)
that
focuses
on
three
dimensions
of
community
integration:
physical,
social,
and

psychological
integration.
This
idea
of
community
integration
moves
beyond
the
traditional

definition
of
community
as
a
place
of
residence
to
include
subjective
emotional

connections,
bonds,
and
a
sense
of
identity
and
belonging
(Cummins
&
Lau,
2003;
Wong
&

Solomon,
2002).

According
to
Wong
and
Solomon’s
dimensions,
community
integration
can
be

defined
in
the
following
ways:

1. Physical
integration:
the
extent
to
which
an
individual
spends
time
and
participates

in
a
community
and
“uses
goods
and
services
in
the
community
outside
his/her

home”
(Wong
&
Solomon,
2002,
p.
18).

2. Psychological
integration:
the
extent
to
which
an
individual
feels
they
are
a
part
of
a

community
(Wong
&
Solomon,
2002).

3. Social
integration:
Social
integration
has
been
divided
in
to
two
sub
dimensions:

18

a. Social
interaction:
the
“extent
to
which
an
individual
engages
in
social

interaction
with
other
community
members”
(Wong
&
Solomon,
2002,
p.
18).

b. Social
networks:
the
adequacy
of
an
individual’s
social
network
in
terms
of

its
size
and
the
level
of
support
it
provides.

The
present
study
has
operationalized
this
multidimensional
perspective
to
bring

together
theories
of
social
capital,
social
support,
and
social
networks
and
weave
them
in
to

the
community
integration
perspective.
Additionally,
we
are
suggesting
an
expansion
of
the

social
integration
dimension
of
Wong
and
Solomon’s
model
in
the
form
of
social
resources

using
the
theory
of
bridging
and
bonding
social
capital
(Putnam,
2000).
This
theory

examines
the
members
of
a
social
network
from
whom
individuals
access
different
kinds
of

social
resources.

Social
Networks,
Social
Resources,
and
Social
Support:
How
They
Inform
Community

Integration

Whereas
there
are
several
overlapping
definitions
of
social
networks,
social

support,
and
social
resources
in
the
literature,
all
three
concepts
are
considered
very

important
to
community
development
and
reintegration
(Barrera,
1986;
Dolan,
2008).
The

current
study
used
Wong
and
Solomon’s
(2002)
framework
of
community
integration

(physical,
social,
psychological
integration),
conceptualized
social
support
levels,
and
social

network
variables
as
the
measure
of
social
integration.
We
used
the
theory
of
social

resources
to
measure
two
dimensions
of
Wong
and
Solomon’s
model
of
community

integration.
First,
we
used
the
availability
of
total
social
resources
to
an
individual
as
a

proxy
of
physical
integration
for
individuals
with
SMI.
Second,
we
made
use
of
the
theory
of

19

bridging
and
bonding
social
resources
to
study
the
actual
network
relationships
that

provide
specific
social
resources
to
individuals
with
SMI.

Figure
1.
Wong
and
Solomon’s
model
of
community
integration
and
its
dimensions.

The
three
concepts,
their
theoretical
foundations,
and
their
relationship
to

community
integration
are
discussed
below.

Social
networks.
Central
to
the
definition
of
a
social
network
is
its
ability
to

“provide
the
structural
element
of
the
mechanism
of
social
interaction”
through
which

“individuals
learn
about,
come
to
understand,
and
attempt
to
handle
difficulties”

(Pescosolido,
1992,
2006).
Social
networks
and
connections
form
the
basis
of
how

community
is
sometimes
defined
(Bradshaw,
2008;
Calhoun,
2007).
Social
networks,
in
the

form
of
family
and
community,
transfer
the
cultural
norms
and
practices
to
an
individual

that
shapes
their
interpersonal
behavior
(Cullen
&
Whiteford,
2001;
National
Institutes
of

Health,
1991).

Community

Integration

Physical

Integration

Psychological

Integration

Social

Integration

Social

interaction

Social

Networks

20

There
is
a
substantial
body
of
literature
that
has
established
the
importance
of
social

ties
and
social
networks
for
mental
illness
and
psychological
well-‐being
(Cullen
&

Whiteford,
2001;
Kawachi
&
Berkman,
2001;
Webber,
2004;
Webber
&
Huxley,
2007).

There
is
also
an
association
between
presence
of
psychiatric
disorders
and
significant

impairment
in
the
social
networks
for
individuals
with
SMI
(Brugha,
Wing,
Brewin,

MacCarthy,
&
Lesage,
1993;
Holmes-‐Eber
&
Riger,
1990).
Compared
with
the
general

population,
individuals
with
schizophrenia
are
known
to
have
smaller
networks,
which
in

turn
are
associated
with
lower
social
support
and
sparse
environmental
resources,

especially
in
the
times
of
stress
and
crises
(Brugha
et
al.,
2004;
Stein,
Barry,
Van
Dien,

Hollingsworth,
&
Sweeney,
1999).

Historically,
being
able
to
form
and
maintain
social
relationships
and
reciprocal

social
ties
has
been
vital
to
better
health
outcomes
and
the
process
of
recovery
for

individuals
with
SMI
(Cooper
et
al.,
1999;
Ware,
Hopper,
Tugenberg,
Dickey,
&
Fisher,

2007).
Strong
social
networks
have
been
shown
to
positively
influence
the
course
and

experience
of
a
mental
illness
and
the
outcome
of
mental
health
services
(Sartorius,
2003).

Paradoxically,
many
individuals
with
SMI
experience
an
absence
of
meaningful

relationships,
have
inadequate
and
limited
social
networks,
and
remain
isolated
while

physically
being
in
a
community
(Brugha
et
al.,
1993;
Holmes-‐Eber
&
Riger,
1990;
Prince
&

Gerber,
2005;
Stein
et
al.,
1999).
Individuals
with
SMI
can
be
caught
with
a
strange

predicament.
In
one
respect,
social
support
and
social
networks
creates
a
buffer
that

shields
them
from
high
stress
situations;
however,
individuals
with
SMI
end
up
isolated

and
in
a
more
socially
underprivileged
position
because
of
their
disability
(Cullen
&

Whiteford,
2001;
Jones,
2005).

21

There
is
significant
research
on
the
size
and
density
of
the
social
network
of

individuals
with
SMI
and
the
detrimental
effects
of
smaller
and
less-‐dense
networks.

Whereas
that
is
very
important,
there
is
also
a
need
to
look
at
the
composition
of
an

individual’s
network
to
identify
the
actual
source
of
support
and
resources
(a
friend
or
a

family
member
versus
a
mental
health
service
provider)
for
more
targeted
interventions

(Holmes-‐Eber
&
Riger,
1990).
An
individual’s
network
consists
of
different
kinds
of

relationships
and
different
types
of
ties.
Different
ties
could
be
potential
sources
of

different
levels
of
support
and
resources
(Wellman
&
Wortley,
1990).
Once
we
are
able
to

delineate
the
actual
source
of
support
and
resources
associated
with
better
outcomes
and

social
integration,
researchers
and
practitioners
could
then
work
toward
mobilizing
similar

types
of
support
in
other
individuals
with
similar
socio-‐cultural
environments.

To
summarize,
social
networks
can
guide
the
understanding
of
community

integration
in
two
ways.
First,
it
can
be
a
good
measure
of
the
levels
of
social
community

integration
of
individuals
with
SMI
to
explore
if
variables
such
as
stigma,
psychosocial

functioning,
and
symptomatology
are
associated
with
social
functioning.
Second,
relational-‐
level
network
data
could
be
used
as
a
means
to
delineate
sources
of
social
resources,
social

support,
and
stigma
in
an
individual’s
network
to
better
understand
the
process
of

community
integration.
The
present
study
used
the
network
data
in
both
ways.

Social
capital.
The
concept
of
social
capital
is
historically
rooted
in
the
concept
of

capital,
introduced
by
Marx,
in
which
capital
described
the
“circulation
of
commodities

between”
the
producers
and
consumers
(N.
Lin,
1999a).
This
concept
led
to
the
origination

of
a
theory
based
on
social
relations
between
two
classes,
which
Lin
called
the
classical

theory
of
capital
(N.
Lin,
1999a).
Subsequent
theories
of
capital
were
classified
into
a

22

broader
category
of
neocapital
theories
that
included
the
human
capital
theory,
cultural

capital
theory,
and
social
capital
theory.
According
to
the
human
capital
theory,
individuals

and
societies
experience
economic
gains
by
investing
in
other
individuals
(N.
Lin,
1999a;

Schultz,
1961;
Sweetland,
1996).
Human
capital
was
mainly
interpreted
in
terms
of

education,
skills,
and
tools
that
could
subsequently
be
used
for
economic
gains
(N.
Lin,

2002).
Cultural
capital
theory
is
based
on
the
premise
of
symbols
and
meanings,
primarily

defined
by
the
dominant
class
and
imposed
on
the
dominated
class,
in
the
form
of

education,
skills,
and
practices
to
propagate
the
dominant
culture
(Bourdieu,
1992,
2008;

N.
Lin,
1999a).

The
third
neoclassical
theory
of
capital,
and
the
one
most
discussed
in
recent

decades,
is
the
social
capital
theory.
Different
theorists
like
Bourdieu,
Lin,
Cokeman,

Erikson,
and
Putnam
have
made
significant
contributions
in
the
conceptualization
of
social

capital.
The
basic
premise
behind
social
capital
is
that
it
exists
in
the
“embedded
resources

in
an
individual’s
social
networks”
(N.
Lin,
1999a).
Social
capital,
through
interaction
with

ones
environment
and
community,
enables
the
development
of
an
individual’s
natural

defenses,
which
help
them
cope
with
life’s
stressors
that
could
lead
to
poor
health
and

functioning
(Cullen
&
Whiteford,
2001).

There
is
a
lack
of
consistency
in
the
literature
on
how
social
capital
is

conceptualized
and,
hence,
measured
(Vyncke
et
al.,
2013).
There
are
two
streams
of

thought
on
whether
social
capital
is
an
individual-‐level
or
a
group-‐level
phenomenon

(Vyncke
et
al.,
2013).
Various
scholars
identify
it
only
as
a
function
of
a
group
or
a

community
(Coleman,
1988;
Cooper
et
al.,
1999;
Grant,
2001).
Within
the
context
of
social

capital
as
a
group
phenomenon,
it
is
viewed
as
a
collective
asset
available
to
an
individual

23

as
part
of
their
membership
in
a
group
or
community
(Bourdieu,
2008;
Coleman,
1988;

Putnam,
1993,
2000).
Bourdieu
has
defined
social
capital
along
the
same
lines
as
he

defined
the
cultural
capital
theory.
He
stated,
“social
capital
as
the
investment
of
the

members
in
the
dominant
class
(as
a
group
or
network)
engaging
in
mutual
recognition

and
acknowledgment
so
as
to
maintain
and
reproduce
group
solidarity
and
preserve
the

group's
dominant
position”
(Bourdieu,
1992;
N.
Lin,
1999b).
Putnam
has
defined
social

capital
as
“the
features
of
social
life
such
as
networks,
norms,
and
social
trust
that
facilitate

coordination
and
co-‐operation
for
mutual
benefit”
(Putnam,
1995,
p.
67).
Coleman
has

conceptualized
social
capital
as
a
derivative
from
the
interaction
between
an
individual’s
or

a
community’s
intangible
social
and
human
resources,
as
well
as
the
tangible
physical
and

economic
resources
(Coleman,
1988).
Various
other
scholars
identify
social
capital
as
an

individual-‐level
phenomenon
but
only
in
the
contexts
of
their
relationships
in
their

networks
(N.
Lin,
1999a,
1999b,
2002;
N.
Lin
&
Dumin,
1986;
Szreter
&
Woolcock,
2004;

Van
Der
Gaag
&
Snijders,
2005).
Within
the
individual-‐level
social
capital
perspective,
the

focus
is
on
(1)
the
investment
an
individual
makes
in
their
social
relationships
and
(2)
the

extent
to
which
they
can
access
resources
embedded
in
their
relationships.

Within
the
context
of
a
community,
these
resources
could
be
embedded
in
a
specific

community
where
individuals
go
to
for
information,
to
facilitate
change,
to
access
social

relationships,
and
to
seek
their
identity.
Within
the
context
of
community
integration,
these

resources
could
influence
which
community
an
individual
feels
integrated
into.
Within
the

context
of
social
capital
through
an
individual’s
networks,
Putnam
distinguishes
between

“bonding”
and
“bridging”
types
of
social
capital
(Putnam,
2000;
Szreter
&
Woolcock,
2004).

Bonding
social
capital
refers
to
a
more
linear
or
horizontal
transfer
of
different
kinds
of

24

resources
within
an
individual’s
social
networks
(De
Silva,
McKenzie,
Harpham,
&
Huttly,

2005;
Ellison,
Steinfield,
&
Lampe,
2007).
Individuals
sharing
bonding
social
capital
are

generally
in
similar
social
positions
and
share
a
sense
of
identity.
Bridging
social
capital
is

more
vertical
or
hierarchical.
It
is
derived
from
a
sense
of
respect.
Bridging
social
capital
is

based
on
weak
ties
(Ellison
et
al.,
2007;
Szreter
&
Woolcock,
2004).
Mitchell
and
LaGory

(2002)
call
bonding
social
capital
“exclusive,
cohesive,
strong
ties
[that]
facilitate
solidarity

and
reciprocity”
and
bridging
social
capital
“inclusive,
diverse,
weak
ties
[that]
provide

linkages
to
external
assets
and
information
diffusion”
(Mitchell
&
LaGory,
2002).

Whereas
network
variables
such
as
size,
density,
and
reciprocity
might
give
a

general
idea
about
the
resources
potentially
available
to
an
individual,
for
the
purposes
of

assessing
social
capital,
measuring
resources
embedded
in
ones
social
network
is

considered
to
provide
a
good
estimate
of
an
individual’s
social
capital
(Van
Der
Gaag
&

Webber,
2008).
The
current
study
suggests
incorporation
of
both
concepts
to
the
existing

model
by
Wong
and
Solomon
(Wong
&
Solomon,
2002)
and
explores
the
concept
of
social

capital
in
the
form
of
social
resources
within
the
context
of
an
individual’s
social

connections
and
networks.
For
example,
bridging
social
capital
could
refer
to
the
mental

health-‐based
peers,
providers,
and
mental
health
friends,
acquaintances,
neighbors,
and

colleagues,
and
bonding
social
capital
could
occur
in
the
context
of
non-‐mental
health

friends
and
family
(Irwin,
LaGory,
Ritchey,
&
Fitzpatrick,
2008)
in
the
current
study.
This

method
not
only
has
the
potential
to
render
the
concept
of
social
capital
measurable
in

multiple
ways,
but
also
provides
a
unique
opportunity
to
measure
individual-‐level
social

support
in
the
context
of
affiliation
into
two
(mental
health
and
non-‐mental
health)

communities.

25

Social
support.
Social
support
is
in
an
integral
part
of
a
community
in
that
it
allows

its
members
to
have
access
to
formal
and
informal
networks
(Dolan,
2008).
Social
support

is
associated
with
community
as
a
place
or
geographical
phenomenon
(Brennan
&
Brown,

2008;
Dolan,
2008)
and
as
a
source
of
belonging
or
identity
(Bhattacharyya,
2004;

Blackshaw,
2009;
Dolan,
2008).
Social
support
can
be
seen
as
a
form
of
bonding
social

capital
(Szreter
&
Woolcock,
2004),
but
it
is
treated
separately
for
the
purposes
of
this

study.

Social
support
is
broadly
defined
as
the
emotional
support,
guidance,
or
tangible
aid

people
receive
from
social
relationships
(Ell,
1984;
Heaney
&
Israel,
2002).
In
the

literature,
social
support
has
been
divided
into
four
categories
of
support:
emotional,

instrumental,
informational,
and
appraisal
(Heaney
&
Israel,
2002;
House,
1981).

Irrespective
of
the
type
of
social
support,
an
individual
goes
back
to
his
or
her
community

of
choice
or
identification
to
access
this
support
(Dolan,
2008),
whether
it
is
from
family,

friends,
peers,
or
more
formal
relationships
such
as
mental
health
service
providers.

Additionally,
social
support
has
been
measured
in
the
context
of
the
availability
and

adequacy
of
support
as
perceived
by
the
participant
(perceived
social
support),
by
the

amount
of
contact
with
and
number
of
significant
individuals,
and
directly
through

indicators
of
support
provided
(enacted
social
support)
(Barrera,
1986;
Tardy,
1985).
The

present
study
simultaneously
explores
social
support
in
terms
of
the
availability
of
the

perceived
support
and
adequacy
of
social
support
in
the
form
of
network
connections.

Numerous
theoretical
models
in
the
literature
conceptualize
different
aspects
of

social
support.
One
such
model
on
“Social
Embeddedness”
(Barrera,
1986)
refers
to
social

connections
and
being
embedded
in
these
connections.
The
concept
of
social

26

embeddedness
can
be
approached
in
two
ways.
According
to
the
first,
an
individual’s
social

connections
could
be
a
source
of
support
when
required.
The
second
approach
identifies

the
importance
of
structural
forms
of
social
support,
namely,
social
network
and
social

network
characteristics
such
as
size,
density,
and
reachability,
as
a
form
of
social
support

(Barrera,
1986;
Dolan,
2008).

Whereas
ongoing
social
support
is
considered
important
in
an
individual’s
life,

social
support
is
most
appreciated
in
moments
of
crisis,
also
known
as
stressful
events.

There
are
a
number
of
studies
that
conceptualize
social
support
in
the
context
of
stress
in

an
individual’s
life.
One
such
model
is
the
stress
prevention
model.
In
this
model,
social

support
is
either
considered
to
prevent
stressful
events
from
occurring
altogether
or
to

minimize
the
threat
of
a
stressful
event
that
has
occurred
(Barrera,
1986).
Another
model

that
conceptualizes
social
support
in
terms
of
stress
is
the
support
deterioration
model,

whereby
the
presence
of
a
stress
might
lead
to
a
lessening
of
perceived
availability
or

effectiveness
of
social
support
(N.
Lin
&
Ensel,
1984).
The
support
deterioration
model
also

seems
to
be
accordant
with
the
stigma
theory,
whereby
an
individual
with
a
stressor
in
his

life,
for
example,
a
mental
illness,
could
potentially
lead
to
the
stigmatizing
of
that

individual,
in
turn,
leading
to
a
deterioration
of
social
support.

Two
other
models
that
link
stress
and
social
support
are
the
main
effect
model
and

the
stress
buffering
model
of
health
outcomes
(Cohen
and
Wills,
1985),
which
explains
the

role
of
social
support
in
the
lives
of
individuals
with
a
mental
illness
(S.
Cohen
&
Wills,

1985;
DeGarmo,
Patras,
&
Eap,
2008;
Kawachi
&
Berkman,
2001).
According
to
the
two

models,
social
relationships
and
social
interactions
impact
the
health
behavior
of
an

individual
in
two
ways.
The
main
effects
model
stipulates
that
the
benefits
of
social
support

27

are
linked
to
the
presence
of
large
and
stable
social
networks
(S.
Cohen
&
Wills,
1985).

Social
networks
provide
regularized
social
interactions
and
could
potentially
influence
an

individual’s
self-‐worth,
in
turn,
leading
to
positive
health
outcomes.
The
main
effect
model

also
posits
that
the
more
diverse
an
individual’s
social
networks
are,
the
greater
their

access
is
to
functional
social
relationships
that
can
provide
necessary
support
for
them
to

deal
with
a
stressor.
However,
the
stress-‐buffering
model
stipulates
that
social
support

could
intervene
between
the
experience
of
stress
and
a
pathological
reaction
by
“reducing

or
eliminating
the
stress
reaction”
(S.
Cohen
&
Wills,
1985;
DeGarmo
et
al.,
2008).

Having
explained
the
role
of
social
support
in
regular
life
and
stressful
situations,

the
literature
links
social
support
and
community,
both
in
terms
of
geography
and,
more

importantly,
the
capacity
of
the
community
members
to
provide
social
support
via
a

common
sense
of
identity
and
bonds.
For
individuals
with
SMI,
this
could
be
a
recovery-‐
based
mainstream
community
or
a
sub-‐identity-‐based
mental
health
community.
Hence,

community,
if
harnessed
suitably,
has
a
potential
to
be
a
source
of
sense
of
identity
and

support
in
stressful
situations.

Whereas
it
is
very
important
to
delineate
and
operationalize
the
concept
of

community
integration
into
measurable
forms,
understanding
of
this
concept
would
be

incomplete
without
understanding
its
relationship
with
associated
factors.
In
addition
to

providing
a
framework
for
understanding
community
integration,
Wong
and
Solomon’s

(2002)
conceptual
model
also
includes
factors
that
could
potentially
be
associated
with

community
integration
levels.
For
example,
where
Wong
and
Solomon’s
model

concentrated
on
an
array
of
socio-‐demographic
and
environmental
factors
potentially

associated
with
community
integration,
studies
by
Prince
and
Prince
(Prince
&
Prince,

28

2002)
and
Gulcur
et
al.
(Gulcur,
Tsemberis,
Stefancic,
&
Greenwood,
2007)
emphasized
the

role
of
stigma
on
different
dimensions
of
community
integration
of
individuals
with
SMI.

The
studies
found
stigma
to
be
negatively
associated
with
psychological
integration,

indicating
that
stigma
might
have
a
detrimental
effect
on
an
individual’s
sense
of
identity

and
belonging
(Gulcur
et
al.,
2007).

Stigma
in
Mental
Health

Stigma
is
defined
as
an
attitude
whereby
certain
individuals
have,
or
are
believed
to

have,
some
attributes
that
set
them
apart
from
others
and
lead
them
to
be
excluded,

rejected,
blamed,
or
devalued
(Day,
Edgren,
&
Eshleman,
2007;
Major
&
O'Brien,
2005;

Weiss
&
Ramakrishna,
2006).
Marginalization
and
stigmatization
of
individuals
with
a

mental
illness
are
harsh
realities
that
individuals
with
SMI
and
their
families
have
to
face

(Lloyd,
King,
&
Moore,
2010).
Various
studies
have
reported
that
stigma
associated
with
a

mental
illness
has
the
potential
of
being
more
destructive
than
the
actual
illness
itself
(P.
W.

Corrigan,
1999;
Day
et
al.,
2007).
Individuals
with
a
mental
illness
are
more
likely
to

remain
unemployed
(Stuart,
2006;
Tsang
et
al.,
2007),
have
relatively
less
income

compared
with
individuals
without
a
mental
illness,
have
limited
social
networks
and
less

social
support,
and
have
lower
self-‐esteem
(Link,
Cullen,
Frank,
&
Wozniak,
1987;
Perkins

et
al.,
2009).
Limited
social
network
resources
caused
by
associated
stigma
leads
to

isolation
and
makes
individuals
with
SMI
more
vulnerable
to
stress,
increasing
the

probability
of
future
relapse
(Penn
&
Martin,
1998;
Perkins
et
al.,
2009).
Individuals
with

SMI
also
have
difficulty
being
close
to
people
because
they
are
generally
perceived
to
be

dangerous
or
untrustworthy
(Mehta
&
Farina,
1997).
Faced
with
such
stigma,
individuals

with
SMI
use
various
strategies,
such
as
secrecy,
denial,
and
silence,
as
protection
against

29

the
discrimination
associated
with
mental
illness
(J.
H.
Jenkins
&
Carpenter-‐Song,
2008).

Gender,
too,
tends
to
interact
with
stigma
to
impact
outcomes
and
levels
of
community

involvement.
Literature
has
shown
that
women
tend
to
conceal
the
presence
of
mental

illness
more
than
men
in
reaction
to
perceived
stigma,
which,
in
turn,
has
implications
for

community
reintegration
(Phelan,
Bromet,
&
Link,
1998).

There
are
many
theoretical
models
linked
with
stigma
and
its
perception.
Major
and

O’Brien
(2005)
have
talked
about
the
identity-‐threat
model,
whereby
stigma
and

stigmatizing
situations
are
perceived
as
a
threat
to
the
sense
of
self
of
an
individual
with

SMI
and
impacts
their
identity
(Major
&
O'Brien,
2005;
Yang
et
al.,
2007).
Along
similar

lines,
Corrigan
and
Watson
(P.
W.
Corrigan
&
Watson,
2002)
have
proposed
a
social-‐
cognitive
model
of
stigma
whereby
exposure
to
stigma
leads
to
a
personal
response,
which

leads
to
the
internalization
of
stigma
and
can
cause
self-‐discrimination
(P.
W.
Corrigan
&

Watson,
2002;
Yang
et
al.,
2007).

Scheff
(1966)
and
Link
et
al.
(1989)
have
proposed
the
labeling
theory
and
modified

labeling
theory
(Link,
Yang,
Phelan,
&
Collins,
2004;
Scheff,
1966).
Whereas
both
theorists

explain
the
process
of
stigma
via
socialization
and
reinforcement,
the
modified
labeling

theory
linked
the
labeling
processes
to
negative
outcomes
related
to
a
mental
illness.

The
present
study
has
explored
stigma
in
the
context
of
the
modified
labeling
theory

to
examine
whether
stigma
in
individuals
with
SMI
is
associated
with
their
levels
of

community
integration.
Although
stigma
associated
with
mental
illness
seems
to
be

pervasive
across
cultures
(J.
H.
Jenkins
&
Carpenter-‐Song,
2008;
Link
et
al.,
2004),
it
needs

to
be
studied
in
the
socio-‐cultural
context
of
an
individual
to
delineate
its
origins,

meanings,
and
associated
outcomes
(J.
H.
Jenkins
&
Carpenter-‐Song,
2008;
Thara
&

30

Srinivasan,
2000).
Studies
on
stigma
in
different
cultures
have
shown
some
differences
and

commonalities
in
terms
of
attitudes
toward
mental
illnesses
(Thara
&
Srinivasan,
2000).
As

identified
by
Yang
et
al.
(2007),
stigma
is
conceptualized,
expressed,
and
practiced

differently
across
different
cultures
and,
hence,
needs
to
be
studied
within
the
context
of
an

individual’s
local
social
world
(Coker,
2005;
Yang
et
al.,
2007).

Stigma
and
Disclosure
about
Mental
Illness

Individuals
with
a
mental
illness
often
hide
their
illnesses
from
people
around
them.

This
lack
of
disclosure
about
one’s
mental
illness
might
be
due
to
a
fear
of
rejection,

discrimination,
and
even
loss
of
self-‐esteem
(Chaudoir
&
Fisher,
2010).
Lack
of
disclosure

might
also
be
caused
by
previous
bad
experiences
associated
with
disclosure
(Omarzu,

2000).
An
individual
with
a
mental
illness
could
be
evaluated
less
favorably
and
judged
for

their
mental
illness,
resulting
in
a
change
in
people’s
perception
and
behavior
toward
them

(Farina,
Gliha,
Bourdreau,
Ale,
&
Sherman,
1971).

Whereas
disclosure
could
have
negative
repercussions,
there
are
positive
effects
of

disclosure.
Self-‐disclosure
has
been
associated
with
higher
levels
of
trust
and
better
mental

health
by
decreasing
alienation
and
a
sense
of
consonance
between
ones
self-‐concept
and

others’
perceptions
(Rosenfeld,
1979).
Disclosure
could
increase
self-‐awareness
and

societal
awareness
about
an
individual’s
mental
illness,
potentially
reducing
the
associated

stigma
in
the
future
(Chaudoir
&
Fisher,
2010).
As
Garcia
and
Crocker
state,
“disclosing

enables
stigmatized
people
to
both
receive
support
and
provide
social
support
to
others

who
have
the
stigma,
thereby
decreasing
feelings
of
loneliness
and
isolation
for
the
self
and

others
…
because
one’s
mental
health
status
is
not
always
readily
visible,
disclosure
may
be

necessary
to
facilitate
contact”
(Garcia
&
Crocker,
2008).

31

Because
disclosure
has
been
recognized
as
an
important
extension
of
stigma,
and

stigma
has
been
recognized
as
an
important
deterrent
to
community
integration,
we
used

multilevel
analysis
to
study
various
individual
and
social
network
factors
that
could
be

associated
with
disclosure
related
to
one’s
mental
illness.

Community
Integration:
Normalization
versus
Subcultures

In
addition
to
how
integration
is
defined,
there
is
also
tension
in
the
literature
about

how
individuals
with
SMI
define
their
sense
of
belonging
in
a
particular
community.
There

are
two
differing
value-‐oriented
perspectives
in
the
literature
on
community
integration

for
the
mentally
ill:
ideas
of
normalization
and
subcultures
or
identity
communities.

Traditionally,
community
integration
has
been
based
on
the
concept
of
normalization

(Bond
et
al.,
2004;
Cummings
&
Kropf,
2009;
Wolfensberger,
2011;
Wolfensberger
&

Tullman,
1982).
Normalization
postulates
that
individuals
with
mental
illness
belong
to

and
are
expected
to
be
reintegrated
in
to
the
general
community
(Bond
et
al.,
2004;
McColl

et
al.,
2001),
potentially
resulting
in
a
shift
of
an
individual’s
primary
identity
from
that
of
a

mental
health
patient
to
well-‐integrated
members
of
the
general
community
(Bond
et
al.,

2004;
McColl
et
al.,
2001;
Pilisuk,
2001).
Individuals
who
spend
more
of
their
time
in
the

mental
health
community
and
whose
networks
primarily
consist
of
mental
health
peers

and
service
providers
are
considered
to
be
further
away
from
recovery
(Bond
et
al.,
2004).

However,
literature
also
shows
that
many
individuals
with
a
mental
illness
living
in

the
general
community
are
isolated,
and
once
they
are
moved
away
from
their
mental

health
community
in
the
name
of
normalization,
they
become
isolated
even
further
(Ware

et
al.,
2007;
Wong
et
al.,
2010).
In
this
regard,
Mandiberg
(1999)
has
discussed
the
concept

32

of
a
mental
health
subculture,
later
renamed
by
Mandiberg
(2012)
as
identity
communities

(Mandiberg,
1999,
2012).

The
idea
of
identity
communities
postulates
that
community
for
individuals
with

mental
illness
is
a
combination
of
both
mental
health-‐based
identity
and
belonging
to
the

larger
non-‐mental
health
community
(Mandiberg,
2012;
Wong
et
al.,
2010).
The
identity

perspective
stresses
that
being
connected
to
the
mental
health
identity
communities
and

trying
to
get
reintegrated
into
the
dominant
community
can
go
in
tandem,
with
the

individual
deciding
the
extent
to
which
they
want
to
be
a
part
of
each
community
at

different
times.
The
identity
community
could
be
a
source
of
identity,
support,
and
buffer

from
stigma
prevalent
in
the
non-‐mental
health
community
(Mandiberg,
1999).

Unfortunately,
there
has
been
little
empirical
investigation
into
the
two
facets—
normalization
and
subcommunities—of
community
integration
for
individuals
with
SMI.

Despite
being
recognized
both
as
an
important
component
of
the
recovery
process

and
an
outcome
in
itself,
community
integration
is
not
well
understood
(Carling,
1990;

Wong
et
al.,
2010).
The
current
study
defines
community
integration
as
belonging,

acceptance,
and
participation,
using
measures
of
different
dimensions
of
community

integration.

Research
on
Community
Integration
of
the
Individuals
with
SMI

There
is
little
empirical
research
on
the
different
dimensions
of
community

integration.
There
is
one
study
by
Gulcur
et
al.
(2007)
that
examined
the
factor
structure
of

community
integration
dimensions
by
Wong
and
Solomon
followed
by
a
predictive
model

to
study
the
association
between
community
integration
dimensions
and
factors
such
as

symptomology
and
stigma
(Gulcur
et
al.,
2007).
Whereas
this
study
found
negative

33

associations
between
stigma
and
community
integration,
as
well
as
symptomatology
and

community
integration,
this
study,
alone,
is
not
sufficient
to
make
predictions
about
the

process
of
community
integration
and
the
factors
associated
with
it
(Gulcur
et
al.,
2007).

Another
study
by
Cohen,
Pathak,
Ramirez,
&
Vahia
(2009)
on
community
integration
of

older
adults
with
schizophrenia
used
five
conceptual
models
to
study
the
association
of
age

with
various
other
variables,
including
community
integration
(C.
I.
Cohen,
Pathak,

Ramirez,
&
Vahia,
2009).
Cohen
et
al.
(2009)
used
Wong
and
Solomon’s
model
to
study

community
integration.
The
study
found
that
individuals
without
schizophrenia
were
twice

as
integrated
in
the
community
compared
with
their
similarly
aged
counterparts
with

schizophrenia.

A
study
by
Bromley
et
al.
(2013)
used
qualitative
semi-‐structured
interviews
to

study
the
concept
of
community
and
community
integration
in
a
sample
of
30
individuals

with
SMI
getting
service
in
two
public
mental
health
clinics.
The
study
found
four
themes

integral
to
their
concepts
of
community:
receiving
help,
minimizing
risk,
avoiding
stigma,

and
giving
back.
The
study
also
found
the
mental
health
community
as
a
source
of
identity

and
belonging
for
individuals
with
SMI,
hence
questioning
the
integration
of
individuals

into
the
mainstream
community
as
a
singular
goal
of
recovery
(Bromley
et
al.,
2013).

Our
previous
study
on
community
integration
(Pahwa
et
al.,
2013)
is
the
only
study

in
the
literature
to
use
multiple
methodologies
to
understand
the
concept
of
community

integration
within
the
normalization
and
subcommunities
perspective
and
its
association

with
service
intensity.
The
study
used
data
from
33
ethnically
diverse
individuals
with
SMI.

Eighteen
participants
belonged
to
a
high-‐service
intensity
group,
and
15
belonged
to
a
low-‐
service
intensity
group.
Community
integration
was
measured
using
the
community-‐

34

integration
scale,
involvement
in
community
activities
scale,
social
resources
scale,
social

support
scale,
and
social
network
maps.
The
results
showed
that,
in
terms
of
psychological

integration,
the
individuals
felt
more
integrated
into
the
mental
health
community.

However,
in
terms
of
social
integration
(measured
via
their
social
network
characteristics),

individuals
relied
more
on
their
non-‐mental
health
communities
(Pahwa
et
al.,
2013).

These
findings
were
replicated
in
the
low-‐intensity
group,
but
the
high-‐intensity
group

showed
more
physical,
social,
and
psychological
integration
into
the
mental
health

community.

The
current
dissertation
was
built
on
the
findings
of
Pahwa
et
al.
(2013).
In
addition

to
the
same
theoretical
model
by
Wong
and
Solomon,
the
current
study
also
used
network

and
quantitative
methodologies,
as
well
as
used
the
same
measures
operationalized,
to

understand
different
dimensions
of
community
integration.
To
reiterate,
the
current
study

has
made
important
additions
to
the
previous
work
in
the
following
ways:

1. The
current
study
included
a
cross-‐national
component
to
the
study
to
get
a
more

nuanced
understanding
of
community
integration
within
an
individual’s
cultural

context.

2. The
current
study
expanded
the
social
dimension
of
community
integration
using

bonding
and
bridging
social
capital
perspectives
of
the
social
capital
theory.

3. The
current
study
enhanced
the
network
methodology
used
in
the
previous
study
to

include
relational-‐level
network
data
to
attain
more
information
about
an

individual’s
networks.

4. The
current
study
added
stigma
as
a
variable
that
might
impact
the
different

dimensions
of
community
integration.

35

5. The
current
study
incorporated
multilevel
modeling
to
study
the
association

between
stigma
and
various
individual-‐level
and
network-‐level
variables.

36

CHAPTER
THREE:

METHODS

Data
Source

This
dissertation
was
a
cross-‐sectional
study
designed
to
understand
community

integration
for
individuals
with
SMI
seeking
treatment
in
community
mental
health

agencies
in
India
and
the
United
States.
The
data
were
collected
by
the
author,
who
was

also
the
principal
investigator
(PI),
with
the
help
of
two
trained
doctoral
research

assistants.
The
study
used
quantitative
measures
and
network
data
using
social
network

analysis
from
voluntary
participants
in
India
and
United
States.
This
is
the
first
study
to

examine
and
compare
community
integration
for
individuals
with
SMI
in
the
two
countries.

Because
of
the
exploratory
nature
of
the
study
and
innovative
mixture
of
social
network

and
quantitative
methodology
for
individuals
with
SMI,
the
PI
conducted
feasibility
trials
in

the
United
States
and
India.

Preliminary
Studies

Community
integration:
A
pilot
study
in
the
United
States.
For
the
pilot
study
in

the
United
States,
data
were
collected
on
different
dimensions
of
community
integration

from
33
ethnically
diverse
individuals
with
SMI
treated
in
two
publicly
funded
mental

health
clinics
in
Los
Angeles
County.
Community
integration
was
assessed
using
different

measures,
including
measures
of
involvement
in
community
activities,
social
resources,

social
support,
social
network
maps,
and
subjective
sense
of
integration.
The
results

indicated
a
difference
in
community
integration
behavior
of
individuals
from
the
high-‐
intensity
service
group
and
the
low-‐intensity
service
group.
The
low-‐intensity
group
was

associated
with
greater
integration
into
the
non-‐mental
health
community,
more
overall

37

social
resources,
and
less
embedding
into
the
mental
health
community.
Results
from
the

preliminary
study
have
been
documented
in
Bromley
et
al.
(2013)
and
Pahwa
et
al.
(2013).

Feasibility
trial
in
India:
To
test
the
feasibility
of
the
study
and
transferability
of

the
measures,
the
preliminary
quantitative
community
integration
study
(CI
study)

previously
described
was
replicated
in
a
community-‐based
psychiatric
hospital
that

provides
outpatient
mental
health
services
in
Chandigarh,
India.
The
feasibility
trial
with

five
subjects
showed
that
although
the
measures
worked,
the
entire
process
took
far
longer

than
in
the
United
States
because
the
participants
asked
the
interviewer
for
Hindi

translations
of
the
more
technical
and
mental
health-‐related
terms.
Hence,
a
decision
was

made
to
translate
the
measures
into
Hindi
and
use
both
the
Hindi
and
English
measures,

depending
on
the
language
preference
of
the
participants.
The
study
scales
and
the

qualitative
interview
were
translated
into
Hindi
by
a
team
of
two
mental
health

professionals
and
a
professional
translator.
Back
translation
of
the
Hindi
adaptation
was

performed
to
examine
the
conceptual
and
semantic
equivalence
of
the
translation.
The

Hindi
translation
was
discussed
with
the
experts.
A
pilot
study
for
comparison
of
the

English
and
Hindi
version
was
conducted.
A
sample
of
five
patients
diagnosed
with
SMI
was

given
the
English
and
the
Hindi
versions
of
the
same
scale,
and
responses
were
compared.

Suggestions
and
feedback
regarding
simplicity
of
the
language
used
and
understandability

of
the
scale
were
taken
and
incorporated
into
the
final
modification.

Recruitment
of
the
Sample

The
sample
for
this
study
came
from
a
subset
of
a
parent
study,
which
was

conducted
in
India
and
the
United
States
by
the
PI
(Pahwa)
to
understand
community

integration
of
consumers
of
outpatient
mental
health
services
in
the
two
countries.
The

38

parent
study
consisted
of
a
sample
of
91
participants,
with
60
participants
from
the
United

States
and
31
participants
from
India.
Of
the
60
individuals
in
the
United
States,
30
came

from
high-‐intensity
community
treatment
teams,
called
full-‐service
partnerships,
that
are

grounded
in
the
assertive
community
treatment
model
with
low
caseloads
(20:1)
and
an
in

vivo
“whatever-‐it-‐takes”
approach.
The
other
30
belonged
to
the
usual
care
outpatient

model
that
uses
a
traditional
office-‐based
approach
(one
to
four
appointments
per
month)

with
medication
management
and
case
management
or
therapy.
The
Indian
sample
was

collected
from
individuals
at
the
low-‐intensity
service
usual
care
site
who
also
used
the

traditional
office-‐based
approach
with
medicine
management
and
therapy.
The
results
of

the
feasibility
trial
in
the
United
States
(Bromley
et
al.,
2013;
Pahwa
et
al.,
2013)
indicated

differences
in
community
integration
in
mental
health
and
non-‐mental
communities
for

individuals
in
different
service
intensities.
Considering
these
results,
and
because
the

Indian
sample
only
consisted
of
individuals
from
the
low-‐intensity
service
setting,
a
subset

of
the
parent
study
using
the
low-‐intensity
service
participants
was
selected
and
used
for

the
current
study.

Participants
were
recruited
in
India
from
a
community-‐based
psychiatric
hospital

providing
outpatient
mental
health
services
to
individuals
with
mental
illnesses
living
in

the
local
and
neighboring
cities.
Eligibility
for
participation
included
the
following

characteristics:

1.
Between
18
and
65
years
of
age

2.
Had
a
diagnosis
of
schizophrenia
spectrum
disorder
(i.e.,
schizophrenia,

schizoaffective,
or
schizophreniform
disorder),
bipolar
disorder,
or
major
depression

3.
Had
availed
the
outpatient
mental
health
services
in
that
particular
site
for
at

39

least
two
months.

The
exclusion
criteria
included
the
following:

1.
A
diagnosis
of
mental
retardation
or
an
identifiable
neurological
disorder

2.
If
they
met
criteria
for
drug
or
alcohol
abuse
or
dependence
in
the
previous
six

months.

To
maintain
confidentiality,
the
department
representatives
at
each
site
identified

the
potential
participants
who
met
the
study
criteria
and
contacted
them.
Once
the

participants
were
recruited,
the
PI
gained
their
consent,
gave
each
participant
an

information
sheet
describing
the
study,
and
addressed
any
doubts
or
questions.
The

approximate
length
of
the
data
collection
process
(including
the
survey
and
network

interview)
was
45-‐60
minutes
per
participant.
Data
were
collected
in
India
from
October

2011
to
December
2011
and
June
2012
to
August
2012
and
from
April
2012
to
July
2012
in

the
United
States.
A
matched-‐pair
sampling
strategy
was
used
to
recruit
participants
in
the

United
States
receiving
outpatient
mental
health
services
at
a
community-‐based
clinic,
both

in
the
high-‐
and
low-‐intensity
groups.
The
participants
were
matched
on
diagnosis,

functioning,
and
demographic
characteristics,
such
as
gender
and
age.
For
the
actual

process
of
matching,
demographic
and
diagnostic
information
of
the
participants
from
the

Indian
sample
were
given
to
the
service
providers
in
the
United
States.
The
service

providers
then
identified
their
matches,
made
the
initial
contact,
and,
if
the
consumers

were
interested,
referred
the
consumers
to
the
PI
or
the
research
assistants
for
the

consenting
process.
The
study
was
reviewed
and
approved
by
the
Institution
Review

Boards
at
the
University
of
California,
Los
Angeles,
the
University
of
Southern
California,

and
the
Los
Angeles
County
Department
of
Mental
Health
Human
Subjects
Committee.
The

40

PI
was
advised
by
the
Indian
agency
not
to
pay
the
participants
at
the
Indian
site
because
it

is
not
only
culturally
inappropriate,
but
could
also
be
construed
as
coercive.
Hence,
only

American
participants
were
paid
$25
per
interview.

Data
Collection

Part
1:
Face-‐to-‐face
network
interview.
Personal
network
data
were
collected
as

a
part
of
the
face-‐to-‐face
interview
using
the
free
recall
name
generator,
whereby

participants
responded
to
a
prompt
that
defined
certain
criteria
(Rice
et
al.,
in
press).
Using

the
free
recall
name
generator
is
fairly
common
in
social
network
analysis
and
enables
the

nomination
of
both
strong
and
weak
ties
(Brewer,
2000;
McCarty
&
Govindaramanujam,

2005).
Additionally,
completeness
of
an
individual’s
network
is
directly
determined
by

inclusiveness
of
the
prompts
used
in
a
name
generator
(McCarty,
Killworth,
&
Rennell,

2007).
As
is
suggested
by
the
name,
the
effectiveness
of
the
free
recall
name
generator
is

dependent
on
the
memory
of
the
respondent.
Failure
to
recall
or
a
biased
recall
directly

impact
the
quality
of
network
data
collected
(McCarty
et
al.,
2007).
Multiple
prompts
and

subsequent
probes
reduce
these
effects
(Brewer,
2000;
Brewer
&
Garrett,
2001;
McCarty
et

al.,
2007).

The
initial
prompt
used
for
the
current
network
interview
was,
“Think
about
the
last

two
months.
I
want
to
know
about
the
people
you
have
had
contact
with,
who
you
talk
to,

who
you
hang
out
with,
who
you
talk
on
the
phone
with,
who
you
email,
text,
or
talk
with

on
the
Internet.”
The
respondents
were
asked
to
list
as
many
people
as
they
could.
Another

option
would
have
been
to
give
the
exact
number
of
possible
alters
(individuals
in
their

networks)
the
respondents
could
nominate.
However,
that
would
have
forced
the

responders
to
nominate
a
specific
number
of
connections,
which
could
have
increased

41

responder
bias
(McCarty
et
al.,
2007).
To
invoke
as
many
names
as
possible,
subsequent

prompts
were
used
to
identify
different
networks,
such
as
“friends,”
“family,”
“case

workers,”
“people
you
know
in
your
neighborhood,”
“girlfriend/boyfriend
or

husband/wife,”
“neighbor,”
“colleague,”
“people
you
know
from
agencies,”
and
“friends
you

knew
before
you
started
using
mental
health
agencies.”

An
egocentric
network
graph
was
created
for
each
participant,
and
directed
ties

were
drawn
from
the
participant
to
and
from
every
alter
nominated
in
their
network.
The

nominated
people
were
represented
with
different
color
codes
signifying
their
affiliation
to

the
mental
health
community
or
non-‐mental
health
community.
Friends
and
family

members
were
also
identified.
The
participants
were
asked
a
follow-‐up
question
about

whom
they
go
to
when
they
need
help.
Participants
were
then
asked
specific
questions

about
each
alter
nominated
in
their
network.
The
questions
included
the
following:

• Socio-‐demographic
data:
Information
was
collected
about
the
alters
such
as
gender,

age,
and
ethnicity
(only
valid
in
the
U.S.
sample),
as
well
as
the
type
of
relationship

(e.g.,
family
member,
friend,
or
mental
health
service
provider).

• Relationship
quality:
To
assess
strength
of
ties,
information
on
the
closeness
of
the

relationship
was
collected
(ranging
from
1

=
not
at
all
to
5

=
extremely),
along
with

the
frequency
of
contact
(ranging
from
1

=
almost
every
day
to
4

=
less
than
once
a

month).

• Information
on
whether
or
not
the
alter
was
a
part
of
the
mental
health
community

and
if
they
were
a
service
provider
or
a
consumer.

• Social
support:
Network
interviews
gained
information
about
the
specific
kinds
of

social
support
the
participant
received
and
the
person
in
their
network
they

42

received
it
from.
To
specify
the
kind
and
source
of
social
support,
the
four
items

included
in
the
Rand
Medical
Outcomes
Study
Social
Support
(MOSSS)
survey

developed
by
Sherbourne
and
Stewart
(1991)
were
used
as
network

prompts(Sherbourne
&
Stewart,
1991).
For
example,
the
question
in
the
survey
that

asked,
“How
often
do
you
have
someone
to
help
with
daily
chores
if
you
are
sick?”

was
reworded
as
a
network
question,
and
the
participant
was
asked
to
identify
the

people
in
their
network
who
helped
them
with
daily
chores
if
they
became
sick.
The

four
social
support
network
questions
adapted
from
the
MOSSS
measure
were
as

follows:

o Who
in
this
network
will
you
go
to
if
you
need
help
with
daily
chores
if
you

are
sick
(tangible
support)?

o Who
in
this
network
will
you
turn
to
for
suggestions
about
how
to
deal
with
a

personal
problem
(emotional
support)?

o Who
in
the
network
would
you
do
something
enjoyable
with
(positive
social

interaction)?

o Who
in
the
network
loves
you
and
makes
you
feel
wanted
(affectionate

support)?

• Social
resources:
Information
on
social
resources
was
also
gathered
using
the
social

network
interview.
Questions
were
taken
from
the
social
capital
resource
generator

(Van
Der
Gaag
&
Webber,
2008)
and
adapted
to
the
mental
health
population.
For

example,
the
question
on
the
resource
generator
that
asked
the
participant
if
they

had
someone
who
“has
a
car
you
can
borrow
sometimes,”
asked
the
individual
to

specify
and
select
everyone
in
their
network
they
would
go
to
if
they
needed
to

43

borrow
a
car.
Additional
choices
of
“none
of
the
above”
and
“all
of
the
above”
were

included
in
case
the
individual
did
not
have
access
to
a
particular
resource
or
if
they

had
access
to
a
resource
from
everyone
in
their
network.
The
information
gathered

from
these
questions
was
used
in
two
ways.
A
sum
score
representing
the
total

social
resources
was
used
as
a
proxy
for
physical
community
integration
in
Wong

and
Solomon’s
model
for
community
integration.
Additionally,
network
information

in
the
form
of
specific
relationships
that
are
sources
of
specific
forms
of
social

resources
were
identified
as
bridging
or
bonding
social
capital
and
used
as
a

suggested
expansion
of
the
social
integration
dimension
of
Wong
and
Solomon’s

model
of
community
integration.
Specific
questions
adapted
from
the
resource

generator
and
included
in
the
network
interview
were
as
follows:

• Domestic
social
resources:

o Who
in
the
network
knows
where
to
find
bargains?

o Who
in
the
network
has
a
car
you
can
borrow
sometimes?

o Who
in
the
network
could
help
you
to
find
somewhere
to
live
if
you
had
to

move?

o Who
in
the
network
could
help
you
make
your
home
more
pleasant
or

comfortable?

• Expert
advice
social
resources:

o Who
in
the
network
gives
good
advice
about
earning
money?

o Who
in
the
network
gives
you
good
advice
about
money
problems?

• Personal
skills
social
resources:

o Who
in
the
network
knows
about
good
places
to
live?

44

o Who

in
the
network
offers
opportunities
for
fun
activities?

o Who

in
the
network
knows
about
volunteer
or
work
opportunities?

o Who

in
the
network
knows
about
education
or
job
training?

o Who

in
the
network
can
sometimes
employ
people?

o Who

in
the
network
could
help
you
take
care
of
your
health?

• Problem-‐solving
social
resources:

o Who

in
the
network
knows
how
to
fix
problems
with
computers?

o Who

in
the
network
knows
a
lot
about
benefits
you
might
qualify
for?

o Who

in
the
network
knows
how
to
get
around
the
city?

o Who

in
the
network
knows
a
lot
about
fixing
things
around
the
house?

• Stigma:
Questions
on
stigma
and
sources
of
stigma
were
also
asked
during
the

network
interview.
For
example,
the
participant
was
asked
to
identify
everyone
in

their
network
who
“knows
that
you
have
a
mental
illness”
and
“who
would
you
not

wish
to
tell
that
you
have
a
mental
illness.”
An
additional
choice
of
“none
of
the

above”
was
included
in
case
the
individual
could
not
identify
anyone
in
his
or
her

network
who
could
be
a
potential
source
of
stigma.

o Who
in
your
network
knows
that
you
have
a
mental
illness?

o Who
would
you
not
wish
to
tell
that
you
have
a
mental
illness?
Of
the
people

who
already
know,
who
do
you
wish
didn’t
know
about
your
mental
illness?

o Who
would
look
at
or
behave
differently
with
you
if
they
found
out
that
you

have
a
mental
illness?

o Who
would
stop
talking
to
you
if
they
found
out
that
you
have
a
mental

illness?

45

• Additional
variables
derived
from
the
network
questions
included
size
and

proportion
of
alters
in
each
network
that
represented
different
relationship
types

like
friends,
family,
mental
health
professionals,
and
mental
health
peers.

Part
2:
Quantitative
survey.
Quantitative
data
came
from
the
following
scales
that

measured
different
dimensions
of
community
integration
and
were
completed
by
the

participants
as
part
of
a
battery
of
instruments
administered
by
the
research
interviewer.

Socio-‐demographic
characteristics.
Demographic
information
on
age,
gender,

education
level,
ethnicity
(only
for
the
U.S.
sample),
relationship
status,
and
employment

status
was
gathered
using
a
demographic
measure.
To
obtain
the
information
on
age,

participants
were
asked
to
report
their
date
of
birth,
and
this
information
was
converted

into
a
continuous
variable
representing
their
age
for
analysis.
Gender
was
coded
into
two

categories
with
values
of
0
and
1
assigned
to
females
and
males,
respectively.
Information

about
relationships
included
six
categories:
currently
married,
widowed,
divorced,

separated,
never
married,
and
with
a
partner.
This
variable
was
dichotomized
into
a
single

item
regarding
whether
or
not
an
individual
was
currently
married
or
with
a
partner
(1

=

married/with
a
partner;
0

=
single,
divorced,
separated,
or
widowed)
for
future
analyses.

Finally,
a
single
question
asked
whether
an
individual
was
currently
employed
to
obtain

their
employment
status
(1

=
currently
employed;
0

=
not
employed).

Community
integration
measures.

Physical
community
integration.
Physical
integration
was
defined
as
the
“extent
to

which
an
individual
participates
in
activities”
and
“uses
goods
and
services
in
community”

(Wong
&
Solomon,
2002).
It
was
measured
by
the
two
following
scales.

46

Involvement
in
community
activities
scale.
This
scale
measured
the
extent
to
which

an
individual
participated
in
community
activities.
It
was
adapted
from
the
subscale
on

community
activities
of
a
larger
scale
measuring
independent
living
skills
developed
by

Wallace,
Liberman,
Tauber,
&
Wallace
(Wallace,
Liberman,
Tauber,
&
Wallace,
2000).

The

subscale
consisted
of
11
items.
The
first
10
items
asked
the
responders
to
indicate
whether

they
had
participated
in
any
of
10
specific
community
activities
in
the
past
30
days.
The
last

item
asked
their
satisfaction
with
their
level
of
community
activity.

Social
resources
scale.
The
extent
to
which
an
individual
used
“goods
and
services”

(Wong
&
Solomon,
2002)
in
a
community
was
measured
using
an
adaptation
of
the
social

capital
resource
generator
(Van
Der
Gaag
&
Webber,
2008).
The
resource
generator
was

expanded
to
trace
the
sources
of
social
resources
from
both
mental
health
and
non-‐mental

health
individuals.
This
scale
included
questions
such
as,
“Is
there
someone
in
your
life
now

who
has
a
car
you
can
borrow
sometimes?”
A
summed
score
of
total
social
resources
and

social
resources
from
mental
health
and
non-‐mental
health
communities
was
created
and

used
as
a
proxy
of
physical
community
integration.

Psychological
community
integration.
Psychological
integration,
defined
as
the

extent
to
which
an
individual
perceives
membership
in
a
community
(Wong
&
Solomon,

2002),
was
measured
in
two
ways.
First,
we
used
the
Community
Integration
Measure

(CIM;
(McColl
et
al.,
2001),
a
10-‐item
self-‐report
measure
that
assesses
the
degree
to
which

someone
participates,
feels
connected
to,
and
feels
a
sense
of
belonging
in
their
community.

It
included
statements
such
as,
“I
feel
like
part
of
this
community,
like
I
belong
here,”
and

asks
the
responders
to
agree
or
disagree
with
the
statement
on
a
5-‐point
Likert
scale,
with

smaller
values
representing
more
agreement.
Subjects
were
asked
to
respond
to
two

47

versions
of
the
scale.
One
version
was
targeted
to
the
mental
health
community
of
peers

and
providers,
and
the
other
version
targeted
the
non-‐mental
health
community,
consisting

of
friends,
family,
and
non-‐mental
health
acquaintances.
The
scale
has
a
reliability
of
0.87

(McColl
et
al.,
2001).
For
the
sample
in
this
study,
alpha
for
the
version
pertaining
to
the

mental
health
community
was
0.87,
and
alpha
for
the
version
pertaining
to
the
non-‐mental

health
community
was
0.89.

Second,
we
used
the
CIM
Interviewer
rating
scale,
adapted
from
the
CIM
(McColl
et

al.,
2001).
It
consists
of
six
items
that
ask
the
interviewer
to
rate
the
degree
to
which
the

subject
participated
in,
was
connected
to,
and
was
accepted
by
the
mental
health
and
non-‐
mental
health
communities.
Cronbach’s
alpha
for
the
three
mental
health
community
items

was
.88
and
.93
for
the
three
non-‐mental
health
community
items.
These
questions
gauge

at
the
interviewer’s
perception
of
each
participant’s
community
involvement
from
the

entire
interview
process,
including
the
network
interview.
The
interviewer
was
asked
to

complete
this
scale
at
the
end
of
the
interviews.

Social
community
integration.

Social
intergration
was
defined
in
terms
of
the
an

individual’s
perception
of
social
support
available
to
them
and
their
social
networks
(Wong

&
Solomon,
2002).
First,
perceived
availability
of
social
support
was
measured
by
the
4-‐
item
short
version
of
the
MOSSS
(Sherbourne
&
Stewart,
1991).
This
survey
is
rated
from
1

to
5,
with
higher
ratings
indicating
more
social
support.
The
4-‐item
scale
has
a
reliability
of

.83,
and
in
this
sample,
reliability
was
.73
(Gjesfjeld,
Greeno,
&
Kim,
2007).

Second,
the
aforementioned
social
network
interview
was
used
to
operationalize

the
social
network
dimension
of
social
integration
(Rice,
2010;
Rice,
Milburn,
&
Monro,

2011).
The
PI
and
the
interviewers
were
trained
and
supervised
in
the
network
mapping

48

method
by
the
developer
of
the
method.
Affiliation
to
the
mental
health
community
or
non-‐
mental
health
community
was
identified
for
each
network
member.
Network
data
from
this

interview
included
variables
such
as
network
size
and
composition
of
the
network,

including
proportions
of
mental
health
and
non-‐mental
health
members,
friends,
family,

mental
health
peers,
and
mental
health
providers,
and
the
data
were
used
as
proxies
of

social
integration.
These
data
were
quantified
using
the
procedure
described
in
Rice

(2010).

In
addition
to
using
the
social
support
and
the
general
social
network

characteristics,
the
current
study
used
the
social
capital
theory
to
expand
the
social

community
integration
dimension
proposed
by
Wong
and
Solomon
(Wong
&
Solomon,

2002).
Availability
of
social
resources
from
specific
network
members
in
the
form
of

bridging
and
bonding
social
capital
were
conceptualized
as
an
extension
of
the
social

community
integration.
For
this
purpose,
the
network
questions
on
social
resources

previously
mentioned
were
used
to
assess
social
integration
in
terms
of
specific
network

members.

Additional
predictors.

Stigma
measure.
The
Internalized
Stigma
of
Mental
Illness
scale
(ISMI,
Ritsher
&

Phelan,
2004;
Ritsher-‐Boyd,
Otilingam,
&
Grajales,
2003)
was
used
to
measure
the

internalized
stigma
of
the
consumers.
The
original
ISMI
is
a
29-‐item,
5
factor,
4-‐point
Likert

scale
and
measures
five
dimensions
of
internalized
stigma.
We
excluded
the
fifth
factor
in

our
study
because
the
alphas
of
the
four
factors
in
the
original
study
by
Ritsher-‐Boyd
were

found
to
be
greater
than
the
threshold
of
α
=
.70,
except
for
the
stigma
resistance
subscale

(α
=
.58;
(Ritsher
&
Phelan,
2004;
Ritsher-‐Boyd
et
al.,
2003;
Stevelink,
Wu,
Voorend,
&
van

49

Brakel,
2012).
The
four
factors
used
in
the
study
were
as
follows:
(1)
alienation,
which

measured
the
subjective
experience
of
being
less
than
a
full
member
of
society
or
having
a

spoiled
identity;
(2)
stereotype
endorsement,
which
measured
the
degree
to
which

respondents
agreed
with
common
stereotypes
about
people
with
mental
illness;
(3)

discrimination
experience,
which
measured
the
extent
to
which
people
experience

discrimination
associated
with
a
mental
illness;
and
(4)
social
withdrawal,
which
measured

the
extent
to
which
people
withdraw
socially
as
a
result
of
experiencing
self-‐stigma.
The

alpha
of
the
4-‐factor
version
was
.91
of
the
original
scale.
The
reliability
of
the
subscales

ranged
between
.74
and
.80,
with
the
test-‐retest
reliability
ranging
between
.68
and
.94

(Ritsher-‐Boyd
et
al.,
2003).
As
per
the
recommendation
of
the
developer
of
the
scale,
we

used
the
items
as
a
single
factor
to
represent
internalized
stigma
instead
of
the
distinct
4

factors.
The
alpha
for
the
single
factor
internalized
stigma
measure
in
the
current
data
was

.90.

Symptom
measure.
Psychiatric
symptoms
were
assessed
using
the
14-‐item

Colorado
Symptom
Index
(CSI,
Shern,
Lee,
&
Coen,
1996)
.
CSI
is
a
self-‐report
measure

assessing
psychological
or
emotional
difficulties
in
the
past
30
days.
It
is
measured
using
a

5-‐point
Likert
scale,
with
higher
ratings
representing
more
difficulty.
The
measure
had
a

reliability
of
.92
for
the
current
data.

Functional
outcome
measure.
The
Strauss
and
Carpenter
Functional
Outcomes

scale
was
used
to
measure
functional
outcomes
(Strauss
&
Carpenter,
1972).
This
measure

has
four
discrete
dimensions,
each
of
which
is
rated
from
0
to
4.
It
measures
functionality

on
the
following
4
dimensions:
duration
of
non-‐institutionalization,
social
contacts,
useful

employment,
and
symptoms.

50

Data
Analysis

Preliminary
analysis.
Data
were
first
subjected
to
preliminary
analyses
to

determine
demographic
characteristics
and
group
differences
between
India
and
the

United
States,
as
well
as
determining
the
reliability
of
the
scales
used
in
the
analyses.

Descriptive
statistics
are
provided
in
the
Results
section.
Differences
between
India
and
the

United
States
on
the
psychosocial
variables
(symptomatology,
psychosocial
functioning,

and
internalized
stigma)
were
also
analyzed
and
are
listed
in
the
Results
section.
Analysis

was
conducted
using
SPSS
version
18
(SPSS
Inc,
2009)

Analysis
of
network
data
and
quantitative
analysis.

AIM
1:
Using
network
analysis
and
Wong
and
Solomon’s
dimensions
of

community
integration,
to
study
and
compare
the
degree
of
community

integration
(including
physical,
social,
and
psychological
integration)
of

individuals
with
severe
mental
illness
in
Indian
and
United
States
samples.

To
address
the
first
aim
of
the
this
study,
a
series
of
independent
sample
t-‐tests

were
run
to
assess
the
differences
between
India
and
the
United
States
for
the
three

dimensions
of
community
integration.
For
physical
community
integration,
scores
on
the

Community
Activities
scale
and
the
Social
Resources
scale
were
compared
between
India

and
the
United
States.
For
psychological
community
integration,
t-‐tests
were
performed
for

the
mental
health
and
non-‐mental
health
versions
of
self-‐perception
of
community

integration
scales,
as
well
as
the
mental
health
and
non-‐mental
health
versions
of
the

Interviewer’s
Perception
of
Community
Integration
scale.
Social
community
integration

was
measured,
first,
by
using
the
social
support
measure
to
assess
differences
in
means
of

social
support
in
India
and
the
United
States
using
a
t-‐test.
To
study
the
second
part
of
the

51

social
community
integration
(social
networks),
data
collected
from
the
network

interviews
were
used
to
generate
descriptive
statistics
of
variables
that
originated
from
the

data.
The
variables
analyzed
included
the
numbers
and
proportions
of
each
type
of
alter

chosen
by
an
individual
(i.e.,
family
members,
friends,
people
from
mental
health
agencies,

service
providers,
etc.),
the
different
types
networks
that
individuals
access
for
social

support
and
other
resources
(i.e.,
proportion
of
friends
that
are
a
source
of
emotional
social

support—tangible
social
support),
and
the
network
affiliates
that
were
a
source
of
stigma

(e.g.,
proportion
of
networks
that
include
family
and
are
also
a
source
of
stigma).
These

variables
were
used
to
compare
the
composition
of
the
networks
in
the
two
countries
for

Aim
2.
The
results
are
presented
in
the
Results
section.

AIM
2:
To
use
quantitative
and
network-‐level
variables
to
understand

community
integration
and
how
the
psychosocial
and
network
variables
are

associated
with
the
three
dimensions
of
community
integration
for

individuals
with
SMI
in
samples
from
India
and
the
United
States.

For
Aim
2,
bivariate
correlations
were
initially
performed
to
examine
relationships

between
the
different
dimensions
of
community
integration
and
individual
demographics,

psychosocial
factors,
and
network
variables.
Coefficients
were
calculated
to
determine

whether
the
study
variables
were
significantly
correlated
and,
thus,
needed
to
be

controlled
for
in
regression
models.
The
statistical
software
package
SPSS
version
18
(SPSS

Inc,
2009)
was
used
for
Aim
2.
A
series
of
nested
regression
models
were
subsequently
run

using
hierarchical
regression
to
examine
the
relative
contribution
of
each
set
of
predictors

for
the
three
dimensions
of
community
integration
as
dependent
variables.
The
correlation

models
were
also
used
as
a
means
to
establish
the
models.
Because
the
sample
size
was

52

small,
we
could
only
include
seven
or
eight
predictors
in
each
model.
Hence,
the
predictors

with
most
significant
relationships
with
the
dependent
variable
at
a
bivariate
level
were

included
in
the
models.
To
be
consistent
across
models
for
the
different
dimensions
of

community
integration,
the
same
set
of
predictors
were
used
across
dimensions.

The
first
set
of
models
aimed
to
study
the
association
between
physical
community

integration
and
the
three
sets
of
demographic,
psychosocial,
and
network
variables.

Physical
community
integration
was
operationalized
using
the
Involvement
in
Community

Activities
scale.
Model
1
included
the
employment
variable;
Model
2
included
the

employment
variable
and
psychosocial
variables,
which
included
symptomatology,

psychosocial
functioning,
and
stigma
levels.
Model
3
included
the
employment
variable,

psychosocial
variables,
and
network
variables,
which
included
proportions
of
family
in
an

individual’s
network,
proportions
of
non-‐mental
health
friends
in
an
individual’s
network,

and
proportions
of
caseworkers
in
an
individual’s
network.
Model
4
included
the

employment
variable,
psychosocial
variables,
network
variables,
and
country
affiliation.

Based
on
the
comparison
in
the
F-‐statistic,
the
model
that
explained
maximum
variation
in

physical
community
integration
was
identified
and
selected.

This
process
was
replicated
for
the
two
forms
of
psychological
community

integration
(into
mental
health
and
non-‐mental
health
communities)
and
social
community

integration
in
the
form
of
perception
of
social
support.

AIM
3:
To
explore
the
use
of
multilevel
analysis
in
examining
the
effects
of

individual-‐
and
network-‐level
variables
on
disclosure
about
mental
illness
in

samples
from
India
and
the
United
States.

53

For
AIM
3,
we
used
multilevel
analysis
to
study
the
network
at
the
individual
and

relational
levels.
Multilevel
modeling
includes
a
random
intercept
for
each
network
of
the

participating
individual
and
their
set
of
relationships
with
their
alters
to
account
for

repeated
observations
of
the
same
individual
(Raudenbush
&
Bryk,
2002;
Snijders
&

Bosker,
1999).
Use
of
multilevel
modeling
accounted
for
variation
because
of
specific

individual-‐alter
(also
known
as
ego-‐alter)
relationships
while
also
accounting
for
the

differences
between
individuals.

For
the
relational-‐level
analysis,
the
individual
and
their
alters
yielded
a
tie.
For

example,
if
an
individual
chose
four
alters,
they
were
considered
as
four
observed

relational
ties
and
not
one
individual.
Therefore,
Level
1
of
the
analysis
was
the
relational

level.
At
this
level,
we
looked
at
the
characteristics
of
the
alters
and
the
characteristics
of

the
relationship
between
the
individuals
and
their
alters.
Level
2
of
the
analysis
was
at
an

individual
level
and
included
the
demographic
characteristics
of
the
egos,
their
network

compositions
and
structures,
psychosocial
variables,
and
levels
of
perception
of
community

integration
into
mental
health
and
non-‐mental
health
communities,
as
well
as
the
impact
of

these
variables
on
stigma
associated
with
disclosure
about
mental
illness.
Data
were

analyzed
using
Stata
version
13
(StataCorp,
2013)
.
Looking
at
the
individual
and
network-‐
level
data
simultaneously
provided
a
more
comprehensive
understanding
of
the

association
between
community
integration
and
stigma.

Because
the
outcome
variable
was
a
dichotomous
variable
on
whether
or
not
an

individual
had
told
or
would
like
to
tell
their
alters
about
their
mental
illness
(0
=
have
told

or
would
not
mind
telling;
1
=
have
not
told/
would
not
want
to
tell),
the
multilevel
models

were
run
as
logit
models.

54

Dependent
variable.
The
variable
addressing
stigma
about
disclosure
was
derived

from
one
item
that
asked
the
individual
to
identify
people
in
their
networks
to
whom
they

would
not
wish
tell
they
have
a
mental
illness.
A
follow-‐up
question
asked
the
respondents

to
list
individuals
who
already
knew
about
their
mental
illness
but
wished
did
not
know.

Using
a
combination
of
the
information
obtained
from
the
one
network
item
and
follow-‐up

question,
a
dichotomous
variable
on
stigma
about
disclosure
was
created.
Responses
were

dichotomized
so
a
value
of
1
represented
individuals
to
whom
the
respondents
wished

they
had
not
disclosed
or
would
not
wish
to
disclose
their
mental
illness
and
a
value
of
0

represented
individuals
to
whom
the
respondents
had
no
problem
disclosing
their
mental

illness.

Level
1
predictors.

1. Length
of
association:
This
was
a
continuous
variable
that
included

information
on
how
long
(in
years)
the
individual
had
known
each
of
their

alters.

2. Knew
before
mental
health
services:
This
variable
provided
information

about
which
alters
the
individual
knew
before
starting
mental
health

treatment.
Responses
were
dichotomized
so
a
value
of
1
indicated
alters
the

individuals
knew
before
starting
mental
health
services
and
a
value
of
0

indicated
alters
the
individuals
did
not
know
before
starting
mental
health

services.

3. Relationship:
The
relationship
variable
was
converted
to
six
dummy

variables.
Family
was
chosen
as
the
reference
group.
The
other
five
dummy

variables
and
their
dichotomized
values
were
categorized
as
follows:

55

a. Friend:
A
value
of
1
indicated
the
alter
was
identified
as
a
friend
and
a

value
of
0
indicated
the
alter
was
not
identified
as
a
friend.

b. Neighbor:
A
value
of
1
indicated
the
alter
was
identified
as
a
neighbor
and

a
value
of
0
indicated
the
alter
was
not
identified
as
a
neighbor.

c. Colleague:
A
value
of
1
indicated
the
alter
was
identified
as
a
colleague

and
a
value
of
0
indicated
the
alter
was
not
identified
as
a
colleague.

d. Caseworker:
A
value
of
1
indicated
the
alter
was
identified
as
a

caseworker
and
a
value
of
0
indicated
the
alter
was
not
identified
as
a

caseworker.

e. Acquaintance:
A
value
of
1
indicated
the
alter
was
identified
as
an

acquaintance
and
a
value
of
0
indicated
the
alter
was
not
identified
as
an

acquaintance.

Level
2
predictors.

1. Country
affiliation:
This
dichotomized
variable
indicated
whether
the
individual

was
from
the
India
sample
or
the
United
States.
The
variable
was
dichotomized

so
a
value
of
indicated
the
individual
was
from
the
United
States
and
a
value
of
0

indicated
the
individual
was
from
India.

2. Employed:
This
dichotomized
variable
indicated
whether
an
individual
was

employed.
A
value
of
1
indicated
employment
and
a
value
of
0
indicated
no

employment.

3. Symptom:
Psychiatric
symptoms
were
assessed
using
a
sum
of
the
14-‐item
CSI

(Shern
et
al.,
1996).
It
was
a
continuous
variable
whereby
an
increase
in
value

indicated
an
increase
in
symptomatology.

56

4. Psychosocial
functioning:
The
Strauss
and
Carpenter
Functional
Outcomes
scale

was
used
to
measure
functional
outcomes
(Strauss
&
Carpenter,
1972).
It
was
a

continuous
variable
whereby
an
increase
in
value
indicated
an
increase
in

functioning.

5. Internalized
stigma:
The
ISMI
was
used
to
measure
the
internalized
stigma
of
the

consumers
(Ritsher-‐Boyd
et
al.,
2003).
It
was
a
continuous
variable
whereby
an

increase
in
value
indicated
an
increase
in
internalized
stigma.

6. Perceived
social
support:
Perceived
availability
of
social
support
was
measured

with
the
4-‐item
short
version
of
the
MOSSS
(Sherbourne
&
Stewart,
1991).

7. Psychological
community
integration
in
a
mental
health
community
and

psychological
community
integration
in
a
non-‐mental
health
community:
Two

versions
of
the
10-‐item
CIM
(McColl
et
al.,
2001)
were
used
to
measure

community
integration
in
mental
health
and
non-‐mental
health
communities.

Both
were
continuous
variables,
where
an
increase
in
value
indicated
an

increase
in
psychological
integration
in
that
particular
community.

Data
were
analyzed
using
the
xtmelogit
model
in
Stata
version
13
(StataCorp,
2013).

Wald
tests
and
z-‐tests
were
used
to
determine
the
significance
of
obtained
odds

ratios
(OR).

57

CHAPTER
FOUR:

RESULTS

Description
of
the
Sample

Of
the
86
individuals
originally
enrolled
in
the
study,
30
participants
from
the
low-‐
intensity
service
group
in
the
United
States
and
26
from
the
low-‐intensity
service
group
in

India
were
selected
for
this
analysis,
making
the
total
sample
size
56.
Table
1
provides
an

overview
of
the
demographic
characteristics
of
the
U.S.
and
Indian
samples.
The
U.S.

sample
had
an
average
age
of
46
years
(SD
=
9.62),
and
the
Indian
sample
had
an
average

age
of
39
years
(SD
=
13.38).
The
U.S.
sample
was
predominantly
female
compared
with
the

Indian
sample
(63%
vs.
42%,
respectively).
The
U.S.
sample
chiefly
consisted
of
ethnic

minorities
with
representations
of
33%
Euro-‐Americans,
53%
Latinos,
3%
African

American,
3%
Asian,
and
approximately
7%
mixed
ethnicity.
The
Indian
sample
was

ethnically
homogeneous.

Most
of
the
Indian
sample
(65%)
was
married
or
with
a
partner

compared
to
23%
of
the
U.S.
sample,
whereas
40%
of
the
U.S.
sample
had
never
been

married
compared
to
23%
of
the
Indian
sample.
In
the
U.S.
sample,
33%
was
divorced
or

separated
compared
to
12%
of
the
Indian
sample,
and
only
the
U.S.
sample
had
a

participant
(3%)
who
was
widowed.
In
terms
of
employment,
more
than
half
of
the
Indian

sample
was
employed
(54%
employed
vs.
46%
unemployed),
whereas
only
one
person
in

the
U.S.
sample
was
employed.
While
both
the
United
States
and
Indian
samples
included

individuals
who
were
diagnosed
with
a
SMI,
approximately
two-‐thirds
of
the
U.S.
sample

was
diagnosed
with
bipolar
disorder,
and
approximately
one-‐third
had
a
diagnosis
of

schizophrenia
spectrum
disorder.
46%
of
the
Indian
sample
had
a
diagnosis
of

58

schizophrenia
spectrum
disorder,
39%
had
a
diagnosis
of
bipolar
disorder
and
15%
had
a

diagnosis
of
major
depression.

Table
1

Demographic
Characteristics
of
the
Whole
Sample
(N

=
56)

United
States
(N

=
30)
India
(N

=
26)

Mean
(SD)
or
N
(%)
Mean
(SD)
or
N
(%)

Age
(in
years)
46.030
(9.615)
38.690
(13.377)

Gender

Male
11
(36.7)
15
(57.7)

Female
19
(63.3)
11
(42.3)

Ethnicity

Euro-‐American
10
(33.3)
-‐

African
American
1
(3.3)
-‐

Latino
16
(53.3)
-‐

Asian/Pacific
Islanders
1
(3.3)
-‐

Mixed
Race
2
(6.7)
-‐

Indian
-‐
26
(100)

Relationship
status

Married/living
with
partner
7
(23.3)
17
(65.4)

Never
married
12
(40)
6
(23.1)

Divorced/separated
10
(33.3)
3
(11.5)

Widowed
1
(3.3)
-‐

Employment
status

Employed
1
(3.3)
14
(53.8)

Not
employed
29
(96.7)
12
(46.2)

Diagnosis

Schizophrenia
spectrum
disorder
9
(30)
12
(46.2)

Bipolar
disorder
21
(70)
10
(38.5)

Major
depression
-‐
4
(15.4)

59

In
the
second
step
of
the
analysis,
independent
sample
t-‐tests
were
performed
to

test
whether
the
two
samples
were
similar
in
terms
of
their
psychosocial
characteristics.

Table
2
describes
the
psychosocial
characteristics
of
the
Indian
and
U.S.
samples
in
terms
of

their
levels
of
symptomatology,
psychosocial
functioning,
and
internalized
stigma.
The
two

groups
were
found
to
be
similar
in
terms
of
their
symptomatology,
as
measured
by
both

the
distress
due
to
symptoms
scale
(Shern
et
al.,
1996)
(t
=
.82;
p
=
.41)
and
as
a
sub

dimension
of
the
functioning
measure
(t
=
1.10;
p
=
.28).
The
two
groups
were
similar
in

terms
of
their
overall
psychosocial
functioning
but
were
found
to
be
different
in
in
terms
of

number
of
hospitalizations,
number
of
social
contacts,
and
vocational
functioning.
While

the
U.S.
sample
was
higher
functioning
in
terms
of
number
of
hospitalizations
(t
=
2.72;
p
=

.01)
and
social
contacts
(t
=
2.56;
p
=
.01),
the
Indian
sample
was
found
to
be
higher
in

vocational
functioning
(t
=
-‐4.08;
p
=
.00).
Yet,
the
Indian
sample
was
found
to
have
higher

levels
of
internalized
stigma
(t
=
-‐2.58;
p
=
.01).

60

Table
2

Psychosocial
Characteristics
of
Samples
from
India
and
United
States
of

Individuals
with
SMI

United
States
(N

=
30)
India
(N
=
26)

Variable
Mean
SD
Mean
SD
t-‐value

Symptomatology

a

36.170
13.123
33.000
15.383
.825

Psychosocial
functioning

b

10.370
2.008
10.580
3.384
-‐.277

Institutionalized
3.970
.183
3.650
.562
2.719**

Social
contacts
3.530
.819
2.730
1.402
2.565**

Employment
.430
1.135
2.040
1.777
-‐3.958***

Absence
of
symptoms
2.430
1.006
2.150
.881
1.098

Internalized
stigma

c

18.170
8.465
24.500
9.925
-‐2.578**

*p
<
.1;
**
p
<
.05;
***
p
<
.01

a

Measured
by
Colorado
Symptom
Inventory
(Shern
et
al.,
1996).

b

Measured
by
Straus
and
Carpenter
Functional
Outcome
Scale
(Strauss
&
Carpenter,
1972).

c

Measured
by
Internalized
Stigma
of
Mental
Illness
scale
(Ritsher-‐Boyd
et
al.,
2003).

61

Results
for
Research
Aim
1:
Physical,
social,
and
psychological
community

integration
of
individuals
with
SMI
in
India
and
the
United
States.

Aim
1
examined
and
compared
the
degree
of
community
integration
of
individuals

with
SMI
in
India
and
the
United
States.
It
was
hypothesized
that
there
would
be
country-‐
specific
differences
in
terms
of
the
three
dimensions
of
community
integration:
physical,

social,
and
psychological.
It
was
further
hypothesized
that
the
two
countries
would
be

different
in
their
network
structures.

Hypothesis
1a:
Samples
of
individuals
with
SMI
in
India
and
the
United
States
will

differ
in
terms
of
different
community
integration
dimensions.
Specifically,

individuals
from
India
will
be
more
integrated
in
the
non-‐mental
health
community

compared
with
individuals
from
the
United
States.

To
assess
the
differences
between
India
and
the
United
States
in
terms
of
the

community
integration
dimensions,
a
series
of
independent
sample
t-‐tests
were
performed.

Table
3
summarizes
the
characteristics
of
the
physical,
social,
and
psychological
measures

of
community
integration
for
the
two
countries.
Concerning
physical
integration,
there

were
no
significant
differences
between
the
U.S.
and
Indian
samples
in
terms
of
number
of

community
activities
the
individuals
participated
in
during
the
previous
30
days
(3.67
vs.

3.38;
p
=
.62).
In
terms
of
satisfaction
with
community
activities,
both
samples
were

“somewhat
satisfied”
with
their
level
of
community
activities
(p
=
.69).
In
terms
of
their

social
resources,
there
was
no
significant
difference
between
the
two
groups
in
terms
of

their
level
of
total
social
resources
(t
=
1.21;
p
=
.23)
and
social
resources
from
the
non-‐
mental
health
community
(t
=
.81;
p
=
.42).
However,
the
U.S.
sample
was
found
to
have

62

significantly
more
social
resources
from
the
mental
health
community
compared
with
the

Indian
sample
(t
=
5.39;
p
=
.00).

In
terms
of
social
community
integration,
the
participants
from
India
had
more

perceived
social
support
compared
with
the
U.S.
sample
(t
=
-‐1.615;
p
=
.056).

Regarding
psychological
integration,
on
the
self-‐rated
scale
of
community

integration,
the
U.S.
group
felt
significantly
more
integrated
into
the
mental
health

community
than
the
Indian
sample
(t
=
5.03;
p
=
.00),
but
the
two
samples
did
not
differ

significantly
in
terms
of
their
perception
of
community
integration
into
the
non-‐mental

health
community
(t
=
-‐.68;
p
=
.25).
However,
in
terms
of
magnitude,
the
Indian
sample

seemed
to
be
more
integrated
in
the
non-‐mental
health
community,
but
this
difference
was

not
statistically
significant.
This
difference
between
the
U.S.
and
Indian
groups
was
more

pronounced
in
terms
of
the
interviewer’s
perception
of
an
individual’s
integration
into
the

mental
health
community,
whereby
the
interviewer
rated
the
Indian
sample
to
be
more

integrated
into
the
non-‐mental
health
community
compared
with
the
U.S.
sample
(t
=
-‐2.37;

p
=
.01),
and,
conversely,
the
U.S.
sample
was
perceived
as
being
more
integrated
into
the

mental
health
community
(t
=
1.71;
p
=
.045).

63

Table
3

Comparison
between
the
United
States
and
India
regarding
Physical,
Social,
and

Psychological
Community
Integration
Characteristics

United
States
(N
=
30)
India
(N
=
26)
t-‐value

Mean
(SD)
Mean
(SD)

Physical
community
integration

Community
activities

No.
of
community
activities

a

3.670
(1.845)
3.380
(2.334)
.505

Satisfaction
with
level
of
activity
1.900
(1.185)
2.040
(1.428)
-‐.397

Social
resources

Total
social
resources

b

35.267
(17.865)
29.231
(19.574)
1.206

From
mental
health
community

b

2.767
(3.059)
.615
(1.098)
3.594***

From
non-‐mental
health
community

b

32.500
(16.598)
28.615
(19.114)
.814

Psychological
community
integration

Self-‐perception
of
community
integration

c

Mental
health
community
39.633
(7.346)
30.038
(6.856)
5.027***

Non-‐mental
health
community

40.500
(8.072)
41.846
(6.398)
-‐.684

Interviewer
perception
of
community
integration

c

Mental
health
community

6.270
(1.929)
5.350
(2.097)
1.711**

Non-‐mental
health
community
8.570
(2.897)
10.310
(2.558)
-‐2.367***

Social
community
integration

Social
support

d

14.130
(4.117)
15.620
(2.684)
-‐1.615*

*p
<
.1;
**p
<
.05;
***p
<
.01
on
two-‐tailed
tests
for
significance
for
physical
community
integration
and
one-‐tailed
tests
for
significance

for
psychological
and
social
integration.

a

Measured
by
Involvement
in
Community
Activities
Scale
adapted
from
Living
Skills
Scale
(Wallace
et
al.,
2000).

b

Measured
by
using
an
adaptation
of
the
social
capital
resource
generator
(Van
Der
Gaag
&
Webber,
2008).

c

Measured
by
Community
Integration
Measure
(CIM;
(McColl
et
al.,
2001)

d

Measured
by
the
4-‐item
short
version
of
the
Medical
Outcomes
Study
Social
Support
Survey
(MOSSS;
(Sherbourne
&
Stewart,
1991)

64

Social
network
differences
in
individuals
with
SMI
in
India
and
United
States.

Hypothesis
1b:
Samples
of
individuals
with
SMI
in
India
and
the
United
States
will

differ
in
terms
of
their
social
network
structures
and
the
networks
from
which
they

access
social
support
and
social
resources.
Specifically,
individuals
from
India
will

have
more
non-‐mental
health
community-‐based
networks
compared
with

individuals
from
the
United
States.

When
examining
social
integration
in
terms
of
the
social
network
characteristics
of

the
two
counties,
there
were
no
significant
differences
in
network
size
between
the
U.S.
and

Indian
samples
(15.90
vs.
15.65;
p
=
.11).
Both
groups
had,
on
average,
a
higher
proportion

of
non-‐mental
health
connections
compared
with
mental
health
connections
(85%
non-‐
mental
health
in
the
United
States
and
88%
non-‐mental
health
in
India).
The
U.S.
group
had

almost
one-‐third
of
their
network
listed
as
friends,
which
was
significantly
more
than
the

India
group
(30%
vs.
8%;
p
=
.00),
and
almost
half
listed
as
family.
Within
the
Indian
group,

two-‐thirds
of
their
network
was
listed
as
family.
The
Indian
sample
also
showed
a

significantly
higher
proportion
of
colleagues
compared
with
the
U.S.
sample
(<1%
vs.
4%;
p

=
.012),
whereas
the
U.S.
sample
had
a
higher
proportion
of
caseworkers
(14%
vs.
11%;
p
=

.068).
Contrary
to
the
hypothesis,
the
Indian
sample
contained
a
higher
proportion
of

acquaintances
from
the
mental
health
community
compared
with
the
U.S.
sample
(>1%
vs.

2%;
p
=
.085),
and
the
U.S.
sample
had
a
higher
proportion
of
non-‐mental
health

acquaintances
compared
with
the
Indian
sample
(2%
vs.

<1%;
p

=
.068).
The
differences

between
the
United
States
and
India
in
terms
of
their
network
characteristics
are
shown
in

Table
4.

65

Table
4

Comparison
between
the
United
States
and
India
In
Terms
of
Their
Network
Characteristics

United
States
India
t-‐value

Mean
(SD)
Mean
(SD)

Social
network
characteristics

Network
size
15.900
(9.689)
15.650
(6.112)
.1.12

Proportion
of
network
in
MH
community
.148
(.075)
.125
(.066)
.1.196

Proportion
of
network
in
NMH
community
.852
(.075)
.875
(.066)
-‐1.196

Proportion
of
network
that
are
family

members

.480
(.192)
.690
(.196)
-‐4.031***

Proportion
of
network
that
are
friends
.300
(.200)
.080
(.114)
5.169***

Proportion
of
NMH
friends
.296
(.189)
.078
(.116)
5.284***

Proportion
of
MH
friends
.007
(.026)
.004
(.014)
.504

Proportion
of
case
workers
.140
(.075)
.112
(.060)
1.513*

Proportion
of
neighbors
.049
(.107)
.057
(.113)
-‐.273

Proportion
of
colleagues
.006
(0.023)
.043
(.078)
-‐2.360**

Proportion
of
NMH
others
.019
(.039)
.007
(.020)
1.514*

Proportions
of
MH
others
.001
(.005)
.008
(.025)
-‐1.405*

Note.
MH
=
mental
health;
NMH
=
non-‐mental
health.

*p
<
.1;
**p
<
.05;
***p
<
.01
on
a
one-‐tailed
test
for
significance
.

66

As
a
next
step,
we
used
the
network
data
to
understand
the
sources
from
which

individuals
from
the
two
countries
differed
in
terms
of
from
where
they
received
their

social
support.
Tables
5
and
6
provide
the
differences
in
social
networks
that
were
sources

of
emotional-‐informational
and
positive
social
interaction
forms
of
social
support.
To

collect
information
on
the
emotional-‐informational
social
support,
participants
were
asked

to
look
at
their
respective
network
maps
and
nominate
individuals
who
they
shared

personal
problems
with.
Results
listed
in
Table
5
show
that
participants
from
the
United

States
identified
26%
of
their
network
as
sources
of
emotional
social
support
compared
to

the
17%
who
Indian
participants
identified
as
sources
of
emotional
social
support
(t
=
2.29;

p
=
.013).
The
U.S.
participants
reported
receiving
more
emotional
and
informational
social

support
from
the
mental
health
community
compared
with
the
Indian
responders
(4%
vs.

1%;
p
=
.022)
and
identified
a
higher
percentage
of
friends
as
their
sources
of
emotional

support
compared
with
the
Indian
group
(21%
vs.
7%;
p
=
.000).
The
U.S.
participants
also

nominated
a
higher
percentage
of
caseworkers
as
those
with
whom
they
shared
personal

problems
compared
with
the
Indian
responders
(4%
vs.
1%;
p
=
.027).

As
noted
in
Table
6,
the
U.S.
participants
identified
a
higher
percentage
of
their

network
from
which
they
accessed
positive
social
interactional
support
(27%
vs.
15%;
p
=

.003).
Participants
from
the
United
States
accessed
more
social
interactional
social
support

from
their
non-‐mental
health
network
members
compared
with
the
Indian
responders

(27%
vs.
15%;
p
=
.004)
and
a
higher
proportion
of
friends
from
whom
they
accessed

interactional
social
support
(22%
vs.
9%;
p
=
.002).

67

Table
5

Social
Support
Network
Characteristics
(Emotional-‐Informational
Social
Support:

Sharing
Personal
Problems)

United
States
India
t-‐value

Mean
(SD)
Mean
(SD)

Proportion
of
total
network

.266
(.143)
.172
(.164)
2.292**

Proportion
of
MH
network

.040
(.083)
.007
(.024)
2.093**

Proportion
of
NMH
network

.226
(.133)
.164
(.160)
1.560

Proportion
of
family
.146
(.113)
.150
(.159)
-‐.130

Proportion
of
friends

.214
(.159)
.070
(.075)
4.412***

Proportion
of
NMH
friends

.077
(.092)
.006
(.016)
4.152***

Proportion
of
MH
friends
.003
(.016)
0
.930

Proportion
of
caseworkers

.037
(.079)
.007
(.024)
1.984**

Proportion
of
neighbors
.003
(.016)
.008
(.031)
-‐.835

Note.
MH
=
mental
health;
NMH
=
non-‐mental
health.

*p
<
.1;
**p
<
.05;
***p
<
.01
on
a
one-‐tailed
test
for
significance.

68

Table
6

Social
Support
Network
Characteristics
(Positive
Social
Interaction:
Do
Something

Enjoyable)

United
States
India
t-‐value

Mean
(SD)
Mean
(SD)

Proportion
of
total
network
.271
(.182)
.146
(.143)
2.810***

Proportion
of
MH
network

.003
(.016)
0
.930

Proportion
of
NMH
network

.268
(.181)
.146
(.143)
2.750***

Proportion
of
family
.173
(.154)
.128
(.148)
1.116

Proportion
of
friends

.222
(.202)
.086
(.126)
2.959***

Proportion
of
NMH
friends

.095
(.156)
.015
(.040)
2.702***

Proportion
of
MH
friends
.003
(.016)
0
.930

Note.
MH
=
mental
health;
NMH
=
non-‐mental
health.

*p
<
.1;
**p
<
.05;
***p
<
.01
on
a
one-‐tailed
test
for
significance.

69

When
examining
social
integration
in
terms
of
social
resources
network

characteristics,
we
tested
four
kinds
of
social
resources,
as
identified
by
the
developers
of

the
social
capital
resource
generator
(Van
Der
Gaag
&
Webber,
2008).
Table
7
shows
the

country
differences
in
the
network
characteristics
of
the
domestic
form
of
social
resources

accessed
by
the
individuals
from
their
networks.
The
results
show
that
Indian
participants

had,
on
average,
a
higher
proportion
of
individuals
in
their
networks
from
whom
they

accessed
the
domestic
type
of
social
resources
(in
the
form
of
help
in
finding
bargains)

(28%
vs.
16%;
p
=
.045).
Participants
from
India
also
had
a
higher
proportion
of
family

members
from
whom
they
accessed
the
domestic
type
of
social
resources
(25%
vs.
16%;
p

=
.012).
The
U.S.
respondents
had
a
higher
proportion
of
individuals
in
their
networks
who

were
identified
as
non-‐mental
health
friends
from
whom
they
accessed
the
domestic
type

of
social
resources
(6%
vs.
1%;
p
=
.004).

70

Table
7

Social
Resources
Network
Characteristics:
Domestic
Resources
Type
(Finding

Bargains)

United
States
India
t-‐value

Mean
(SD)
Mean
(SD)

Proportion
of
total
network
.163
(.094)
.282
(.340)
-‐1.734**

Proportion
of
MH
network

0
.020
(.046)
-‐2.204**

Proportion
of
NMH
network

.163
(.094)
.263
(.307)
-‐1.591

Proportion
of
family
.108
(.085)
.253
(.299)
-‐2.394**

Proportion
of
friends

.135
(.108)
.103
(.208)
.575

Proportion
of
NMH
friends

.056
(.085)
.010
(.032)
2.736***

Proportion
of
caseworkers
0
.020
(.046)
-‐2.204**

Note.
MH
=
mental
health;
NMH
=
non-‐mental
health.

*p
<
.1;
**p
<
.05;
***p
<
.01
on
a
one-‐tailed
test
for
significance.

71

To
assess
the
differences
between
the
two
countries
in
terms
of
the
expert
advice

form
of
social
resources,
participants
were
asked
to
look
at
their
respective
network
maps

and
nominate
individuals
who
gave
them
advice
about
earning
money.
The
results
in
Table

8
show
that
participants
from
the
United
States
nominated
more
individuals
from
their

network
from
whom
they
accessed
expert
advice
(14%
vs.
10%;
p
=
.063).
Participants

from
the
United
States
also
identified
10%
of
their
friends
and
4%
of
their
non-‐mental

health
friends
from
whom
they
accessed
expert
advice,
which
was
significantly
different

from
the
Indian
sample
(5%
of
friends
and

<1%
of
non-‐mental
health
friends;
p
<
.05).

To
attain
information
on
the
personal
skills
type
of
social
resources,
participants

were
asked
to
look
at
their
respective
network
maps
and
nominate
individuals
who
helped

them
take
care
of
their
health.
The
results
indicated
in
Table
9
show
that
whereas
there

was
no
difference
between
India
and
the
United
States
in
terms
of
total
network
from

which
they
accessed
personal
skills,
participants
from
the
United
States
nominated
4%
of

their
nominees
from
the
mental
health
community
as
individuals
who
would
help
them

take
care
of
their
health
(as
compared
to
<1%
of
the
Indian
respondents;
p
=
.002).

Participants
from
the
United
States
also
nominated
a
higher
percentage
of
caseworkers

(3%
vs.
<1%;
p
=
.005)
and
proportion
of
friends
(16%
vs.
10%;
p
=
.045)
compared
with

the
Indian
responders
in
this
social
resource.
However,
compared
with
the
U.S.

participants,
Indian
participants
had
a
higher
percentage
of
family
members
who
helped

them
take
care
of
their
health
(24%
vs.
14%;
p
=
.026).

72

Table
8

Social
Resources
Network
Characteristics
(Expert
Advice
Type:
Advice
About
Earning

Money)

United
States
India
t-‐value

Mean
(SD)
Mean
(SD)

Proportion
of
total
network
.142(.102)
.107(.064)
1.554*

Proportion
of
MH
network

.006(.024)
0
1.404*

Proportion
of
NMH
network

.136(.098)
.107(.064)
1.321

Proportion
of
family
.095(.088)
.102(.070)
-‐.301

Proportion
of
friends

.107(.103)
.057(.061)
2.222**

Proportion
of
NMH
friends

.039(.073)
.006(.021)
2.369**

Proportion
of
case
workers
.006(.024)
0
1.404*

Note.
MH
=
mental
health;
NMH
=
non-‐mental
health.

*p
<
.1;
**p
<
.05;
***p
<
.01
on
a
one-‐tailed
test
for
significance.

73

Table
9

Social
Resources
Network
Characteristics
(Personal
Skills
Type:
Taking
Care
of
Health)

United
States
India
t-‐value

Mean
(SD)
Mean
(SD)

Proportion
of
total
network
.210(.140)
.245(.228)
-‐.705

Proportion
of
MH
network

.036(.055)
.004(.016)
2.998***

Proportion
of
NMH
network

.174(.127)
.241(.219)
-‐1.422*

Proportion
of
family
.144(.111)
.239(.219)
-‐2.005**

Proportion
of
friends

.156(.140)
.097(.116)
1.696**

Proportion
of
NMH
friends

.025(.077)
.002(.009)
1.639*

Proportion
of
MH
friends
.003(.016)
0
.930

Proportion
of
case
workers
.033(.055)
.004(.016)
2.740***

Note.
MH
=
mental
health;
NMH
=
non-‐mental
health.

*p
<
.1;
**p
<
.05;
***p
<
.01
on
a
one-‐tailed
test
for
significance.

74

To
assess
differences
between
the
two
countries
in
terms
of
the
problem-‐solving

type
of
social
resources,
participants
were
asked
to
look
at
their
respective
network
maps

and
nominate
individuals
who
helped
them
in
fixing
things
around
the
house.
As
shown
in

Table
10,
individuals
from
the
United
States
nominated
a
higher
proportion
of
their
total

network
that
could
help
them
fix
things
around
the
house
as
compared
to
8%
nominated

by
the
India
participants
(p
=
.012).
Compared
with
the
Indian
participants,
the
U.S.

participants
also
nominated
twice
as
many
individuals
from
their
non-‐mental
health

network
that
could
provide
them
with
the
problem-‐solving
type
of
social
resources
(p
=

.012).
Moreover,
they
selected
approximately
12%
of
their
friends
who
could
help
them
fix

things
around
their
house
as
compared
to
˂3%
nominated
by
the
Indian
participants
(p
=

.00).

75

Table
10

Social
Resources
Network
Characteristics
(Problem-‐Solving
Type:
Fixing
Things
around
the

House)

United
States
India
t-‐value

Mean
(SD)
Mean
(SD)

Proportion
of
total
network
.138(.105)
.078(.086)
2.336***

Proportion
of
NMH
network

.138(.105)
.078(.086)
2.336***

Proportion
of
family
.097(.087)
.078(.086)
.812

Proportion
of
friends

.115(.114)
.028(.034)
3.997***

Proportion
of
NMH
friends

.033(.068)
0
2.687***

Note.
NMH
=
non-‐mental
health.

*p
<
.1;
**p
<
.05;
***p
<
.01
on
a
one-‐tailed
test
for
significance.

76

Table
11
lists
the
differences
between
the
United
States
and
India
in
terms
of
stigma

associated
with
disclosure
of
mental
illness.
The
Indian
sample
had
a
higher
proportion
of

their
social
network
to
whom
they
would
not
like
to
disclose
their
mental
illness
(11%
vs.

25%;
p
=
.033).
The
Indian
sample
also
had
a
higher
proportion
of
non-‐mental
health

network
individuals
to
whom
they
did
not
want
to
disclose
their
mental
illness
(11%
vs.

25%;
p
=
.035).
Indian
responders
also
indicated
stigma
related
to
disclosure
from
slightly

more
than
16%
of
their
family
members
(as
compared
to
only
4%
indicated
by
the
U.S.

responders;
p
=
.023).
Furthermore,
the
Indian
sample
had
a
higher
percentage
of
their

network
who
were
colleagues
to
whom
they
did
not
want
to
disclose
their
mental
illness

(>1%
vs.
3%;
p
=
.021)

77

Table
11

Stigma
Network
Characteristics
(Not
Wish
to
Tell
or
Wish
the
Network
Members

Did
Not
Know
about
Their
Mental
Illness)

United
States
India
t-‐value

Mean
(SD)
Mean
(SD)

Proportion
of
network
.114
(.212)
.254
(.340)
-‐1.813**

Proportion
MH
network
.004
(.020)
.009
(.028)
-‐.822

Proportion
NMH
network
.110
(.203)
.245
(.320)
-‐1.845**

Proportion
family

.042
(.094)
.163
(.285)
-‐2.077**

Proportion
friends

.098
(.212)
.159
(.267)
-‐.957

Proportion
of
NMH
friends

.044
(.098)
.031
(.091)
.508

Proportion
of
caseworkers

.004
(.020)
.009
(.028)
-‐.822

Proportion
of
neighbors
.017
(.062)
.018
(.055)
-‐.017

Proportion
of
colleagues
.004
(.021)
.029
(.057)
-‐2.106**

Note.
MH
=
mental
health;
NMH
=
non-‐mental
health.

*p
<
.1;
**p
<
.05;
***p
<
.01
on
a
one-‐tailed
test
for
significance.

78

Results
for
Research
Aim
2:
Psychosocial
and
network
variables
associated

with
the
three
dimensions
of
community
integration
for
individuals
with
SMI
in
India

and
the
United
States

Aim
2
explored
the
association
of
various
psychosocial
and
network
variables
with

the
three
dimensions
of
community
integration.

Table
12
shows
the
correlation
values
for
the
dependent
and
independent
variables

in
the
regression
models.
Bivariate
correlations
showed
significant
relationships
between

the
dependent
variables
and
various
predictors
in
the
models.
For
the
physical
community

integration,
community
activities
were
significantly
associated
with
employment
status
(r

=
.29;
p
<
0.05),
psychosocial
functioning
(r
=
.40;
p
<
0.01),
internalized
stigma
(r
=
-‐.27;
p

<
.05),
proportion
of
mental
health
network
(r
=
-‐.39;
p
<
0.01),
proportion
of
family
(r
=
-‐
.26;
p
<
.10),
and
proportion
of
non-‐mental
health
friends
(r
=
.29;
p
<
0.05).
Social

integration,
measured
by
the
perceived
availability
of
social
support,
was
significantly

associated
with
symptomatology
(r
=
-‐.42;
p
<
.01)
and
psychosocial
functioning
(
r
=
-‐.30;
p

<
.05).
For
psychological
integration,
community
integration
in
a
mental
health
community

was
significantly
associated
with
marital
status
(r
=
-‐.34;
p
<
.01),
symptomatology
(r
=
-‐.25;

p
<
.10),
psychosocial
functioning
(r
=
.33;
p
<
.05),
internalized
stigma
(r
=
-‐.50;
p
<
0.01),

proportion
of
family
(r
=
-‐.44;
p
<
0.01),
and
proportion
of
non-‐mental
health
friends
(r
=

.49;
p
<
0.01).
Community
integration
into
a
non-‐mental
health
community
was

significantly
associated
with
employment
status
(r
=
.31;
p
<
0.05),
symptomatology
(r
=
-‐
.32;
p
<
0.05),
psychosocial
functioning
(r
=
.38;
p
<
.01),
and
proportion
of
friends
from
the

mental
health
community
(r
=
-‐.41;
p
<
0.01).

79

Table
12

Correlation
between
Variables
Used
in
the
Regression
Models

Variable
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17

1.
NMH
Community
Integ

a

1

2.
MH
Community
Integ

a

.297**

3.
Symptoms

b

-‐.324**
-‐.254*
1

4.
Psychosocial
functioning

c

.380***
.329**
-‐.501***
1

5.
Internalized
stigma

d

-‐.206
-‐.496***
.372***
-‐.256*
1

6.
Social
support

e

.258*
.136
-‐.422***
.125
-‐.297**
1

7.
Community
activities

f

.008
.298**
-‐.128
.405***
-‐.274**
.102
1

8.
MH
Comm
Integ
interviewer

g

.044
.372***
.175
.162
-‐.242*
-‐.099
.304**
1

9.
NMH
Comm
Integ
interviewer

g

.399***
.066
-‐.308**
.411***
-‐.188
.411***
.457***
.179
1

10.
Country
affiliation
-‐.093
.565***
.113
-‐.039
-‐.331**
-‐.209
.069
.227*
-‐.307**
1

11.
Total
social
resources

h

.010
.264**
-‐.049
.218
-‐.074
.185
.386***
.048
.295**
.162
1

12.
Employment
.307**
-‐.184
-‐.227*
.451***
.112
.167
.294**
.28
.390***
-‐.569***
.106.
1

13.
Proportion
of
caseworkers
-‐.074
.179
.124
-‐.249*
.011
-‐.048
-‐.386***
-‐.149
-‐.439***
.202
-‐.233*
-‐.245*
1

14.
Proportion
of
family
.144
-‐.439***
.160
-‐.282**
.460***
.101
-‐.260*
-‐.211
-‐.032
-‐.481***
-‐.125
.210
-‐.189
1

15.
Proportion
of
friends
-‐.195
.481***
-‐.119
.209
-‐.385***
.058
.293**
.260*
.121
.561***
.243*
-‐.293**
.012
-‐.794***
1

16.
Proportion
of
NMH
friend
-‐.156
.492***
-‐.124
.228*
-‐.379***
.045
.286**
.272**
.119
.571***
.251*
-‐.285**
.026
-‐.781***
.995***
1

17.
Proportion
of
MH
friend
-‐.412***
.040
.012
-‐.116
-‐.163
.133
.152
-‐.037
.056
.068
-‐.004
-‐.158
-‐.121
-‐.348***
.334**
.235*
1

Note.
MH
=
mental
health;
NMH
=
non-‐mental
health.

*p
<
.1;
**p
<
.05;
***p
<
.01
on
a
two-‐tailed
test
of
significance.

a

Measured
by
Community
Integration
Measure
(CIM;
(McColl
et
al.,
2001).

b

Measured
by
Colorado
Symptom
Inventory
(Shern
et
al.,
1996).

c

Measured
by
Straus
and
Carpenter
Functional
Outcome
scale
(Strauss
&
Carpenter,
1972).

d

Measured
by
Internalized
Stigma
of
Mental
Illness
scale
(Ritsher-‐Boyd
et
al.,
2003).

e

Measured
by
the
4-‐item
short
version
of
the
Medical
Outcomes
Study
Social
Support
Survey
(MOSSS;
(Sherbourne
&
Stewart,
1991).

f

Measured
by
Involvement
in
Community
Activities
Scale
adapted
from
Living
Skills
Scale
(Wallace
et
al.,
2000).

g

Measured
by
Community
Integration
Interviewer
scale
mental
health
and
non-‐mental
health
versions,
adapted
from
the
CIM
(McColl
et
al.,
2001).

h

Measured
by
an
adaptation
of
the
social
resources
resource
generator
(Van
Der
Gaag
&
Webber,
2008).

80

Physical
community
integration
regression
model.
Initial
modeling
strategies

showed
high
collinearity
between
the
network
variables.
Due
to
collinearity
and
the
small

sample
size,
we
did
not
include
all
significant
social
network
variables
in
the
bivariate

analysis.
The
network
variables
used
in
the
models
included
the
following:
proportion
of

family,
proportion
of
non-‐mental
health
friends,
and
proportion
of
caseworkers.
Using
the

different
dimensions
of
community
integration
in
separate
analyses,
four
sets
of

hierarchical
regression
were
tested,
each
with
four
nested
models.
Table
13
shows
a

detailed
review
of
the
models
for
physical
community
integration
using
the
sum
of
the
total

community
activities
an
individual
participated
in
during
the
previous
30
days
as
a

dependent
variable
and
operationalization
of
the
physical
community
integration.
Model
1

tested
the
association
of
employment
status
with
physical
community
integration.
Models
2

and
3
subsequently
added
psychosocial
factors
(symptomatology,
psychosocial
functioning,

and
internalized
stigma)
and
social
network
variables
(proportion
of
family,
proportion
of

non-‐mental
friends,
and
proportion
of
caseworkers),
respectively,
and
Model
4
added
the

country
affiliation.

As
shown
in
Table
13,
Model
2
was
a
better-‐fitted
model
compared
with
Model
1

(∆F
=
3.69;
p
<
.05);
Model
3
was
significantly
better
than
Model
2
(∆F
=
3.56;
p
<
.05).

However,
Model
4
was
not
better
fitted
than
Model
3.
Based
on
these
findings,
Model
3
was

identified
as
the
best-‐fitting
model,
which
accounted
for
approximately
39%
of
the
variance

in
physical
community
integration.
Compared
with
the
people
who
were
unemployed,
the

people
who
were
employed
showed
high
levels
of
physical
integration
at
a
trend
level.

Being
employed
led
to
a
1.33-‐unit
increase
in
physical
community
integration
(p
<
.10).

Additionally,
a
higher
proportion
of
caseworkers
in
one’s
network
led
to
a
decrease
in

81

physical
community
integration.
Every
unit
increase
in
the
proportion
of
caseworkers
led

to
a
9.74-‐unit
decrease
in
physical
community
integration
(p
<
.05).
Controlling
for
other

variables,
symptomatology,
psychosocial
functioning,
stigma,
proportion
of
family,
and

proportion
of
non-‐mental
health
friends
were
not
found
to
be
significantly
associated
with

physical
community
integration.
Because
adding
country
affiliation
did
not
improve
the

model
fit,
country
affiliation
was
not
found
to
be
associated
with
physical
community

integration.

Another
set
of
models
was
run
with
total
social
resources
as
an
operationalization
of

physical
community
integration
using
hierarchical
regression
testing
four
models.
Model
1

tested
the
association
of
employment
status
with
physical
community
integration
(total

social
resources).
Models
2
and
3
subsequently
added
psychosocial
factors

(symptomatology,
psychosocial
functioning,
and
internalized
stigma)
and
social
network

variables
(proportion
of
family,
proportion
of
non-‐mental
friends,
and
proportion
of

caseworkers),
respectively,
and
Model
4
added
the
country
affiliation.
None
of
the
models

was
found
to
be
significant,
and
we
did
not
find
any
independent
variables
that
were

associated
with
the
amount
of
social
resources
an
individual
had.
The
full
model
accounted

for
17%
of
variance
in
the
dependent
variable.
The
results
of
these
models
are
given
in

Table
14.

82

Table
13

Multivariate
Regression
of
Physical
Community
Integration
(Involvement
in
Community

Activities)
a

of
Individuals
with
SMI

Model
1
Model
2
Model
3
Model
4

Variables
B
SE
B
SE
B
SE
B
SE

Constant
3.171
.315
.949
1.765
3.591
2.450
3.589
2.465

Demographic
variables

Employed

1.363**
.609
1.027
.661
1.332*
.673
1.568**
.766

Psychosocial
predictors

Symptomatology

b

.028
.022
.025
.020
.022
.021

Psychosocial
functioning

c

.251**
.120
.114
.122
.101
.125

Internalized
stigma

d

-‐.062**
.030
-‐.042
.030
-‐.038
.031

Social
network
predictors

Proportion
of
NMH
friends

1.989
2.075
1.524
2.203

Proportion
of
family

-‐1.258
1.929
-‐1.288
1.941

Proportion
of
caseworker

-‐9.739**
3.760
-‐10.095**
3.822

Cross-‐national
predictors

Country

.466
.707

F
5.014**

4.211***

4.300***

3.772***

R
2

.086

.252

.390

.396

∆F

3.688**

3.558**

.435

∆R
2

.166

.138

.006

Note.
NMH
=
non-‐mental
health.

*p
<
.1;
**p
<
.05;
***p
<
.01
on
a
two-‐tailed
test
of
significance.

a

Measured
by
Involvement
in
Community
Activities
Scale
adapted
from
Living
Skills
Scale
(Wallace
et
al.,
2000).

b

Measured
by
Colorado
Symptom
Inventory
(Shern
et
al.,
1996).

c

Measured
by
Straus
and
Carpenter
Functional
Outcome
scale
(Strauss
&
Carpenter,
1972).

d

Measured
by
Internalized
Stigma
of
Mental
Illness
scale
(Ritsher-‐Boyd
et
al.,
2003).

83

Table
14

Multivariate
Regression
of
Physical
Community
Integration
(Total
Social
Resources)

a

of

Individuals
with
SMI

Model
1
Model
2
Model
3
Model
4

Variables
B
SE
B
SE
B
SE
B
SE

Constant
31.268
2.966
12.282
17.972
11.117
26.189
11.092
26.095

Demographic
variables

Employed

4.465
5.732
1.043
6.726
4.511
7.193
8.894
8.107

Psychosocial
predictors

Symptomatology

b

.128
.220
.111
.216
.053
.221

Psychosocial
functioning

c

1.676
1.223
.833
1.308
.579
1.322

Internalized
stigma

d

-‐.099
.301
-‐.010
.317
.072
.324

Social
network
predictors

Proportion
of
NMH
friends

36.162
22.179
27.525
23.326

Proportion
of
family

11.425
20.619
10.854
20.551

Proportion
of
caseworker

-‐46.184
40.194
-‐52.795
40.456

Cross-‐national
predictors

Country

8.657
7.485

F
.607

.720

1.189

1.215

R
2

.011

.054

.150

.174

∆F

.761

1.770

1.338

∆R
2

.043

.096

.024

Note.
NMH
=
non-‐mental
health.

*p
<
.1;
**p
<
.05;
***p
<
.01
on
a
two-‐tailed
test
of
significance.

a

Measured
by
total
social
resources
obtain
from
social
resources
resource
generator
(Van
Der
Gaag
&
Webber,
2008).

b

Measured
by
Colorado
Symptom
Inventory
(Shern
et
al.,
1996).

c

Measured
by
Straus
and
Carpenter
Functional
Outcome
scale
(Strauss
&
Carpenter,
1972).

d

Measured
by
Internalized
Stigma
of
Mental
Illness
scale
(Ritsher-‐Boyd
et
al.,
2003).

84

Psychological
community
integration
into
the
mental
health
community.

Hierarchical
regression
was
used
to
test
the
association
between
psychological
community

integration
into
the
mental
health
community
and
various
demographic,
psychosocial,
and

network
variables.
Model
1
tested
the
association
of
employment
status
with
psychological

community
integration
into
the
mental
health
community.
Models
2
and
3
subsequently

added
psychosocial
factors
(symptomatology,
psychosocial
functioning,
and
internalized

stigma)
and
social
network
variables
(proportion
of
family,
proportion
of
non-‐mental

friends,
and
proportion
of
caseworkers),
respectively,
and
Model
4
added
the
country

affiliation.

As
shown
in
Table
15,
Model
2
was
a
better-‐fitted
model
compared
with
Model
1

(∆F
=
8.575;
p
<
.01);
Model
3
was
significantly
better
than
Model
2
(∆F
=
2.917;
p
<
.05),

and
Model
4
was
significantly
better
than
Model
3
(∆F
=
8.604;
p
<
.01).
Based
on
these

findings,
Model
4
was
identified
as
the
best-‐fitting
model,
which
accounted
for

approximately
55%
of
the
variance
in
psychological
community
integration
into
the
mental

health
community.
An
increase
in
internalized
stigma
was
found
to
be
associated
with
a

decrease
in
psychological
community
integration
into
the
mental
health
community.
A
1-‐
unit
increase
in
stigma
was
associated
with
a
.22-‐unit
decrease
in
psychological
community

integration
into
the
mental
health
community
(p
<
.05).
A
1-‐point
increase
in
psychosocial

functioning
was
also
associated
with
a
1.11-‐unit
increase
in
psychological
community

integration
into
the
mental
health
community.
Among
the
network
variables,
proportion
of

caseworkers
was
associated
with
an
increase
in
psychological
community
integration
into

the
mental
health
community
at
a
trend
level.
A
1-‐point
increase
in
the
proportion
of

caseworkers
in
an
individual’s
network
was
associated
with
a
25.825-‐unit
(p
<
.10)

85

increase
in
psychological
community
integration
into
the
mental
health
community.
Finally,

county
affiliation
was
found
to
be
associated
with
psychological
community
integration

into
the
mental
health
community.
Individuals
from
the
United
States
were
found
have

higher
levels
of
psychological
community
integration
into
the
mental
health
community

compared
with
the
participants
from
India.
Participants
from
the
United
States
had
7.417

units
(p
<
.01)
greater
psychological
community
integration
into
the
mental
health

community
compared
with
participants
from
India.
Controlling
for
other
factors,

employment
status,
symptomatology,
proportions
of
non-‐mental
health
friends,
and

proportion
of
family
were
not
found
to
be
significantly
associated
with
psychological

community
integration
into
the
mental
health
community.

86

Table
15

Multivariate
Regression
of
Psychological
Community
Integration

a

in
Mental
Health

Community
of
Individuals
with
SMI

Model
1
Model
2
Model
3
Model
4

Variable
B
SE
B
SE
B
SE
B
SE

Constant
36.122
1.337
31.187
6.732
19.096
9.502
19.074
8.816

Demographic
variables

Employed
-‐3.522
2.584
-‐6.002**
2.519
-‐3.406
2.610
.348
2.739

Psychosocial
predictors

Symptoms

b

-‐.001
.082
.007
.078
-‐.056
.075

Functioning

c

1.189**
.458
1.108**
.475
.890*
.447

Stigma

d

-‐.323***
.113
-‐.294**
.115
-‐.224**
.109

Social
network
predictors

Proportion
NMH
friends

17.040**
8.047
9.642
7.880

Proportion
of
family

7.790
7.481
7.301
6.943

Proportion
of
case

workers

31.489**
14.583
25.825*
13.667

Cross-‐national
predictors

Country

7.417***
2.529

F
1.858

7.095***

5.771***

6.942***

R
2

.034

.362

.462

.547

∆F

8.575***

2.917**

8.604***

∆R
2

.328

.100

.085

Note.
NMH
=
non-‐mental
health.

*p
<
.1;
**p
<
.05;
***p
<
.01
on
a
two-‐tailed
test
of
significance.

a

Dependent
variable:
measured
by
the
mental
health
version
of
Community
Integration
Measure
(CIM;
(McColl
et
al.,
2001).

b

Measured
by
Colorado
Symptom
Inventory
(Shern
et
al.,
1996).

c

Measured
by
Straus
and
Carpenter
Functional
Outcome
scale
(Strauss
&
Carpenter,
1972).

d

Measured
by
Internalized
Stigma
of
Mental
Illness
scale
(Ritsher-‐Boyd
et
al.,
2003).

87

Psychological
community
integration
into
the
non-‐mental
health
community.

Hierarchical
regression
was
used
to
test
the
association
between
psychological
community

integration
into
the
non-‐mental
health
community
and
various
demographic,
psychosocial,

and
network
variables.
Model
1
tested
the
association
of
employment
status
with

psychological
community
integration
into
the
non-‐mental
health
community.
Models
2
and

3
subsequently
added
psychosocial
factors
(symptomatology,
psychosocial
functioning,
and

internalized
stigma)
and
social
network
variables
(proportion
of
family,
proportion
of
non-‐
mental
friends,
and
proportion
of
caseworkers),
respectively.
Initial
modeling
indicated
the

absence
of
a
linear
relationship
between
proportion
of
family
members
and
psychological

community
integration
into
the
non-‐mental
health
community,
which
seemed
contrary
to

the
literature
on
importance
of
family
support
in
psychological
community
integration
into

the
non-‐mental
health
community.
Hence,
adding
the
quadratic
term
for
the
proportion
of

family
variables
tested
a
curvilinear
relationship
between
proportion
of
family
in
the

network
and
psychological
community
integration
into
the
non-‐mental
health
community.

The
results
indicated
the
presence
of
a
curvilinear
relationship.
Hence,
both
the
linear
and

quadratic
terms
were
added
to
the
final
Model
3.
Model
4
added
the
country
affiliation,

along
with
employment
status,
psychosocial
variables,
and
social
network
variables.

As
shown
in
Table
16,
Model
2
was
a
better-‐fitted
model
compared
with
Model
1

(∆F
=
2.32;
p
<
.10);
Model
3
was
significantly
better
than
Model
2
(∆F
=
3.834;
p
<
.01).

However,
Model
4
was
not
a
better-‐fitted
model
than
Model
3.
Based
on
these
findings,

Model
3
was
identified
as
the
best-‐fitting
model,
which
accounted
for
approximately
40%

of
the
variance
in
psychological
community
integration
into
the
non-‐mental
health

community.
An
increase
in
internalized
stigma
was
associated
with
a
decrease
in

88

psychological
community
integration
into
the
non-‐mental
health
community
at
a
trend

level.
A
1-‐unit
increase
in
internalized
stigma
was
associated
with
a
.192-‐unit
decrease
in

psychological
community
integration
into
the
non-‐mental
health
community
(p
<
.10).
A
1-‐
unit
increase
in
the
proportion
of
family
also
led
to
a
65.24-‐unit
(p
<
.01)
increase
in

psychological
community
integration
into
the
non-‐mental
health
community,
but
this
effect

tapered
off
as
the
proportion
of
family
increased,
and
eventually,
this
relationship
became

negative.
Hence,
a
high
proportion
of
family
was
found
to
be
negatively
associated
with

psychological
community
integration
into
the
non-‐mental
health
community
(B
=
-‐47.44;
p

<
.01).
Controlling
for
other
factors,
employment
status,
symptomatology,
psychosocial

functioning,
proportions
of
non-‐mental
health
friends,
and
proportion
of
caseworkers
were

not
found
to
be
significantly
associated
with
psychological
community
integration
into
the

non-‐mental
health
community.
Additionally,
because
adding
country
affiliation
did
not

improve
the
model
fit,
country
affiliation
was
not
found
to
be
associated
with
psychological

community
integration
into
the
non-‐mental
health
community.

89

Table
16

Multivariate
Regression
of
Psychological
Community
Integration
in
Non-‐Mental
Health

Community

a

of
Individuals
with
SMI

Model
1
Model
2
Model
3
Model
4

Variable
B
SE
B
SE
B
SE
B
SE

Constant
39.780
1.107
39.551
6.423
21.815
9.040
21.999
9.103

Demographic
variables

Employed
5.020**
2.138
3.396
2.404
3.138
2.445
3.906
2.740

Psychosocial
predictors

Symptoms

b

-‐.070
.079
-‐.111
.074
-‐.120
.076

Functioning

c

.497
.437
.516
.461
.480
.467

Stigma

d

-‐.100
.108
-‐.192*
.105
-‐.177
.108

Social
network
predictors

Proportion
NMH
friends

2.459
7.543
.734
8.061

Proportion
of
family

65.241***
20.482
63.753***
20.748

Proportion
of
family

(quad
term)

-‐47.441***
16.929
-‐46.230***
17.146

Proportion
of
case

workers

6.643
13.420
5.567
13.613

Cross-‐national
predictors

Country

1.600
2.511

F
5.510**

3.223**

3.894***

3.461***

R
2

.094

.205

.404

.409

∆F

2.323*

3.834***

.406

∆R
2

.111

.199

.005

Note.
NMH
=
non-‐mental
health.

*p
<
.1;
**p
<
.05;
***p
<
.01
on
a
two-‐tailed
test
of
significance.

a

Dependent
variable:
measured
by
the
non-‐mental
health
version
of
Community
Integration
Measure
(CIM;
(McColl
et
al.,
2001).

b

Measured
by
Colorado
Symptom
Inventory
(Shern
et
al.,
1996).

c

Measured
by
Straus
and
Carpenter
Functional
Outcome
scale
(Strauss
&
Carpenter,
1972).

d

Measured
by
Internalized
Stigma
of
Mental
Illness
scale
(Ritsher-‐Boyd
et
al.,
2003).

90

Social
community
integration.
Hierarchical
regression
was
used
to
test
the

association
between
social
community
integration
and
various
demographic,
psychosocial,

and
network
variables.
Model
1
tested
the
association
of
employment
status
with
social

community
integration,
as
measured
by
perceived
social
support.
Models
2
and
3

subsequently
added
psychosocial
factors
(symptomatology,
psychosocial
functioning,
and

internalized
stigma)
and
social
network
variables
(proportion
of
family,
proportion
of
non-‐
mental
friends,
and
proportion
of
caseworkers),
respectively,
and
Model
4
added
the

country
affiliation.

As
shown
in
Table
17,
Model
2
was
a
better-‐fitted
model
compared
with
Model
1

(∆F
=
4.781;
p
<
.01);
Model
3
was
significantly
better
than
Model
2
(∆F
=
2.367;
p
<
.10),

and
Model
4
was
significantly
better
than
Model
3
(∆F
=
3.467;
p
<
.10).
Based
on
these

findings,
Model
4
was
identified
as
the
best-‐fitting
model,
which
accounted
for

approximately
35%
of
the
variance
in
social
community
integration.
A
1-‐unit
increase
in

symptom
severity
was
significantly
associated
with
a
.081-‐unit
decrease
in
perceived
social

support
(p
<
.05).
A
1-‐unit
increase
in
internalized
stigma
was
also
found
to
be
associated

with
a
.14-‐unit
decrease
in
perceived
social
support
(p
<
.05).

Among
the
network
variables,
proportion
of
non-‐mental
health
friends
was

associated
with
an
increase
in
psychological
community
integration
into
the
mental
health

community
at
a
trend
level.
A
1-‐point
increase
in
the
proportion
of
non-‐mental
health

friends
in
an
individual’s
network
was
associated
with
a
7.498-‐unit
increase
in
perceived

social
support
(p
<
.05).
Proportion
of
family
members
in
an
individual’s
network
was
also

found
to
be
significantly
associated
with
the
levels
of
perceived
social
support.
A
1-‐unit

increase
in
the
proportion
of
family
members
was
found
to
be
associated
with
9.179-‐unit

91

increase
in
perceived
social
support
(p
<
.01).
Finally,
county
affiliation
was
found
to
be

associated
with
perceived
social
support
at
a
trend
level.
Individuals
from
the
United
States

were
found
have
lower
levels
of
perceived
social
support
compared
with
the
participants

from
India.
Participants
from
the
United
States
had
2.283
units
(p
<
.01)
units
smaller

perceived
social
support
levels
compared
with
participants
from
India.
To
summarize,

controlling
for
other
factors,
employment
status,
psychosocial
functioning,
and
proportions

of
caseworkers
were
not
found
to
be
significantly
associated
with
levels
of
perceived
social

support.

92

Table
17

Multivariate
Regression
of
Social
Community
Integration
(Perceived
Social
Support

a
)
in

Individuals
with
SMI

Model
1
Model
2
Model
3
Model
4

Variable
B
SE
B
SE
B
SE
B
SE

Constant
14.463
.560
22.860
3.061
14.797
4.386
14.803
4.275

Demographic
variables

Employed
1.337
1.083
1.627
1.146
1.665
1.205
.509
1.328

Psychosocial
predictors

Symptoms

b

-‐.102***
.037
-‐.097***
.036
-‐.081**
.036

Functioning

c

-‐.301
.208
-‐.197
.219
-‐.130
.217

Stigma

d

-‐.085
.051
-‐.120**
.053
-‐.141**
.053

Social
network
predictors

Proportion
NMH
friends

7.498**
3.714
9.775**
3.821

Proportion
of
family

9.179**
3.453
9.330***
3.367

Proportion
of
case
workers

5.814
6.731
7.558
6.627

Cross-‐national
predictors

Country

-‐2.283*
1.226

F
1.523

4.048***

3.517***

3.673***

R
2

.028

.245

.344

.390

∆F

4.781***

2.367*

3.467*

∆R
2

.217

.099

.046

Note.
NMH
=
non-‐mental
health.

*p
<
.1;
**p
<
.05;
***p
<
.01
on
a
two-‐tailed
test
of
significance.

a

Measured
by
the
4-‐item
short
version
of
the
Medical
Outcomes
Study
Social
Support
Survey
(MOSSS;
(Sherbourne
&
Stewart,
1991).

b

Measured
by
Colorado
Symptom
Inventory
(Shern
et
al.,
1996).

c

Measured
by
Straus
and
Carpenter
Functional
Outcome
scale
(Strauss
&
Carpenter,
1972).

d

Measured
by
Internalized
Stigma
of
Mental
Illness
scale
(Ritsher-‐Boyd
et
al.,
2003).

93

Results
for
Research
Aim
3:
Multilevel
analysis
with
individual-‐
and
network-‐level

variables
associated
with
Disclosure
about
mental
illness.

Aim
3
examined
the
effects
of
individual-‐
and
network-‐level
variables
on
stigma

associated
with
disclosure
about
mental
illness
using
a
multilevel
model.

Table
18
presents
the
findings
from
the
multilevel
multivariate
logistic
regression

models.
Among
the
individual-‐level
variables,
symptoms,
internalized
stigma,
and

perceived
social
support
were
found
to
be
associated
with
disclosure
about
mental
illness.

With
every
unit
increase
in
symptom
level,
the
odds
of
an
individual
disclosing
that
they

have
a
mental
illness
increased
by
approximately
9%
(OR
=
.913;
p
<
.10).
It
is
noteworthy

that
the
OR
indicated
in
Table
18
is
.913,
which
would
indicate
an
inverse
relationship
with

our
dependent
variable.
Because
our
dependent
variable
is
worded
negatively
(did
not

want
to
disclose),
an
inverse
relationship
would,
hence,
indicate
an
increase
in
the

probability
of
disclosure.

As
per
the
internalized
stigma
levels,
an
increase
was
associated
with
a
25%

decrease
in
the
odds
of
disclosure
about
mental
illness
(OR
=
1.245;
p
<
.01).
An
increase
in

perception
of
social
support
was
associated
with
a
28.5%
increase
in
the
odds
of
disclosure

(OR
=
.715;
p
<
.10).

In
terms
of
the
network-‐level
variables,
length
of
association
was
found
to
be

associated
with
disclosure.
A
1-‐year
increase
in
length
of
association
with
a
network

member
was
associated
with
a
5%
increase
in
the
odds
of
disclosure
(OR
=
.950;
p
<
.01).
In

terms
of
whether
an
individual
knew
a
network
member
before
they
started
receiving

mental
health
services,
individuals
were
twice
as
likely
to
disclose
to
individuals
they
knew

before
mental
health
treatment
compared
with
the
network
members
they
did
not
know

94

before
starting
mental
health
services.
In
terms
of
relationship
with
the
alter,
compared

with
family
members,
individuals
were
two
times
less
likely
to
share
with
friends
(OR
=

2.185;
p
<
.10),
four
times
less
likely
to
disclose
to
neighbors
(OR
=
4.218;
p
<
.05),
and
22

times
less
likely
to
disclose
to
colleagues
(OR
=
22.608;
p
<
.01).
However,
compared
with

family
members,
individuals
were
99%
more
likely
to
disclose
to
a
caseworker
(OR
=
.005;

p
<
.01).

95

Table
18

Multilevel
Analysis
with
Individual-‐
and
Network-‐Level
Variables
Associated
with

Unwillingness
to
Disclose
about
Mental
Illness

a

Variable
OR
SE
95%
CI

Constant
.169
1.334

Individual-‐level
variables

Country

.300
.502
.011—7.954

Employed

1.378
2.538
.037—50.937

Symptoms

b

.913*
.052
.817—1.022

Psychosocial
functioning

c

.866
.262
.478—1.568

Internalized
stigma

d

1.245***
.104
1.057—1.467

Perceived
social
support

e

.715*
.157
.465—1.098

Psychological
community
integration
in
MH
community

f

.903
.101
.725—1.126

Psychological
community
integration
in
NMH
community

f

1.210
.167
.923—1.587

Network-‐level
variables

Length
of
association
.950***
.017
.917—.985

Knew
before
mental
health
services
.430*
.206
.168—1.100

Network-‐level
relationship
variables
(reference
group:

family/partner)

Friend
2.185*
.977
.910—5.248

Neighbor
4.218**
2.741
1.180—15.076

Colleague
22.608***
19.285
4.248—120.323

Caseworker
.005***
.006
.001—.044

Acquaintance
2.888
3.362
.295—28.298

Note.
MH
=
mental
health;
NMH
=
non-‐mental
health.

*p
<
.1;
**p
<
.05;
***p
<
.01.

a

Dependent
variable:
measured
using
a
question
asked
during
the
network
interview
on
disclosure
about
mental
illness.

b

Measured
by
Colorado
Symptom
Inventory
(Shern
et
al.,
1996).

c

Measured
by
Straus
and
Carpenter
Functional
Outcome
scale
(Strauss
&
Carpenter,
1972).

d

Measured
by
Internalized
Stigma
of
Mental
Illness
scale
(Ritsher-‐Boyd
et
al.,
2003).

e

Measured
by
the
4-‐item
short
version
of
the
Medical
Outcomes
Study
Social
Support
Survey
(MOSSS;
(Sherbourne
&
Stewart,
1991).

f

Measured
by
the
mental
health
and
non-‐mental
health
versions
of
Community
Integration
Measure
(CIM;
(McColl
et
al.,
2001).

96

CHAPTER
FIVE:

DISCUSSION

Although
community
integration
has
been
identified
as
an
important
treatment

outcome
and
an
integral
aspect
of
mental
health
recovery,
there
is
little
empirical
work
that

examines
the
experience
and
characteristics
of
community
integration
for
the
individuals

with
SMI
in
community-‐based
settings
(Abdallah
et
al.,
2009;
Prince
&
Gerber,
2005;

Wieland
et
al.,
2007;
Wong
&
Solomon,
2002).
Additionally,
cultural
factors
are
known
to

have
tremendous
impact
on
the
conception,
course,
and
outcomes
associated
with
the

illness
(Hopper
&
Wanderling,
2000;
Lauber
&
Rossler,
2007).
The
differential
courses
of

illness
and
prognosis
have
implications
for
the
cross-‐cultural
and
cross-‐national

generalizations,
both
in
terms
of
research
and
practice.
Existing
empirical
evidence
on

community
integration
fails
to
give
sufficient
credence
to
these
individual
differences

across
individuals
and
ethnic
and
cultural
groups
(Townley
et
al.,
2009).
Keeping
this
in

mind,
the
primary
aim
of
this
dissertation
was
to
explore
the
concepts
of
community

integration
in
India
and
the
United
States
to
bridge
this
theoretical
and
conceptual

disconnect.
We
aimed
to
explore
the
idea
of
community
integration
of
individuals
with
SMI

who
were
receiving
treatment
in
a
public
mental
health
care
setting
within
the
socio-‐
cultural
contexts
of
India
and
the
United
States
and
examine
the
social
network
and

psychosocial
factors
that
facilitate
and
subvert
the
process
of
community
integration.

Using
the
conceptual
framework
of
Wong
and
Solomon

(2002),
and
within
the

contexts
of
theories
of
community,
community
integration,
social
network,
social

resources,
social
support,
and
stigma,
we
conceptualized
community
integration
as

physical
integration,
social
integration,
and
psychological
integration.
The
study
used
a

unique
methodology
to
increase
the
nuanced
understanding
of
community
integration
and

97

factors
associated
with
it.
Along
with
the
measures
of
integration,
community
activities,

social
resources,
and
social
support,
we
used
social
network
analysis
methods
to
obtain

detailed
information
about
the
individuals’
networks
and
attributes
of
those
networks,
as

well
as
to
identify
the
individuals
in
their
networks
who
were
a
source
of
social
support,

social
resources,
and
stigma.

Specific
aims
of
this
dissertation
were
as
follows:
(1)
to
study
and
compare
the

degree
of
community
integration
(including
physical,
social,
and
psychological
integration)

of
individuals
with
SMI
in
Indian
and
U.S.
samples,
(2)
to
understand
community

integration
and
how
the
psychosocial
and
network
variables
are
associated
with
the
three

dimensions
of
community
integration
for
individuals
with
SMI
in
Indian
and
U.S.
samples,

and
(3)
to
explore
the
use
of
multilevel
analysis
in
examining
the
effects
of
individual-‐
and

network-‐level
variables
on
disclosure
about
mental
illness.

Overall,
the
results
indicate
country
differences
in
some
of
the
conceptualization

and
levels
of
community
integration
for
the
different
dimensions
proposed
by
Wong
and

Solomon
(2002).
Primary
descriptive
statistics
reveal
some
very
interesting
differences

between
the
Indian
and
U.S.
samples.
Most
(approximately
two-‐thirds)
of
the
Indian

sample
is
married
as
compared
to
less
than
a
quarter
of
the
U.S.
sample.
This
finding
is

consistent
with
the
culturally
rooted
Indian
society
that
still
believes
in
marriage
as
a

panacea
to
mental
illness
and
where
not
being
married
is
almost
as,
if
not
more,

stigmatizing
as
having
a
mental
illness
(Srivastava,
2013;
Thara
et
al.,
2003a,
2003b).
We

also
found
more
than
half
of
the
Indian
sample
is
employed
as
compared
to
less
than
5%
of

the
U.S.
participants.
Even
within
the
Indian
sample,
the
phenomenon
of
being
employed
is

predominantly
male.
This
can
also
be
traced
back
to
the
cultural
roots
of
the
country,

98

whereby
men
have
a
compelling
need
to
be
the
bread
winners
of
the
family
(Marwaha
&

Johnson,
2004;
Srinivasan
&
Thara,
1997).
At
the
same
time,
just
being
employed
is
not

generally
indicative
of
the
kind
of
work
an
individual
is
employed
in
and
the
levels
of
their

satisfaction
with
their
work.
A
study
by
Srinivasan
and
Thara
(1997)
on
an
Indian
sample

found
“good
rates”
(80%)
of
employment
in
their
sample
but
attributed
this
to
their
low-‐
education
and
socio-‐economic
status,
which
could
imply
that
these
jobs
were
not
highly

skilled
or
did
not
require
much
education
(Srinivasan
&
Thara,
1997).

Importantly,
the
samples
from
the
two
countries
are
not
significantly
different
from

each
other
in
terms
of
their
symptom
levels
and
over
all
psychosocial
functioning.
The
two

samples,
however,
are
found
to
be
significantly
different
in
terms
of
their
internalized

stigma
levels.
This
finding
parallels
previous
research
that
has
shown
stigma
related
to

mental
illness
to
be
pervasive
and
multilayered
in
India
(Chowdhury,
Sanyal,
Dutta,

Banerjee,
De,
Bhattacharya,
Palit,
et
al.,
2000;
Longanathan
&
Murthy,
2012;
Raguram
et
al.,

1996).

In
terms
of
country
differences
associated
with
community
integration
of

individuals
with
SMI,
AIM
1
of
this
study,
the
two
countries
are
different
in
some

dimensions
of
community
integration
and
not
in
others.
Indian
respondents,
in
general,
are

less
integrated
in
the
mental
health
community,
rated
by
the
interviewer
to
be
more

integrated
in
the
non-‐mental
health
community,
have
higher
amounts
of
perceived
social

support,
and
have
more
members
affiliated
to
the
non-‐mental
health
community,
and

family
primarily
populates
their
social
networks.
These
findings
can
be
interpreted
using

the
normalization
perspective
(Wolfensberger,
2011;
Wolfensberger
&
Tullman,
1982)
that

supports
the
notion
that
community
integration
into
the
non-‐mental
health
community
can

99

occur
(Bond
et
al.,
2004;
Wolfensberger,
2011;
Wolfensberger
&
Tullman,
1982).

Individuals
from
the
United
States
feel
more
integrated
into
the
mental
health
community,

were
rated
by
the
interviewer
to
be
more
integrated
in
the
mental
health
community,

receive
more
social
resources
from
mental
health
community
members,
and
have
a
mixture

of
mental
health-‐
and
non-‐mental
health-‐based
networks
ties
in
their
lives.
These
findings

can
be
interpreted
using
the
subcultures
paradigm,
whereby
community
for
individuals

with
mental
illness
is
a
combination
of
both
mental
health-‐based
identity
and
belonging
to

the
larger
non-‐mental
health
community
(Mandiberg,
2012;
Wong
et
al.,
2010).
Hence,
we

find
support
for
Wong’s
(Wong
et
al.,
2010)
idea
of
multiple
communities
and
Mandiberg’s

(Mandiberg,
2012)
idea
of
subcultures
in
that
not
only
do
the
individuals
have
distinct

communities
they
interact
with,
but
they
also
experience
integration
into
these

communities
differently.
Pending
further
investigation,
this
might
also
indicate
that

individuals
from
the
United
States
have
more
congruence
in
their
sense
of
community

belonging
and
where
they
receive
social
network
membership
but
might
be
further
away

from
one
expected
goal
of
recovery,
which
is
to
integrate
into
the
non-‐mental
health

community.

Network
Differences:
India
and
the
United
States

The
general
network
characteristics
support
our
hypothesis
in
that
the
Indian

sample
has
more
family
members
and
colleagues
in
their
networks
compared
with
the
U.S.

sample
and
a
smaller
proportion
of
caseworkers.
However,
contrary
to
our
hypothesis,
the

U.S.
sample
has
a
higher
proportion
of
friends
and
non-‐mental
health
acquaintances.
The

first
finding
speaks
to
the
cultural
environment
of
India,
where
family
is
more
emphasized

and
forms
the
primary
sources
of
support
(Bhatia
et
al.,
2012a;
Nunley,
1998),
which
is

100

why
almost
70%
of
Indian
networks
are
family
based.
The
U.S.
sample,
however,
is
more

friend
based.
This
finding
is
in
keeping
with
the
existing
literature
on
social
networks,
their

typologies,
and
the
effect
of
different
types
of
majority
networks
on
the
mental
health
of
an

individual.
Previous
studies
in
the
United
States
on
networks
suggest
that
friend-‐based

networks
are
more
prominent
and
more
influential
compared
with
family-‐based
networks

and
have
a
higher
association
with
positive
mental
health
outcomes
(Fiori,
Antonucci,
&

Cortina,
2006;
Litwin,
2001).
If
we
were
to
go
by
this
previous
evidence
gathered
from

research
in
the
United
States,
high
family-‐intensive
networks
in
India
would
imply
lower

functioning
individuals.
However,
our
findings
actually
suggest
that
Indian
participants
are

not
different
from
the
U.S.
participants
in
terms
of
symptomatology
or
overall
functioning

and
are
actually
better
functioning
in
terms
of
employment.
This
is
a
good
example
of
how

culture
influences
norms
and
practices
in
a
society,
and
findings
from
one
culture
should
be

generalized
to
another
culture
with
extreme
caution.

The
importance
of
friends-‐based
networks
for
the
U.S.
participants
is
further

emphasized
when
we
look
at
the
specific
relationships
that
are
sources
of
social
support

and
social
resources
in
India
and
the
United
States.
In
our
study,
we
found
that
the

individuals
from
the
United
States
nominated
significantly
more
friends
from
whom
they

derive
different
types
of
social
support
and
social
resources
compared
with
the

participants
from
India.

Our
study
results
also
show
some
interesting
findings
in
terms
of
mental
health
and

non-‐mental
health
acquaintances
that
are
contrary
to
what
we
hypothesized.
We
had

hypothesized
that
Indian
participants
would
have
more
non-‐mental
health-‐based

networks,
which
is
true
for
most
categories.
However,
we
found
the
participants
from
India

101

to
have
more
mental
health
acquaintances
compared
with
the
U.S.
participants.
A
possible

explanation
for
this
finding
could
be
that
the
Indian
sample
has
most
of
their
strong
ties

defined
by
the
non-‐mental
health
community.
Other
than
the
caseworker,
the
other
ties

identified
within
the
mental
health
community
are
actually
weak
ties
and,
hence,
fall
under

the
category
of
acquaintances
and
not
identified
under
other
mental
health
categories
of

friends,
peers,
or
peer
providers.
This
would
imply
that
most
strong
ties
in
the
participants

from
India
are
based
out
of
the
non-‐mental
health
community.

The
ability
to
identify
the
specific
relationships
that
could
be
sources
of
social

support,
social
resources,
and
stigma
was
a
unique
contribution
of
this
study.
The

quantitative
results
were
enhanced
by
the
use
of
network
methodology
that
allowed
us
to

dig
deeper
and
get
a
better
understanding
of
the
various
dimensions
and
concepts
related

to
community
integration.
For
example,
when
the
participants
were
asked
about
their

perception
of
social
support,
it
was
found
that
on
an
average,
participants
from
India
have
a

higher
perception
of
social
support
compared
with
the
participants
from
the
United
States.

However,
when
asked
to
identify
specific
individuals
from
whom
they
receive
this
social

support,
participants
from
the
United
States
nominate
a
higher
percentage
of
individuals

from
whom
they
access
both
emotional
and
interactional
social
support.
This
could
imply

that
although
Indian
participants
identify
fewer
numbers
of
network
members
who
can
be

a
potential
source
of
social
support,
whoever
they
do
nominate
are
strong
enough
and

close
enough
ties
that
they
lead
to
an
increased
perception
of
social
support.
In
other

words,
more
people
do
not
necessarily
indicate
quality
relationships
(House,
Landis,
&

Umberson,
1988;
House,
Umberson,
&
Landis,
1988).
Additionally,
participants
from
the

United
States
receive
significantly
more
emotional
support
from
the
mental
health

102

community,
friends,
and
caseworkers
compared
with
the
participants
from
India,
which

could
be
another
reason
why
they
feel
more
integrated
into
the
mental
health
community.

Bridging
and
Bonding
Social
Resources:
A
Social
Networks
Phenomenon
in
India
and

the
United
States

Social
resources
accessed
from
one’s
network
has
been
linked
in
the
literature
to

positive
experiences
related
to
mental
illness
and
better
outcomes
(Sartorius,
2003).

Whereas
bonding
social
resources
(in
the
form
of
intimate
friends,
family
members,
and

close
ties
in
this
study)
are
linked
to
better
health
outcomes,
bridging
social
resources

(service
providers,
neighbors,
and
colleagues
in
this
study)
are
known
to
protect

individuals
from
the
stressors
associated
with
a
mental
illness
by
providing
access
to
a

broader
range
of
networks
and
resources
(Szreter
&
Woolcock,
2004).
Our
results
on
social

resources
obtained
from
individual
networks
indicate
that
individuals,
both
in
India
and

the
United
States,
access
different
kinds
of
social
resources
from
different
groups
of
people.

More
importantly,
when
we
specifically
looked
at
the
information
on
personal
social

resources,
whereby
individuals
identified
people
in
their
networks
who
helped
them
take

care
of
their
health,
there
was
a
marked
difference
in
where
the
individuals
from
the
two

countries
access
resources.
Indian
participants
largely
depend
on
family
to
help
them
take

care
of
their
health,
whereas
participants
from
the
United
States
have
a
mixture
of
mental

health
(caseworkers)
and
non-‐mental
network
members
(friends)
who
they
access
their

health
care
resources
from.
If
interpreted
in
terms
of
bridging
and
bonding
social
capital,
it

would
seem
that
Indian
participants
depend
more
on
the
bonding
social
capital
for
their

health
care
needs
and
resources,
whereas
the
participants
from
the
United
States
have
a

mix
of
bridging
and
bonding
social
capital.
Other
types
of
social
resources
show
mixed

103

results,
but
the
personal
skills
type
of
social
resources,
whereby
the
network
members

influence
positive
health
behavior,
is
most
meaningful
for
our
study.
The
Indian
sample

indicate
using
family
as
their
primary
source
of
help
for
their
health,
which
is
intuitive,

since
family
is
closest
and,
in
many
instances,
individuals
in
India
live
with
immediate
and

extended
family
members
who
could
physically
be
present
to
help
them
take
care
of
their

health.
However,
health
resources
are
more
than
that.
They
include
information
about

resources
that
come
from
a
more
heterogeneous
group
of
people
that
are
usually
more

resourceful
(Irwin
et
al.,
2008),
for
instance,
caseworkers
who
can
provide
and
link
them
to

much-‐needed
services
(Mitchell
&
LaGory,
2002;
Szreter
&
Woolcock,
2004).
These

resources
are
equally
important
for
an
individual
to
access
appropriate
care
when
needed.

Whereas
family
can
be
an
important
support,
in
a
country
like
India
with
limited
awareness

about
mental
illnesses,
there
is
a
need
for
more
formalized
networks
consisting
of

professionals
and
para-‐professionals
who
can
link
them
with
appropriate
care
and

services.
In
conclusion,
both
bridging
and
bonding
social
capital
are
required
in
balance
to

promote
proper
health
behavior.
Whereas
bonding
social
capital
is
very
important,

bridging
social
capital
is
required
for
this
population
to
break
“social
and
economic

isolation”
due
to
their
illness
(Mitchell
&
LaGory,
2002;
Putnam,
2000).

Regression
Models
of
Community
Integration

The
second
aim
of
this
dissertation
was
to
understand
community
integration
and

how
the
psychosocial
and
network
variables
are
associated
with
the
three
dimensions
of

community
integration
for
individuals
with
SMI
in
India
and
the
United
States.
Several

important
results
emerged
from
the
regression
models
on
different
community
integration

dimensions.
The
overall
findings
are
as
follows:
First,
country
affiliation
is
associated
with

104

some
dimensions
of
community
integration
and
not
others.
Specifically,
psychological

integration
into
the
mental
health
community
as
an
indicator
of
psychological
community

integration
and
social
support
as
an
indicator
of
social
community
integration
are
found
to

be
associated
with
country
affiliation.
Participants
from
the
United
States
are
found
to
have

higher
levels
of
physical
community
integration
in
terms
of
involvement
in
community

activities.
Indian
participants,
on
the
other
hand,
are
found
to
have
higher
levels
of

perceived
social
support.
These
findings
are
consistent
with
the
previous
literature
on
the

presence
of
social
support
in
the
lives
of
individuals
with
SMI
in
India
that
can
be
related
to

better
outcomes
in
countries
like
India
(Hopper
&
Wanderling,
2000).

The
second
important
finding
is
the
importance
of
social
networks
that
are

associated
with
different
community
integration
dimensions.
This
was
the
first
study
to

find
that
different
network
relationships
are
associated
with
different
types
of
community

integration.
In
the
current
study,
a
higher
proportion
of
caseworkers
are
associated
with

decreasing
physical
integration
and
increasing
psychological
integration
into
the
mental

health
community.
Higher
proportions
of
family
members
in
one’s
network
are
found
to
be

associated
with
an
increase
in
psychological
integration
into
the
non-‐mental
health

community,
to
a
certain
extent.
However,
as
the
proportion
of
family
increases,
its

association
with
psychological
community
integration
becomes
negative.
This
finding

speaks
to
the
literature
on
family
involvement
in
mental
illness,
specifically
in
India
and

otherwise.
Whereas
family
engagement
of
Indian
families
in
different
facets
of
the
lives
of

individuals
with
SMI
is
associated
with
a
better
course
of
illness
and
outcomes
(Bhatia
et

al.,
2012a;
Nunley,
1998),
it
has
also
been
linked
with
increased
stigma,
blaming,
expressed

emotions,
and
delay
in
seeking
services
(Bhatia
et
al.,
2012a;
Khandelwal,
Jhingan,
Ramesh,

105

Gupta,
&
Srivastava,
2004;
Longanathan
&
Murthy,
2012;
Pakaslahti,
2012).
Additionally,

an
increased
proportion
of
family-‐based
networks
could
imply
the
presence
of
extended

family
in
the
nominated
networks,
which
could,
in
turn,
imply
the
presence
of
the
negative

factors
associated
with
family
members,
with
no
or
little
support
associated
with
close

family
networks.

As
for
the
social
community
integration,
both
the
proportion
of
family
and
non-‐
mental
health
friends
are
found
to
be
significantly
associated
with
an
increase
in
perceived

social
support
as
an
indicator
of
social
community
integration.
This
speaks
to
the
idea
of

the
main
effect
theory
of
social
support,
whereby
the
more
diverse
an
individual’s
social

network,
the
greater
their
access
is
to
functional
social
relationships
that
can
provide
the

necessary
support
for
them
to
deal
with
a
stressor
(S.
Cohen
&
Wills,
1985).

Finally,
in
terms
of
demographic
and
psychosocial
characteristics,
being
employed
is

significantly
associated
with
physical
community
integration
(involvement
in
community

activities).
Higher
psychosocial
functioning
is
found
to
be
associated
with
a
decrease
in

psychological
community
integration
in
the
mental
health
community.
This
could
imply

that
poorer
psychosocial
functioning
would
mean
that
an
individual
would
have
a
harder

time
adjusting
in
mainstream
community,
and
the
mental
health
community
during
those

times
could
feel
like
a
safe
haven.
This
finding
has
implications
for
interventions,
whereby

an
individual
with
SMI
would
need
to
be
equipped
with
various
social
and
instrumental

tools
to
function
better
in
mainstream
community
and
move
toward
recovery.

Higher
psychiatric
symptoms
are
also
found
to
be
associated
with
lower
social

support.
We
can
find
roots
of
this
in
the
literature
where
social
support
has
been
found
to

be
inversely
related
to
symptoms
of
mental
illness
and
associated
distress
(Barrera,
2000;

106

Patrick
W
Corrigan
&
Phelan,
2004;
Eaton,
1978;
Ozer,
Best,
Lipsey,
&
Weiss,
2008).

Greater
internalized
stigma
is
associated
with
higher
psychological
community

integration
in
the
mental
health
community
and
a
decrease
in
community
integration
in
the

non-‐mental
health
community.
This
finding
is
consistent
with
the
literature
on

subcommunities,
whereby
stigma
in
the
non-‐mental
health
community
could
be
a
primary

reason
that
an
individual
with
a
mental
illness
might
seek
support
in
the
mental
health

community,
and
less
internalized
stigma
could
potentially
increase
integration
into

mainstream
community
(Mandiberg,
2012;
Wong
&
Solomon,
2002)

The
current
study
recognizes
the
importance
of
stigma
in
the
process
of
community

integration.
This
is
why
stigma
was
studied
at
the
level
of
the
individual
in
the
form
of

internalized
stigma
and
also
at
the
network
level
to
identify
the
sources
of
stigma.
Our

initial
bivariate
and
multivariate
analyses
on
stigma
are
representative
of
the
existing

literature
that
emphasizes
the
importance
of
mental
health
stigma
in
the
lives
in

individuals
with
mental
illness,
especially
in
India.
The
Indian
participants
had
high
levels

of
internalized
stigma
compared
with
the
U.S.
participants
and
nominated
more
individuals

in
their
non-‐mental
health-‐based
social
networks
who
were
a
source
of
stigma.
They

recognize
mainly
family
and
colleagues
who
are
sources
of
stigma.
These
findings
give

credence
to
the
literature
on
stigma
in
India
whereby
stigma
is
pervasive
across
different

levels,
including
at
the
level
of
the
family
(Chowdhury,
Sanyal,
Dutta,
Banerjee,
De,

Bhattacharya,
Palit,
et
al.,
2000;
Imran
&
Haider,
2007;
Longanathan
&
Murthy,
2012;

Longanathan
&
Murthy,
2008;
Raguram
et
al.,
1996;
Thara
&
Srinivasan,
2000).

Finally,
the
third
aim
of
this
study
was
to
explore
the
use
of
multilevel
analytical

techniques
in
examining
the
effects
of
individual-‐
and
network-‐level
variables
on
an

107

individual’s
disclosure
about
mental
illness.
Given
that
the
disclosure
about
mental
illness

could
be
a
good
indicator
of
stigma,
understanding
disclosure
gives
us
an
insight
into
how

stigma
might
be
expressed
in
the
lives
of
individuals
with
SMI.
By
using
the
individual-‐level

and
network-‐level
data
simultaneously,
this
study
provides
a
complex
understanding
of

factors
that
are
associated
with
disclosure
related
to
mental
illness.
Several
important

findings
emerged
from
this
analysis.

Turning
to
the
individual-‐level
results,
we
found
that
higher
symptoms
and
more

perceived
social
support
each
lead
to
greater
disclosure
about
mental
illness.
The
first

finding
supports
existing
literature
that
suggests
it
is
relatively
easy
to
hide
a
mental
illness

if
the
signs
and
symptoms
are
not
obvious
(Chaudoir
&
Fisher,
2010;
Garcia
&
Crocker,

2008);
however,
if
the
symptoms
become
more
severe,
disclosure
may
become
more

necessary.
Increased
social
support
has
been
linked
in
the
literature
with
both
stigma

reduction
(Rüsch,
Angermeyer,
&
Corrigan,
2005)
and
an
increase
in
disclosure
about

mental
illness
(Chaudoir
&
Fisher,
2010;
Garcia
&
Crocker,
2008).
Internalized
stigma
is

also
associated
with
disclosure.
Higher
levels
of
internalized
stigma
are
associated
with
a

greater
unwillingness
to
disclose.
This
finding
gives
us
an
insight
in
how
self-‐stigmatizing

might
lead
someone
to
not
share
or
disclose
about
their
mental
illness.
This
relationship

between
internalized
stigma
and
disclosure
could
be
a
result
of
decreased
self-‐image
or

previous
bad
experiences
associated
with
disclosure
(Omarzu,
2000).

An
individual’s
willingness
(or
unwillingness,
as
measured
in
the
current
study)
to

disclose
is
found
to
be
related
primarily
to
the
relationship
network-‐level
variables.

Compared
with
family,
individuals
are
less
willing
to
disclose
to
friends,
neighbors,
and

colleagues
and
more
willing
to
disclose
to
caseworkers.
This
finding
is
consistent
with
the

108

literature
on
disclosure
in
that
disclosure
is
not
only
linked
with
stigma
and
the
possibility

of
stigma,
but
it
is
also
associated
with
maintaining
a
certain
self-‐image
in
front
of
society

(Fisher,
Burnet,
Huang,
Chin,
&
Cagney,
2007;
Garcia
&
Crocker,
2008).

There
is
one
individual-‐level
finding
that
is
not
significant
but
is
worth
mentioning.

Country
affiliation
is
not
significantly
associated
with
disclosure
about
mental
illness.
This

finding
is
especially
relevant
because
both
our
bivariate
and
multivariate
analyses
show

important
country
differences
in
terms
of
stigma
and
disclosure.
Indian
participants
have

higher
levels
of
internalized
stigma,
and
at
a
bivariate
level,
have
higher
levels
of

unwillingness
to
disclose,
especially
to
family
members
and
colleagues.
A
possible
rationale

for
this
could
be
that
membership
in
a
particular
network
category
is
more
important
to

the
behavior
associated
with
disclosure
rather
than
country
affiliation.
Hence,
because
we

added
network-‐level
variables
that
account
for
the
relationship
with
the
network
tie
and

how
having
a
particular
relationship
could
be
associated
with
disclosure,
the
country

affiliation
is
no
longer
significant.
This
particular
finding
speaks
to
the
relevance
of

multilevel
methodologies
to
study
complex
phenomena
like
community
integration,
stigma,

and
social
resources,
where
understanding
the
source
or
context
of
a
phenomenon
is
as

important
as
measuring
the
total
levels
of
a
particular
phenomenon.
For
example,
if
we

were
to
use
this
technique
for
predicting
various
forms
of
social
resources,
we
would
be

able
to
account
for
different
individual-‐level
and
network-‐level
factors
that
could
influence

the
presence
of
social
resources.

Implications
for
Social
Work
Practice
and
Research

The
current
study
has
important
implications
for
cross-‐national
research
and

practice.
First,
given
that
our
samples
show
different
levels
of
integration
into
different

109

aspects
of
community
integration
suggests
that
community
integration
is
not
an
unalloyed

concept
and,
hence,
interventions
aimed
toward
increasing
community
integration
should

be
adapted
and
targeted
appropriately.
Second,
because
social
networks
are
found
to
be

important
both
as
a
representation
of
social
community
integration
and
as
significantly

associated
with
various
dimensions
of
community
integration,
it
opens
the
avenue
for

practitioners
to
incorporate
individuals
from
their
client’s
networks
to
increase
the
efficacy

of
interventions.
Whereas
incorporation
of
family
members
into
the
treatment
and
follow-‐
up
plans
have
already
been
suggested
and
encouraged
in
service
delivery
settings,
our

results
emphasize
the
importance
of
friends
in
the
lives
of
individuals
with
mental
illness,

especially
in
the
United
States,
which
gives
rise
to
the
possibility
of
tapping
into
this

network
resource
in
an
individual’s
life.

Third,
given
that
the
study
found
country
differences
in
some
dimensions
of

community
integration
and
not
others,
any
practice
or
intervention
needs
to
be
culturally

adapted
to
be
relevant
across
different
cultures
and
nations.
Where
cross-‐national

differences
give
credence
to
the
cultural
basis
of
community
and
community
integration,

they
also
raise
questions
about
the
practice
of
applying
concepts
to
ethically
heterogeneous

groups
of
people
within
the
United
States.
Research
with
racial/ethnic
minorities
with
SMI

should
examine
cultural
factors,
such
as
social
support
and
social
network
structures
that

may
influence
concepts
like
community
integration.

In
terms
of
implications
for
research,
this
study
uses
two
innovative
methods
to

study
community
integration
and
associated
factors.
First,
the
combination
of
quantitative

and
network
methods
give
good
insight
into
the
community
integration
behavior
of

individuals
with
SMI.
Second,
this
study
uses
multilevel
modeling
to
understand
how
the

110

complex
association
of
individual
and
network-‐level
factors
are
associated
with
factors

related
to
community
integration.
In
this
study,
we
used
multilevel
modeling
to
get
an
in-‐
depth
understanding
of
disclosure
about
mental
illness.
However,
this
can
be
used
to

understand
other
concepts
related
to
community
integration.

Limitations,
Strengths,
and
Suggestions
for
Further
Study

There
are
a
few
important
limitations
to
the
current
study
that
need
to
be

considered
while
interpreting
the
results.
First,
this
study
used
cross-‐sectional
data,
which

make
the
results
associational
and
not
causal.
Lack
of
a
longitudinal
design
and

unavailability
of
accompanying
qualitative
data
limited
our
ability
to
establish
causality.

However,
inferences
about
causality
are
often
difficult
to
make
in
field
research
because
of

many
uncontrolled
background
sources
of
variance.

Second,
all
data
were
self-‐report,
which
may
lead
to
social
desirability
bias.

Additionally,
the
data
only
represented
voluntary
participants.
Third,
these
data
were
drawn

from
purposive
sampling,
which
is
good
for
comparisons
but
might
not
be
generalizable
to
the

entire
population
of
individuals
with
SMI.
Fourth,
data
were
collected
from
one
site
each
in
the

United
States
and
India,
which
could
present
another
problem
in
the
generalizability
of
the

results.
Next,
most
of
the
sample
in
the
United
States
was
Euro-‐American
or
Latino.
Hence,
the

findings
might
not
be
representative
of
other
ethnic
or
cultural
groups.
Another
limitation
of

the
study
is
its
small
sample
size,
which
in
combination
with
cross-‐sectional
data,
limited

our
ability
to
draw
conclusive
inferences.
Fifth,
we
did
not
have
enough
information
in
the

data
to
distinguish
between
urban
and
rural
participants,
both
in
India
and
the
United

States.
This
difference
might
be
significant
because
of
the
presence
of
more
stigma
and

different
perceptions
related
to
mental
illnesses.

111

There
were
several
major
strengths
of
this
dissertation.
First,
this
is
the
first
study

to
examine
different
dimensions
of
community
integration
in
two
different
countries
using

a
unique
blend
of
quantitative
and
network
methodologies.
The
cross-‐national
component,

in
particular,
gave
us
a
more
nuanced
understanding
of
community
integration
within
an

individual’s
cultural
context.
Second,
this
study
expanded
the
social
dimension
of

community
integration
given
by
Wong
and
Solomon
(2002)
using
bonding
and
bridging

social
capital
perspectives
of
the
social
capital
theory.
Third,
we
enhanced
the
information

availed
using
network
methodology
to
include
relational-‐level
network
data
to
attain
more

information
about
an
individual’s
network.
Fourth,
stigma
was
studied
on
multiple
levels:

as
a
variable
that
might
impact
the
dimensions
of
community
integration
and
its

relationship
with
disclosure
about
mental
illness
at
the
network
level.
Finally,
this
study

incorporated
multilevel
modeling
to
study
the
association
between
stigma
and
various

individual-‐level
and
network-‐level
variables.

Despite
the
limitations
of
the
current
study,
this
research
provides
a
foundation
for

further
exploration
in
a
variety
of
the
ways.
First,
because
we
found
differences
in
the

levels
of
community
integration
for
individuals
in
high-‐
and
low-‐intensity
service
groups
in

the
United
States
(Pahwa
et
al.,
2013),
future
studies
could
explore
country
differences
in

terms
of
different
service-‐intensity
modalities.
Additionally,
our
sample
size
was
not
large

enough
for
us
to
do
subgroup
analyses
in
terms
of
different
cultural/racial
groups
within

the
United
States.
Future
studies
should
include
diverse
subgroups
to
understand
cultural

differences
in
terms
of
different
community
integration
dimensions.
Furthermore,
the

current
study
provided
some
interesting
and
important
insights
into
the
process
of

community
integration.
However,
the
concepts
require
a
larger
sample
size
using
a

112

longitudinal
methodology
to
establish
causal
relationships
and
emphatic
conclusions.
Using

qualitative
methodologies
would
also
enhance
our
understanding
of
community,

community
integration,
social
support,
social
resources,
and
stigma
in
the
lives
of

individuals
with
SMI.
Beyond
the
methodological
and
theoretical
contributions,
this
study

raised
questions
about
community
integration
and
factors
that
might
influence
this

process.
Additional
studies
will
further
elucidate
the
questions
raised
by
this
study.

Conclusion

Findings
from
the
current
study
provide
a
good
foundation
to
understand
the

concept
of
community
integration
and
the
factors
associated
with
this
process.
This
study

advances
the
investigation
of
community
integration
in
important
ways
by
using

comprehensive
and
innovative
methodologies
and
measures
and
by
distinguishing

empirically
between
mental
health
and
non-‐mental
health
communities
for
individuals
in

two
different
countries.

Overall,
we
would
recommend
that
approaches
to
community
integration
for
this

population
recognize
the
challenges,
opportunities,
and
contradictions
that
individuals

with
SMI
face
as
they
navigate
between,
share
network
membership
with,
and
access

resources
from
mental
health
and
non-‐mental
health
communities.

113

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