Post-traumatic growth among high-risk youth: predictors, impact of stressful life events, and relationship with changes in substance use behaviors

PDF

Download

Open document

Flip pages

Copy asset link

Request this asset

Request accessible transcript

Transcript (if available)

Content
i

POST-TRAUMATIC GROWTH AMONG HIGH-RISK YOUTH:
PREDICTORS, IMPACT OF STRESSFUL LIFE EVENTS, AND RELATIONSHIP
WITH CHANGES IN SUBSTANCE USE BEHAVIORS

by

Thalida Em Arpawong

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
(PREVENTIVE MEDICINE)

August 2013

Copyright 2013 Thalida Em Arpawong

ii

DEDICATION

This
dissertation
is
dedicated
to
Pat
and
Milo,
and
our
attempts
to
create
a
new

conceptualization
of
“normal.”

iii

ACKNOWLEDGEMENTS

I
wish
to
impart
a
huge
sense
of
gratitude
to
my
committee
members,
each
of

whom
provided
valuable
feedback
for
this
dissertation
as
well
as
respected
advice

throughout
the
process
of
getting
to
this
point.
To
Dr.
Louise
Ann
Rohrbach,
I
want

to
express
my
sincere
appreciation
for
taking
me
on
as
a
new
mentee
before
my

third
year
in
the
program
when
my
interests
and
research
foci
were
not
always

clear.
You
have
provided
an
unwavering
sense
of
stability,
direction,
and
detail-‐
orientation
to
this
doctoral
process,
while
always
kindly
and
patiently
reminding
me

to
be
guided
by
theory.

To
Dr.
Steve
Sussman,
I
thank
for
providing
me
with

multiple
research
opportunities
(including
local
and
global
ones),
exceedingly

timely
and
constructive
feedback
whenever
needed,
and
encouraging
words
about

the
field
and
my
future
career.

To
Dr.
Joel
Milam,
I
am
grateful
for
your
generosity

in
sharing
your
depth
of
understanding
and
enthusiasm
for
this
dissertation
topic,

your
infinite
patience,
encouragement,
and
levity
in
my
training.

To
Dr.
Jennifer

Unger,
I
am
grateful
for
your
bountiful
advice
on
topics
from
data
analysis
to
paper

writing
to
post-‐doctoral
training,
and
for
your
constant
encouragement.
To
Dr.

Helen
Land,
I
thank
for
the
perspectives
that
challenged
my
thinking
and
approach

to
this
research,
and
as
a
result
have
broadened
my
vantage
point
and
improved
this

work.

A
special
thanks
goes
to
individuals
at
IPR
and
Preventive
Medicine
who

have
become
a
genuine
circle
of
support.
Thank
you
to
Marny
Jane
Barovich
for
her

inestimable
support,
guidance,
and
friendship
throughout
the
many
years
I
have

been
connected
with
IPR.
To
fellow
students
who
have
become
esteemed
colleagues

and
cherished
friends,
I
wish
to
thank
Dr.
Lilia
Espinoza,
Dr.
Kari-‐Lyn
Sakuma,
and

Dr.
Keosha
Partlow,
for
being
my
safety
net
and
role
models
in
many
realms.
Thank

you
to
Kathleen
Ruccione
in
being
the
other
50%
of
my
cohort,
and
a
friend.
Thank

you
to
Dr.
Kathleen
Meeske,
Dr.
Jean
Richardson,
Margaret
Hawkins,
Dr.
Selena

Nguyen-‐Rodriguez,
Claradina
Soto,
and
Grace
Huang
for
their
support
and

encouragement.
Thank
you
to
Dr.
Ping
Sun
for
always
providing
statistical

advisement
as
well
as
realistic
viewpoints
on
the
research
life.
Also,
thank
you
to

IPR
staff,
especially
Gabriela
Torres,
Leah
Meza,
and
Jennifer
Patch,
for
handling

logistical
details
that
made
my
life
easier.

A
heart-‐felt
thanks
to
those
who
form
the
foundation
of
support
for
my
life.

To
my
mom,
Ubol
Arpawong,
who
has
imbued
in
me
the
understanding
of

unconditional
love,
support,
and
family
priorities.

She
sets
the
bar
far-‐and-‐above

my
reach
as
a
person
and
mother,
but
inspires
me
to
reach
higher
when
I
can.
To
my

dad,
Kumtorn
Arpawong,
who
has
tacitly
demonstrated
a
strong
sense
of
values,

iv

generosity,
and
work
ethic
that
compel
me
to
channel
him
as
I
persist
though
my

life’s
work
and
tasks.

To
my
sister,
Aer
Lee,
and
brother,
Ed
Arpawong,
with
their

respective
spouses
and
children
(Ken,
Lucas,
and
Emma
Lee,
and
Hieu,
Ethan,
Hailey,

and
Sean
Arpawong),
I
am
grateful
for
all
your
love,
support,
humor,
and
unique

personalities
that
keep
life
interesting
no
matter
what
the
circumstance.

A

monstrous
thank
you
to
Tag
Milo
who
amazes
me
every
day
and
inspires
me
to
be

and
do
better
in
every
way
possible,
while
reminding
me
to
pay
attention
to
what
is

truly
important
in
life.

And,
to
Pat
Kouwabunpat,
who
decidedly
began
this

marathon
journey
with
me
14
years
ago,
has
tirelessly
supported
me
through
the

multiple
mini-‐marathons,
patiently
allowed
me
space
and
time
to
pursue
my
own

goals,
continues
to
encourage
me
to
find
happiness
in
all
areas
of
life,
and
has

become
a
true
partner
and
ally
against
challenges
we
have
come
upon.

Lastly,
but
certainly
not
least,
thank
you
to
the
continuation
high
school

students,
some
of
whom
surprised
me
in
their
willingness
to
provide
perspectives

on
this
research
that
was
above-‐and-‐beyond
what
I
had
asked
them
to
do.

This
research
was
supported
by
the
Tobacco-‐Related
Disease
Research
Program

Dissertation
Award
(Grant
#20DT-‐0041).

v

TABLE
OF
CONTENTS

DEDICATION
ii

ACKNOWLEDGEMENTS
iii

LIST
OF
TABLES
vi

LIST
OF
FIGURES
vii

ABSTRACT
viii

CHAPTER
1:
INTRODUCTION
1

Substance
Use
and
Stressful
Life
Events
Among
High-‐Risk
Older
Youth
2

Post-‐traumatic
Growth
and
Stressful
Life
Events
3

Overview
of
the
Dissertation
5

CHAPTER
2:
STUDY
1
10

Stressful
Life
Events
and
Predictors
of
Posttraumatic
Growth
10

Method
17

Results
29

Discussion
33

CHAPTER
3:
STUDY
2
46

Posttraumatic
Growth
and
Change
in
Substance
Use
Behaviors
46

Method
50

Results
57

Discussion
60

CHAPTER
4:
CONCLUSION
68

Implications
for
Preventive
Interventions
68

Limitations
71

Future
Research
Directions
72

Summary
76

REFERENCES
77

vi

LIST
OF
TABLES

Table
1.
Selected
Sample
Characteristics
(n=564)
42

Table
2.
Correlates
and
Predictors
of
Post-‐Traumatic
Growth
43

Table
3.
Regression
Models
for
the
Associations
of
Socio-‐Demographic

Characteristics,
Stressful
Life
Events,
Personal
System
and
Environmental

System
Characteristics
with
Posttraumatic
Growth
44

Table
4.
Prevalence
of
Substance
Use
Behaviors
Among
the
CHS
Sample
at
Two-‐
Year
Follow-‐Up
66

Table
5.
Regression
Models
Showing
the
Impact
of
SLEs
and
PTG
on
Change
in

Frequency
of
Substance
Use
Behaviors
67

vii

LIST
OF
FIGURES

Figure
1.

Frequencies
of
Stressful
Life
Events
Reported
44

Figure
2.
Moderation
of
the
Relationship
Between
PTG
and
Future
Time
Perspective

by
Hispanic
Ethnicity
45

viii

ABSTRACT

Background:
The
experience
of
a
highly
stressful
life
event
(SLE)
may
elicit
positive

psychosocial
change
in
some
individuals,
referred
to
as
Post-‐traumatic
Growth

(PTG).
This
dissertation
represents
novel
research
in
which
two
studies
were

designed
to
answer
the
following
questions:
(1)
what
predicts
PTG,
including

personal
and
environmental
characteristics
as
well
as
the
number
and
severity
of

stressfulness
of
SLEs
experienced?;
and
(2)
how
do
SLEs
and
PTG
influence
changes

in
the
frequency
of
substance
use
behaviors
over
time
among
vulnerable,
ethnically

diverse,
older
youth?

In
addition,
theoretical
postulates
were
tested
to
examine

whether
mean
scores
of
PTG
in
this
sample
represent
an
illusory
perception
of

growth
as
a
transient
palliative
strategy
to
regain
a
sense
of
self-‐esteem
post-‐SLE
or

if
PTG
scores
represent
an
attempt
to
achieve
congruence
through
growth
in
both

cognitive
and
behavioral
functioning.

Methods:
Students
were
recruited
from
alternative
high
schools
(n=564;
mean

age=16.8),
where
they
participated
in
the
Project
Toward
No
Drug
Abuse

intervention.
Surveys
were
administered
in-‐person,
by
phone
or
mail-‐back.
Data

regarding
socio-‐demographic,
personal
and
environmental
characteristics
were

collected
at
baseline
and
1-‐year
follow-‐up.
Data
regarding
SLEs
and
PTG
were

collected
at
2-‐year
follow-‐up.
Data
on
substance
use
behaviors
were
collected
at

both
baseline
and
2-‐year
follow-‐up.

For
both
studies,
multi-‐level
regression
models

were
constructed,
controlled
for
sociodemographic
variables,
peer
and
baseline

substance
use,
attrition,
and
treatment
group
where
relevant.
For
the
exploratory

ix

moderation
in
Study
1,
interaction
terms
were
created
between
ethnicity
and
each

of
the
potential
predictors
to
evaluate
their
relationship
with
PTG.

Results:
Nearly
half
of
the
participants
were
female;
65%
were
Hispanic,
and
on

average,
all
reported
experiencing
3
SLEs
in
the
past
two
years.

Findings
from
Study

1
were
that
the
majority
of
participants
reported
developing
PTG
as
a
result
of
their

most
life-‐altering
SLE.
Predictors
of
PTG
included
fewer
SLEs,
less
general
stress,

greater
identification
with
the
developmental
stage
of
Emerging
Adulthood,
and
an

interaction
between
Hispanic
ethnicity
and
future
time
perspective.
Findings
from

Study
2
were
that
a
greater
number
of
SLEs
predicted
greater
use
of
cigarettes,

alcohol,
marijuana,
hard
drugs
and
substance
use.

In
contrast,
greater
PTG

significantly
predicted
less
use
of
alcohol,
getting
drunk
on
alcohol,
binge
drinking,

marijuana
use,
and
substance
abuse.

Conclusions:
Taken
together,
findings
from
these
studies
indicate
that
high-‐risk,

older
youth
report
SLEs
that
reflect
their
unique
life
stage
and
set
of
circumstances.

Constructs
that
assessed
stage
of
life
were
more
salient
in
predicting
PTG
than
were

constructs
reflecting
mood
states
(i.e.,
depression,
positive
affect),
although
future

time
perspective
predicted
higher
PTG
among
Hispanics
only.

Regarding
theoretical

postulates,
the
finding
that
higher
PTG
predicted
less
substance
use
suggests
that

higher
PTG
scores
were
not
representative
of
transient
or
merely
illusory

exaggerations
of
post-‐SLE
adjustment
rather
they
were
indicative
of
growth
on
both

the
cognitive
and
behavioral
levels
among
these
youth.

Furthermore,
although

x

greater
SLEs
predicted
lower
PTG,
findings
from
these
studies
support
the
notion

that
positive
psychosocial
adjustment
to
a
life-‐altering
experience
may
counteract

the
negative
impact
of
stress
from
SLEs
on
substance
use
behaviors
among
high-‐risk

youth.

1

CHAPTER
1:
INTRODUCTION

Stress
from
significant
life
events
(e.g.,
medical
trauma,
being
witness
to
a

crime,
natural
disaster,
relationship
break-‐up,
abuse)
can
induce
substantial

cognitive,
emotional,
spiritual,
social,
and
behavioral
change
within
an
individual’s

life.
Particularly
among
late
adolescence
to
young
adulthood,
a
time
period

sometimes
referred
to
as
emerging
adulthood,
individuals
are
seen
as
fulfilling
a

normative
role
in
going
through
a
process
of
individuation,
where
they
become

psychologically
and
socially
independent
(Arnett,
2000;
Arnett,
2004;
Grotevant
&

Cooper,
1986;
Steinberg,
1981).

They
are
expected
to
progress
through
the

developmental
stage
of
life
that
includes
experimenting
with
taking
on
adult

responsibilities
and
learning
how
to
effectively
adapt
to
circumstances
that
occur
in

their
lives,
however
stressful
(Arnett,
2000;
Arnett,
2004).

Experiencing
distressing

and
traumatic
events
at
this
young
age
may
lead
to
maladaptive
coping
or

adjustment
and
has
been
linked
to
psychological
impairment
such
as
the

development
of
depressive
and
anxiety
disorders,
suicidal
ideation,
complicated

grief
states,
post-‐traumatic
stress
disorder
(PTSD),
substance
use
and
dependence,

and
chronic
physical
problems
(Buckner,
Beardslee,
&
Bassuk,
2004;
Hahn
&
Se,

2008;
Kessler,
2002;
Wills
&
Shiffman,
1985).

Aside
from
such
psychological

consequences,
substantial
evidence
shows
that
maladaptive
coping
methods
and

adjustment
to
stress
with
the
resulting
emotional
distress
causing
more
health-‐
compromising
behaviors
among
adolescents,
such
as
earlier
initiation
of
and
more

2

frequent
substance
use
(e.g.,
Ursano
et
al.,
2004;
Wagner
et
al.,
2009;
Wills,
1986;

Wills,
Vaccaro,
&
McNamara,
1992).

Substance
Use
and
Stressful
Life
Events
Among
High-‐Risk
Older
Youth

With
respect
to
substance
use,
estimates
indicate
that
in
2009,
approximately

69.7,
130.6,
and
21.8
million
Americans
age
12
and
older
used
tobacco,
alcohol,
or

illicit
drugs
(i.e.,
marijuana/hashish,
cocaine,
heroin,
hallucinogens,
inhalants
or

prescription-‐type
psychotherapeutics
taken
for
non-‐medical
reasons)
in
the
past

month,
which
represents
27.7%,
51.9%,
and
8.7%
of
the
population
within
that
age

range,
respectively
(SAMHSA,
2010).
Further,
illicit
drug
use
among
older

adolescents
ages
16
to
17
reached
16.7%
in
2009
(SAMHSA,
2010).
Adolescents

who
use
or
misuse
substances
have
a
higher
likelihood
of
having
experienced
highly

stressful
and
traumatic
events
in
their
past
(e.g.,
childhood
sexual
abuse,
witnessing

violence,
natural
disaster),
tend
to
live
in
higher-‐risk
environments,
with
substance-‐
using
peers
and
substance-‐using
role
models
in
close
proximity
(Fergusson,
Boden,

&
Horwood,
2008;
Sussman
&
Ames,
2008).

Further,
substances
have
long
been

used
as
a
method
of
coping
and
relief
from
distress
(e.g.,
Holahan
et
al.,
2001;
Wills,

1986).

Some
youth
are
at
higher
risk
than
others
for
experiencing
both
higher
levels

of
stress/trauma
as
well
as
engaging
in
health-‐compromising
behaviors.
In

particular,
students
who
attend
continuation
high
schools
1

(CHSs)
may
experience

greater
levels
of
stress/trauma
than
their
regular
high
school
(RHS)
counterparts,

1
Continuation
High
Schools
may
be
called
alternative,
contract,
or
community
high
schools
in
states

other
than
California.
Generally,
students
in
these
schools
have
left
regular
high
school
because
of

excessive
truancy,
poor
academic
performance,
drug
use,
violence,
other
illegal
activity,
or
disruptive

behavior
(Rohrbach
et
al.,
2005).

3

including
emotional
and
physical
abuse
or
victimization,
loss
of
a
parent,
cycling
in-‐
and-‐out
of
foster
care,
being
a
witness
to
violence,
and
other
occurrences
that
cause

them
to
feel
disconnected
from
mainstream
society
(Zweig
&
Institute,
2003).

In

addition,
CHS
students
report
a
higher
prevalence
of
tobacco,
alcohol,
and
marijuana

use
(Rohrbach
et
al.,
2005;
Sussman
et
al.,
1995).
Although
the
relationship
between

self-‐reported
stress/trauma
and
substance
use
disorders
and
other
health-‐
compromising
behaviors
has
been
well
established
in
the
literature
(Ursano,
et
al.,

2004;
Wagner,
et
al.,
2009;
Wiechelt,
2007;
Wills,
1986;
Wills,
et
al.,
1992)
not
all

adolescents
exhibit
maladaptive
behaviors
after
having
experienced
a
highly

stressful
life
event.

Post-‐traumatic
Growth
and
Stressful
Life
Events

Many
youth
are
able
to
adapt
very
well
after
experiencing
highly
stressful
life

events
(SLEs).
Such
youth
undergo
a
process
in
which
they
emerge
in
the
aftermath

of
a
traumatic
experience
with
a
more
positive
perspective
on
life.

They
develop

Post-‐traumatic
Growth
(PTG).
PTG
has
been
characterized
as
the
qualities
of
having

developed
a
greater
investment
in
and
appreciation
for
life,
improved
interpersonal

relationships,
a
greater
sense
of
one’s
spirituality,
and
an
augmented
sense
of

personal
strength
(Tedeschi
&
Calhoun,
1996).

Thus,
PTG
represents
a
multi-‐
dimensional
construct
such
that
an
individual
realizes
cognitive,
emotional,
and

psychosocial
change.

In
order
for
one
to
develop
PTG
following
a
SLE,
one
needs
to
have

experienced
an
event
significant
enough
such
that
the
event
itself
has
come
to

represent
a
significant
challenge
to
the
adaptive
resources
of
the
individual.

In

4

other
words,
that
event
has
come
to
represent
a
significant
challenge
the
person’s

way
of
understanding
of
themselves,
the
world,
and
their
place
in
it
(Janoff-‐Bulman,

2002).
Such
a
highly
distressing
experience
thereby
results
in
the
process
of

rumination,
cognitive
restructuring
and
re-‐building
of
the
life
perspective
such
that

one
is
not
only
able
to
cope
effectively,
but
also
function
at
a
higher
level
than
the

pre-‐SLE
self
(Calhoun
&
Tedeschi,
2001;
Tedeschi
&
Calhoun,
1995).
The

development
of
PTG
varies
by
individual,
and
may
be
influenced
by
personal
and

contextual
factors,
as
well
as
features
of
stress
from
SLEs
experienced.
These
factors

that
influence
PTG
in
the
aftermath
of
an
SLE
will
be
further
discussed
in
Chapter
2.

In
the
past
20
years,
there
has
been
a
steady
surge
of
research
conducted
on

PTG
and
related
constructs.
As
noted
by
Park
(Park,
2004),
it
is
important
to
study

growth
after
trauma
due
to
its
demonstrated
positive
associations
with
health

outcomes
in
the
context
of
improvements
in
well-‐being
(e.g.,
increased
quality
of
life,

better
adjustment,
reduced
depression
symptoms;
Carver
&
Antoni,
2004;
Lelorain,

Bonnaud-‐Antignac,
&
Florin,
2010;
Schuettler
&
Boals,
2011),
functioning
(e.g.,

socially,
stress
resistance,
immunity
or
endocrinology;
Cruess
et
al.,
2001;
McGregor

et
al.,
2004;
Milam,
2004),
and
behaving
(e.g.,
adopting
healthier
lifestyles,

medication
adherence,
interpersonal
interactions,
generative
actions,
altruism).

This
focus
on
positive
valence
outcomes
is
consistent
with
the
growing
line
of

inquiry
in
the
field
of
positive
psychology
(Seligman
&
Csikszentmihalyi,
2000),

which
has
generated
substantial
support
for
the
significance
of
PTG
and
evidence
of

how
it
may
provide
salutary
benefit
to
overall
well-‐being,
improved
functionality,

and
promote
health-‐related
behaviors.
Furthermore,
research
is
beginning
to

5

provide
a
growing
pool
of
evidence
that
PTG
is
related
to
better
health
behaviors,

and
specifically
inversely
related
to
substance
use.
The
relationship
between
PTG

and
substance
use
will
be
further
discussed
in
Chapter
3.

Overview
of
the
Dissertation

Because
the
sample
for
these
dissertation
studies
is
highly
diverse
in

racial/ethnic
make-‐up
and
is
comprised
of
CHS
youth
categorized
as
being
in
late

adolescence/early
adulthood,
examining
specific
variables
that
contribute
to
the

development
of
PTG
particularly
in
this
sample
provided
a
unique
opportunity
for

several
reasons.
First,
correlates
and
predictors
of
PTG
(i.e.,
SLEs,
general
stress,

depression,
positive
affect,
family
conflict,
peer
substance
use)
have
been
studied
for

their
relationship
to
PTG
in
other
populations,
but
had
not
yet
been
studied
in
CHS

youth
prior
to
this
dissertation.
Second,
correlates
that
assess
stage
of
development,

or
stage
of
life
(e.g.,
emerging
adulthood)
and
life
perspective
(e.g.,
future
time

perspective,
motivation
to
improve),
had
not
been
previously
examined
for
their

relationship
to
PTG
yet
are
highly
relevant
for
CHS
youth,
who
tend
to
take
on
adult

roles
earlier
than
RHS
youth.

Thus,
this
dissertation
includes
the
first
study
(Study

1,
presented
in
Chapter
2)
to
examine
such
correlates
and
predictors
of
PTG
among

a
sample
of
high-‐risk,
older
youth.
Third,
both
occurrences
of
SLEs
and
substance

use
behaviors
tend
to
be
high
among
CHS
youth
such
that
this
sample
provided
an

opportunity
to
examine
the
relationships
with
PTG.

Therefore,
this
dissertation

includes
the
first
study
(Study
2,
presented
in
Chapter
3)
to
examine
the
behavioral

benefit
of
PTG
among
high-‐risk
youth,
by
assessing
the
direct
relationship
between

PTG
and
change
in
substance
use
behaviors
over
time.

6

The
specific
aims
for
the
two
studies
included
in
this
dissertation
were

guided
by
theories
of
PTG
(i.e.,
Schaefer
&
Moos,
1992;
Tedeschi
&
Calhoun,
1995;

Tedeschi
&
Calhoun,
2004)
and
prior
research.

According
to
theories
of
PTG

(Schaefer
&
Moos,
1992;
Schaefer
&
Moos,
1998;
Tedeschi
&
Calhoun,
1995;

Tedeschi
&
Calhoun,
2004),
the
occurrence
of
some
SLE,
whether
or
not
it
qualifies

as
a
diagnosable
traumatic
stressor
2
,
according
to
the
Diagnostic
and
Statistical

Manual
of
Mental
Disorders—IV
(DSM-‐IV;
Association
&
DSM-‐IV.,
2000),
is
a

prerequisite
for
the
development
of
PTG.
Thus,
in
the
endeavor
to
better
understand

PTG,
it
is
important
to
further
explore
the
theoretical
relationship
with
SLEs;
more

specifically,
to
test
whether
or
not
PTG
developed
in
relation
to
a
specific
SLE

(rather
than
general
stress
or
other
life
events).

Numerous
studies
have
been

conducted
to
assess
the
levels
of
PTG
reported
in
relation
to
a
specific
SLE
that
is
a

stressor
common
to
all
participants.
However,
only
a
handful
of
studies
have
been

conducted
to
examine
all
types
of
SLEs
reported
by
participants
(e.g.,
Alisic
et
al.,

2008;
Ickovics
et
al.,
2006;
Milam,
Ritt-‐Olson,
&
Unger,
2004).
Particularly
among

this
sample
of
CHS
youth,
who
experience
high
levels
of
SLEs
and
are
unlikely
to

experience
a
single
common
stressor,
it
is
important
to
examine
the
relationship

between
all
types
of
SLEs
they
experience
and
PTG.

Thus,
these
dissertation
studies

were
designed
to
assess
a
range
of
SLEs,
that
may
not
have
conformed
to
the

diagnostic
criteria
of
trauma
defined
by
the
APA
(i.e.,
events
that
involve
actual
or

threatened
death
or
serious
injury),
yet
referred
to
an
event,
and
the
circumstances

2

An
event
qualifies
as
a
traumatic
stressor
if
it
(a)
involved
an
actual
or
threatened
death
or
serious

injury,
or
a
threat
to
the
physical
integrity
of
oneself
or
to
others,
and
(b)
if
the
individual's
response

involved
intense
fear,
helplessness,
or
horror
(Association
&
DSM-‐IV.,
2000).

7

that
surround
the
event,
that
have
come
to
represent
a
significant
challenge
to
one’s

adaptive
resources.

With
regard
to
Study
1,
one
objective
was
to
document
the
types
and
number

of
highly
stressful
life
events
reported
by
CHS
youth
as
having
occurred
within
a

two-‐year
time
period,
and
to
assess
the
degree
to
which
these
events
were

associated
with
more
positive
perspectives
on
life
in
the
aftermath
an
event

respondents
designated
as
most
life-‐altering
of
the
past
two
years.

Another

objective
of
Study
1
was
to
assess
the
impact
of
relative
severity
of
the
most
life-‐
altering
SLE
on
development
of
PTG.
In
addition,
remaining
objectives
of
Study
1

were
to
examine
how
predictors
of
psychosocial
adjustment
that
are
particularly

relevant
to
CHS
youth
may
influence
the
development
of
PTG.
For
example,

constructs
that
indicate
stage
of
development,
or
stage
of
life
(e.g.,
emerging

adulthood)
may
be
particularly
relevant
in
predicting
psychosocial
adjustment

among
CHS
youth,
who
tend
to
take
on
adult
roles
earlier
than
RHS
youth.
Thus,
for

Study
1,
the
Specific
Aims
and
hypotheses
were
as
follows
(see
Chapter
2
for
an
in-‐
depth
presentation
on
these
concepts):

Aim
1.1.
To
examine
predictors
of
PTG,
including
SLEs,
Personal
System
and

Environmental
System
characteristics
in
a
sample
of
CHS
youth.

H1.1a.
Levels
of
PTG
will
exhibit
an
inverse
relationship
with
the

number
of
SLEs
reported.

H1.1b.
Mean
PTG
will
exhibit
a
curvilinear
(inverted
U-‐shape)

relationship
with
severity
of
SLEs.

8

H1.1c.
The
Personal
System
characteristics
of
general
stress
and

depression
will
be
negative
predictors
of
PTG.
In
contrast,

positive
affect,
emerging
adulthood,
motivation
to
improve,
and

future
time
perspective
will
be
positive
predictors
of
PTG.

H1.1d.
The
Environmental
System
characteristics
of
family
conflict
and

peer
substance
use
will
be
negative
predictors
of
PTG.

Aim
1.2.
To
explore
whether
predictors
of
PTG
(Personal
and
Environmental

System
characteristics,
SLEs)
vary
by
race/ethnicity
(Hispanic
versus
non-‐Hispanic

ethnicity).

With
regard
to
Study
2,
the
effect
of
PTG
on
substance
use
behaviors
was

examined
among
the
ethnically
diverse
sample
of
CHS
students.
With
an
estimated

67%
of
youth
having
experienced
at
least
one
traumatic
event
by
the
age
of
16

(Copeland
et
al.,
2007)
and
substance
use
having
long
been
used
as
a
method
of

coping
with
stress
from
significantly
distressing
experiences,
objectives
of
this
study

were
to
answer
the
question
of
how
stress
from
SLEs
and
PTG
influenced
change
in

the
frequency
of
substance
use
behaviors
over
time
among
these
highly
vulnerable,

older
youth.

Specific
Aims
and
hypotheses
for
Study
2
were
as
follows
(see
Chapter

3
for
an
in-‐depth
presentation
on
these
concepts):

Aim
2.1.
To
confirm
the
relationship
between
cumulative
stress
from
SLEs
and

change
in
substance
use
behaviors
over
time.

H2.1.
Greater
cumulative
stress
from
a
higher
number
of
SLEs
will
be

positively
associated
with
increased
substance
use
(cigarette
use,

9

marijuana,
alcohol,
hard
drugs,
overall
substance
use)
over
time

(baseline
to
2-‐year
follow-‐up).

Aim
2.2.
To
investigate
the
relationship
between
PTG
and
change
in
substance
use

behaviors
over
time.

H2.2.
PTG
will
be
positively
associated
with
decreased
substance
use

(cigarette
use,
marijuana,
alcohol,
hard
drugs,
overall
substance

use)
over
time
(baseline
to
2-‐year
follow-‐up).

10

CHAPTER
2:
STUDY
1

Stressful
Life
Events
and
Predictors
of
Posttraumatic
Growth

Experiencing
traumatic
or
extremely
stressful
life
events
(SLEs)
at
a
young

age
may
contribute
to
poorer
psychological
and
vulnerable
emotional
states,

particularly
as
one
attempts
to
diminish
the
level
of
stress
or
regain
a
sense
of

control
over
life
(Buckner,
et
al.,
2004;
Wills,
1986;
Wills
&
Shiffman,
1985).
In

contrast,
experiencing
relatively
high
levels
of
acute
stress
or
trauma
may
elicit

positive
psychosocial
change
in
some
individuals,
referred
to
as
Post-‐traumatic

Growth
(PTG),
and
may
help
individuals
avoid
long-‐term
psychological
and

emotional
distress
(Tedeschi,
1995;
Tedeschi
&
Calhoun,
1996).
PTG
entails
not
only

recovery
from
highly
stressful
events,
but
also
a
transformative
process
that
results

in
growth
to
a
higher
level
of
functioning
than
the
pre-‐trauma
state
(Aldwin,

Levenson,
&
Spiro,
1994;
O'Leary
&
Ickovics,
1995).
It
has
been
characterized
by

changes
in
the
individual’s
relationships
with
others,
sense
of
personal
strength
and

self-‐reliance,
spiritual
beliefs,
as
well
as
finding
new
possibilities
and
having
a
greater

appreciation
of
life
(Tedeschi
&
Calhoun,
1996).

Examining
the
factors
that

contribute
to
the
development
of
PTG
over
time
may
help
facilitate
more
positive

psychological
states
in
the
aftermath
of
experiencing
highly
stressful
events.

Most
studies
of
PTG
have
been
conducted
among
samples
in
which
all

individuals
in
the
group
have
experienced
some
common
type
of
SLE,
such
as
cancer,

loss
of
a
loved
one,
or
natural
disaster
(e.g.,
Arpawong
et
al.,
In
Press;
Cieslak
et
al.,

2009;
Ho,
Chu,
&
Yiu,
2008;
Park
et
al.,
2008).

Only
a
handful
of
studies
have

assessed
PTG
that
has
developed
in
the
aftermath
of
a
broad
range
of
stressors
(e.g.,

11

Alisic,
et
al.,
2008;
Ickovics,
et
al.,
2006;
Milam,
et
al.,
2004;
Peterson
et
al.,
2008;

Tedeschi
&
Calhoun,
1996).

For
example,
in
one
study
that
was
used
to
validate
the

original
21-‐item
Post-‐traumatic
Growth
Inventory
(PTGI),
604
undergraduate

university
students
were
surveyed
for
the
development
of
PTG
after
having

experienced
one
of
the
following
in
the
past
5
years:
bereavement
(36%),
injury-‐
producing
accidents
(16%),
separation
or
divorce
of
parents
(5%),
relationship

break-‐up
(7%),
criminal
victimization
(5%),
academic
problems
(4%),
unwanted

pregnancy
(2%),
and
several
other
undefined
experiences
(25%)
(Tedeschi
&

Calhoun,
1996).
In
another
study,
authors
assessed
PTG
among
a
sample
of
319

urban
female
adolescents
(mean
age
=
17.24
±
1.49)
who
were
requested
to
divulge

the
hardest
thing
they
ever
had
to
deal
with
(Ickovics,
et
al.,
2006).
General

categories
for
these
stressors
consisted
of
pregnancy/motherhood
(42%),
death
of
a

loved
one
(34%),
physical
threats
(i.e.,
socioeconomic
problems,
health
problems,

crime;
15%
total),
and
interpersonal
problems
(i.e.,
relationship
problems,
another

person’s
problems,
sexual
abuse/harassment;
28%
total).
Significant
differences
in

PTG
were
demonstrated
by
the
event
categories
(p=.002),
suggesting
the

importance
of
assessing
multiple
types
of
SLEs
when
assessing
positive

psychological
growth
among
urban
youth.

For
the
present
study,
the
primary
aim
was
to
examine
PTG
among
an

ethnically
diverse
youth
who
attended
continuation
high
schools
(CHSs)
in
urban

settings.
Typically,
those
who
have
attended
CHSs,
sometimes
referred
to
as

alternative
high
schools,
have
left
regular
high
schools
(RHSs)
because
of
excessive

truancy,
poor
academic
performance,
drug
use,
violence,
other
illegal
activity,
or

12

disruptive
behavior
(Rohrbach,
et
al.,
2005).
Because
this
sample
of
youth

presumably
experience
higher
levels
of
SLEs
compared
to
their
RHS
counterparts

(Zweig
&
Institute,
2003),
they
are
at
risk
of
poorer
psychological
and
emotional

outcomes.
Thus,
the
present
study
was
designed
as
a
novel
examination,
to
assess

the
relationship
between
PTG
and
a
range
of
SLEs,
broad
enough
to
enable
a

vulnerable
sample
to
report
any
event
that
they
considered
as
impacting
enough
to

have
elicited
the
cognitive
perception
of
life
change.

Theories
on
PTG
posit
that
in
order
for
PTG
to
occur,
one
needs
to
have

experienced
a
SLE
that
has
come
to
represent
a
significant
challenge
to
the
adaptive

resources
of
the
individual,
shattering
the
person’s
way
of
understanding

themselves,
their
assumptions
about
the
world,
and
their
place
in
it
(Janoff-‐Bulman,

2002).
The
study
by
Ickovics
et
al
(2006)
supports
this
notion
in
that
those
who
had

experienced
traumas
that
were
coded
as
more
severe
(pregnancy/motherhood,

death
of
a
loved
one,
physical
threats)
reported
significantly
greater
PTG
compared

to
those
who
reported
less
severe
traumas
(interpersonal
problems).
However,
the

relationship
between
distress
and
growth
may
be
more
complicated,
and
thus,
non-‐
linear.
Prior
research
suggests
that
those
who
experience
a
very
low
or
very
high

level
of
distress
from
an
SLE
are
either
not
sufficiently
impacted,
or
overwhelmingly

impacted,
respectively,
and
thus
both
groups
would
report
lower
levels
of
PTG
(e.g.,

Armeli,
Gunthert,
&
Cohen,
2001;
Calhoun
et
al.,
2000;
Carver,
1998;
Kleim
&
Ehlers,

2009;
Laufer
&
Solomon,
2006;
McCaslin
et
al.,
2009;
Tomich
&
Helgeson,
2004).

In

contrast,
those
who
report
that
an
SLE
is
sufficiently
challenging
to
their
sense
of

purpose
and
meaning
in
life
(moderate
level
of
distress)
may
be
more
likely
to

13

report
higher
levels
of
PTG.
Therefore,
in
a
general
sample
PTG
may
demonstrate
a

curvilinear
(inverted
U-‐shape)
relationship
with
the
relative
severity
of
stress
from

the
SLE
experienced
such
that
PTG
is
more
likely
to
develop
from
SLEs
that
are

sufficiently
stressful
yet
not
overwhelmingly
so.

Due
to
the
central
role
that
level
of

distress
from
SLEs
play
in
the
development
of
PTG,
investigating
aspects
of
SLEs

that
influence
PTG
will
help
to
better
characterize
why
PTG
occurs,
and
possibly

how
to
foster
it.

Thus,
another
aim
of
this
study
was
to
examine
the
relationship

between
PTG
and
severity
of
stress
experienced
from
a
life-‐altering
SLE.

Increasing
evidence
also
demonstrates
that
psychosocial
adjustment
to

stress
may
be
impacted
by
the
additivity
of
events,
or
cumulative
number
of
SLEs

experienced
such
that
there
is
a
dose-‐response
relationship
between
the
number
of

different
types
of
SLEs
and
poorer
health
outcomes
(Anda
et
al.,
2006;
Dong
et
al.,

2003;
Larkin,
Shields,
&
Anda,
2012).
Thus,
the
sum
total
of
SLEs
experienced
may

serve
as
a
measure
of
cumulative
stress
and
may
negatively
impact
the
development

of
PTG.
Particularly
among
a
sample
of
youth
who
have
a
higher
likelihood
for

experiencing
stressful
events
than
the
general
population
of
adolescents,
examining

this
dose-‐response
impact
of
types
of
SLEs
on
PTG
provides
a
clearer
picture
of
how

stressful
events
in
a
vulnerable
subgroup
impact
PTG.

With
regard
to
theories
on
PTG,
the
development
of
it
requires
processes
of

rumination,
cognitive
restructuring
and
re-‐building
of
the
life
perspective
(Calhoun

&
Tedeschi,
2001;
Tedeschi
&
Calhoun,
1995).
The
level
of
PTG
reported
varies
by

individual
due
to
other
more
proximal
characteristics
that
influence
how
individuals

create
their
assumptive
world
and
re-‐build
their
new
perspectives
in
the
aftermath

14

of
a
SLE.
These
components
may
be
negatively
or
positively
impacted
by
what
can

be
referred
to
as
Personal
System
characteristics
(e.g.,
cognitive
ability,
motivation,

affect,
health
status;
see
Life
Crises
and
Personal
Growth
Model;
Schaefer
&
Moos,

1992;
Schaefer
&
Moos,
1998)).
For
example,
greater
general
stress
may
cause

individuals
to
interpret
subsequent
SLEs
more
negatively,
which
thereby
inhibits

effective
cognitive
processing
of
the
event.
Additionally,
in
previous
studies,

depression
demonstrated
a
weak
inversely
significant
relationship
with
PTG
in
a

meta-‐analysis
of
17
studies
(Helgeson,
Reynolds,
&
Tomich,
2006)
as
it
presumably

impacts
how
individuals
are
able
to
re-‐build
their
new
realities
post-‐trauma.

Thus,

in
the
present
study,
these
two
pre-‐trauma
factors
will
be
examined
as
predictors
of

PTG.

In
contrast,
theories
on
PTG
presume
that
some
personality
characteristics

may
protect
individuals
from
become
too
immersed
in
feelings
of
distress
and
lead

to
more
meaningful
life
narratives
that
result
in
a
higher
growth
potential
(Tedeschi

&
Calhoun,
1995).
One
such
factor
is
positive
affect.
Positive
affect
has
shown
to

explain
up
to
19%
of
the
variance
in
PTG
in
some
studies
with
breast
cancer

patients
(Lelorain,
et
al.,
2010),
and
thus
may
be
worthy
of
examination
for
its

contribution
to
PTG
among
a
sample
of
youth.
Another
possible
protective
factor
is

one’s
motivation
to
improve
one’s
life
and
circumstances
that
surround
the
crisis.
In

turn,
motivation-‐to-‐improve
may
promote
aspects
of
PTG,
such
as
greater
personal

strength,
cultivating
one’s
appreciation
for
life
and
realizing
new
possibilities.

Similarly,
having
future
time
perspective
(FTP),
may
positively
encourage
the

development
of
PTG.
Generally,
a
person
with
FTP
is
one
who
has
a
relative

15

temporal
orientation
that
motivates
him/her
to
act
towards
achieving
goals
by

focusing
on
the
future
rather
than
dwelling
on
the
past
or
the
present
(Henson
et
al.,

2006).
FTP
represents
a
concept
that
has
garnered
increasing
attention
in
the

literature
as
a
potential
protective
factor
for
health
(Barnett
et
al.,
In
Press)
and
may

be
a
contributor
of
PTG.

Lastly,
a
factor
that
has
particular
relevance
to
the
CHS

youth
in
the
present
study
is
emerging
adulthood,
which
has
been
characterized
as
a

time
period
of
transition
during
which
individuals
aged
18
to
25
gain
more

autonomy,
continue
to
explore
the
direction
of
their
lives,
and
take
on
more
adult

roles
(e.g.,
finding
a
career,
getting
married)
(Arnett,
2000).
CHS
students
tend
to

take
on
adult
roles
earlier
than
their
counterparts
attending
RHSs,
as
some
enter
a

CHS
upon
becoming
a
young
parent,
after
having
taken
on
job
or
guardian

responsibilities
in
the
home
in
the
absence
of
their
parent,
or
while
cohabitating

with
a
romantic
partner
(Rohrbach,
et
al.,
2005).
Because
the
emerging
adulthood

dimensions
have
demonstrated
positive
relationships
with
resiliency
constructs

(Chassin
et
al.,
1996;
Masten,
2004)
and
may
be
similar
to
the
domains
of
PTG

(relating
to
others,
realizing
new
possibilities,
greater
personal
strength),
they
may

be
important
predictors
of
PTG
among
the
sample
in
this
study.

Also
as
theorized,
contextual
environmental
factors
(e.g.,
family
and
social

support,
community
assets)
may
influence
the
level
of
PTG
reported
by
individuals

in
the
aftermath
of
experiencing
an
SLE
(Schaefer
&
Moos,
1992;
Schaefer
&
Moos,

1998).
One
factor
with
particular
relevance
to
the
sample
of
CHS
youth
is
family

conflict.
The
presence
of
higher
levels
of
family
conflict
may
be
another
indicator
of

higher
stress
levels,
as
well
as
family
relationships
that
are
less
nurturing,
more

16

tense,
and
interpersonally
distant
(Wills,
1986;
Wills,
et
al.,
1992).
Peer
substance

use
is
an
indicator
of
peer
relationships
that
may
provide
for
less
adaptive
ways
of

dealing
with
stress,
and
thereby
inhibit
the
development
of
PTG.
Peer
substance
use

has
been
widely
established
as
a
robust
predictor
of
maladaptive
adjustment
among

adolescents
(Collins
et
al.,
1987;
MacKinnon
et
al.,
1991;
Sussman,
Dent,
&
Leu,

2000).

These
two
more
distal
Environmental
System
characteristics
may
attenuate

gaining
a
better
appreciation
of
life,
finding
strength
in
supportive
relationships,
or

finding
new
life
possibilities
as
a
result
of
SLEs.

The
first
aim
of
this
study
is
to
examine
the
influence
of
the
number
of
SLEs

and
their
relative
severity
of
stressfulness
on
the
development
of
PTG.
We

hypothesize
that
the
cumulative
stress
from
a
higher
number
of
SLEs
will
exhibit
an

inverse
relationship
with
PTG
(hypothesis
1)
while
the
severity
of
SLEs
will
exhibit
a

curvilinear
(inverted
U-‐shape)
relationship
with
PTG
(hypothesis
2).
Further,
we

hypothesize
that
general
stress
and
depression
will
negatively
impact
PTG
while

motivation-‐to-‐improve,
positive
affect,
Emerging
Adulthood,
and
Future
Time

Perspective
will
positively
impact
PTG
(hypothesis
3).
With
regard
to

Environmental
System
characteristics,
we
hypothesize
that
family
conflict
and
peer

substance
use
will
demonstrate
an
inverse
relationship
with
PTG
(hypothesis
4).

Lastly,
empirical
evidence
suggests
that
adjustment
to
stress
and
trauma
through

PTG
differs
by
race/ethnicity
(Weiss
&
Berger,
2010;
Helgeson,
et
al.,
2006),

particularly
between
Hispanics
and
non-‐Hispanics
(J.
Milam,
2006;
Powell,
Rosner,

Butollo,
Tedeschi,
&
Calhoun,
2003;
Richard
G.
Tedeschi
&
Calhoun,
2004),
but
it
is

not
clear
why.
Thus,
an
auxiliary
aim
of
this
study
is
to
replicate
previous
findings

17

by
examining
the
interactions
between
predictors
of
PTG
and
this
potential

moderator.

Method

Participants

Participants
were
enrolled
in
a
randomized
controlled
trial
of
Project

Towards
No
Drug
Abuse
(TND),
a
12-‐lesson
drug
abuse
prevention
curriculum
that

targets
youth
in
continuation
high
schools
(CHSs).
Project
TND
has
been
evaluated

in
seven
randomized
trials
that
have
shown
short
and
long-‐term
effects
on
reducing

cigarette
smoking
and
other
drug
use
among
teens
(Rohrbach,
et
al.,
2005;
Sussman

et
al.,
2012).
The
most
recent
trial
(Sussman,
et
al.,
2012)
examined
the
efficacy
of
a

booster
component
that
utilizes
motivational
interviewing
techniques.
Twenty-‐four

CHSs
were
randomly
assigned
to
one
of
three
experimental
conditions:
control,
TND

only,
or
TND
plus
motivational
interviewing
booster.
A
total
of
1704
(71.1%)
of

students
enrolled
in
classes
selected
from
the
24
CHSs
consented
to
participate
in

the
study.

Reasons
for
non-‐participation
include
parent
decline
of
consent
(0.8%),

student
decline
of
consent
or
assent
(5.1%),
or
parental
non-‐response
(23.4%).

Data
were
collected
at
baseline,
which
was
immediately
before
program

implementation,
and
at
two
follow-‐up
time-‐points
(1-‐year
and
2-‐year
follow-‐up).

The
first
follow-‐up
data
collection,
1-‐year
follow-‐up,
took
place
over
a
15-‐month

period.

The
second
follow-‐up
occurred
12
months
later.

Of
the
students
who

completed
a
survey
at
baseline,
1186
students
completed
the
1-‐year
follow-‐up

survey
(29.2%
attrition
rate)
and
703
students
completed
the
2-‐year
follow-‐up

18

survey
(58.1%
attrition
rate).
For
this
study,
the
analytic
sample
was
comprised

only
of
students
who
reported
having
experienced
a
SLE
within
the
past
two-‐years

and
answered
PTG
items
referring
to
the
SLE
(n=564).

Attrition
Analyses

To
assess
sample
attrition,
the
sample
retained
for
this
study
was
compared

to
the
group
that
was
lost-‐to-‐follow-‐up
from
baseline
to
two-‐year
follow-‐up.

The

groups
were
compared
for
all
variables
used
in
this
study
that
were
assessed
at

baseline
(9
variables)
using
the
Student
t-‐test
or
chi-‐square
test
in
order
to
detect

statistically
significant
differences
between
samples
at
the
p-‐value
alpha
of
0.05

(two-‐tailed).
The
group
retained
for
this
study
was
comparable
to
the
group
lost-‐to-‐
follow-‐up
at
two-‐year
on
all
variables
except
they
were
younger
and
more
likely
to

live
with
both
parents
at
baseline
(p<.0001).

Data
Collection

Data
were
collected
in
accordance
with
IRB
practices
at
the
University
of

Southern
California
(USC).
Informed
consent
was
obtained
from
students
who
were

at
least
18
years
of
age.
Informed
assent
procedures
were
followed,
in
addition
to

informed
consent
for
parents,
if
students
were
under
18
years
old.
Trained
data

collectors
administered
a
paper
and
pencil
survey
in
one
50-‐minute
classroom

period
at
the
baseline
and
one-‐year
follow-‐up
data
collection
sessions.
At
one-‐year

follow-‐up,
students
who
provided
consent
but
were
absent
the
day
of
survey

administration
or
had
left
the
school
received
a
telephone
call
and
were
given
the

option
to
complete
the
survey
verbally
at
that
time.
The
majority
of
students

19

completed
on-‐year
follow-‐up
surveys
by
phone
(60.5%).
For
the
two-‐year
follow-‐up

data
collection,
76.3%
of
students
completed
surveys
by
telephone,
8.8%
completed

them
in-‐person
(at
school
or
via
home
visit),
and
14.8%
completed
them
by
mail.

Measures

As
the
primary
aim
of
this
study
was
to
examine
predictors
of
PTG,
we

measure
the
majority
of
variables
(sociodemographic
variables,
general
stress,

depression,
motivation,
future
time
perspective)
prior
to
the
assessment
of
PTG.

However,
because
positive
affect
has
shown
to
be
mutable
from
adolescence
to

adulthood,
and
emerging
adulthood
assesses
ones
sense
of
independence
and

responsibility
at
a
particular
time
point,
we
measure
both
positive
affect
and

identification
with
emerging
adulthood
at
two-‐year
follow-‐up,
contemporaneous

with
the
assessment
of
PTG.

Demographics.

Socio-‐demographic
information
was
collected
at
baseline
for

age
(in
years),
gender,
race/ethnicity
categories
(Asian
or
Asian
American;
Latino
or

Hispanic;
African
American
or
Black;
White,
Caucasian,
Anglo,
European
American;

not
Hispanic;
American
Indian
or
Native
American;
Mixed:
My
parents
are
from
two

different
groups;
Other),
and
SES
(a
single
variable
reflecting
either
mother’s
or

father’s
highest
educational
attainment,
whichever
was
higher).
Other

characteristics
included
current
living
and
job
situation
(live
with
both
parents;
live

with
a
boyfriend/girlfriend/partner;
currently
married;
currently
a
parent;
have
a

job).

20

Post-‐traumatic
Growth.
The
instrument
used
to
assess
Post-‐traumatic
Growth

at
2-‐year
follow-‐up
was
based
on
an
11-‐item
Post-‐traumatic
Growth
Inventory

(PTGI),
a
modification
of
the
original
inventory
by
Tedeschi
and
Calhoun
(Tedeschi,

1995;
Tedeschi
&
Calhoun,
1996).
The
11-‐item
version
of
the
scale
has
been
used

previously
among
both
adolescent
and
adult
samples
(Arpawong
et
al.,
2012;
Milam,

2006;
Milam
et
al.,
2005;
Milam,
2004).
We
selected
8
items
from
the
11-‐item
PTGI

for
based
on
their
high
factor
loadings
on
the
first
unrotated
factor
at
or
above
0.66

with
an
Eigenvalue
of
5.44
(Eigenvalue
for
the
second
factor
was
1.40).
Participants

were
asked
to
respond
to
items
in
reference
to
the
Stressful
Life
Event
they

designated
as
most-‐life
altering
and
occurred
within
the
past
two
years.
To
avoid

the
potential
bias
from
participants
only
being
able
to
report
positive
valenced

change
that
may
have
resulted
from
their
stressful/traumatic
event,
items
were

modified
to
allow
for
response
choices
of
negative
change,
no
change,
or
positive

change
(3-‐point
scale).
A
composite
score,
averaging
responses
on
all
8
items,
was

used
for
this
study.

Internal
consistency
of
this
scale
was
high
(Cronbach

alpha=0.81).

Stressful
Life
Events
(SLEs)
and
Most
Impactful
SLE.
The
SLE
checklist

included
in
the
2-‐year
follow-‐up
survey
was
derived
from
an
abbreviated
(18-‐item)

version
of
the
Adolescent
Negative
Life
Events
Inventory
(Wills,
1986;
Wills
&

Cleary,
1996)
that
was
used
in
a
previous
study
of
adolescents
(mean
age=14.4

years
±
0.8)
(Rohrbach
et
al.,
2009).

For
the
present
study,
at
two-‐year
follow-‐up,

we
included
a
checklist
of
the
8
life
events
that
were
most
prevalently
reported

among
adolescents
in
the
Rohrbach
et
al.,
(2009)
study.

Wording
for
some
items

21

was
altered
in
order
to
be
more
relevant
to
this
older
adolescent
population
(mean

age
at
the
time
of
this
survey
=
18.8
±
9.3).
For
example,
“My
parents
had
problems

with
money”
was
changed
to
“I
did
not
have
enough
money
for
basics
(like
food)”

and
“I
had
a
lot
of
arguments
with
my
parents”
was
changed
to
“There
were
a
lot
of

arguments
that
happened
at
home.”
Participants
were
provided
with
a
checklist
of

the
8
stressful
life
events
and
asked
to
indicate
which
events
they
had
experienced

within
the
past
two
years
(1=yes
or
2=no
to
each
item).
A
ninth
question
allowed
for

participants
to
indicate
that
they
had
experienced
other
events
not
listed
in
the

checklist
with
a
free-‐entry
field
for
them
to
write
in
the
event(s).

Responses
were

summed
to
create
a
score
of
the
total
number
of
stressful
life
events
experienced

within
the
past
two
years.
Subsequently,
participants
were
asked
to
indicate
which

of
the
events
listed
(including
anything
listed
in
the
“Other”
category)
affected
their

life
the
most.

Severity
of
Stressful
Life
Event.

Development
of
the
severity
or
relative
level

of
stressfulness
score
for
the
most
impactful
SLEs
reported
by
participants
was

based
on
scores
from
the
Life
Events
for
Students
Scale
(LESS),
a
36-‐item
list
of
life

events
(Linden,
1984).

The
LESS
was
developed
with
the
premise
that
different

levels
of
coping
efforts
are
required
to
adjust
to
life
changing
events
that
are

stressful.

In
other
words,
events
ranked
at
the
top
of
the
list
(i.e.,
1
versus
36)

would
purportedly
require
a
higher
intensity-‐level
of
adjustment
to
mitigate

negative
impact
of
the
life
event.

The
LESS
represents
a
revision
of
the
Social

Readjustment
Rating
Scale
(Holmes
&
Rahe,
1967)
developed
for
adults,
although

the
LESS
includes
events
that
are
more
likely
to
occur
during
the
young
adult
or

22

emerging
adult
age
range
(e.g.,
17
to
25).
Both
scales
include
scores
attached
to
each

event
that
represent
“life
change
units”
thereby
providing
a
relative
indicator
of

severity
of
stressfulness
for
the
respective
event.

A
total
of
114
students
listed
an
SLE
in
the
“Other”
category
of
the
SLE

checklist,
perhaps
reflecting
the
uniqueness
of
this
sample,
although
41
items
were

duplicate
items
written
in
by
multiple
students
or
could
be
re-‐categorized
into
an

existing
item
on
the
original
checklist.
Thus,
73
remaining
items
plus
the
8
items
of

the
checklist
(listed
in
Figure
1)
yielded
a
total
of
81
items
to
be
scored.
From
that

list,
30
items
were
matched
to
an
item
on
the
LESS
(e.g.,
disciplined
or
suspended

from
school,
serious
illness
or
injury
in
the
family,
break-‐up
with

boyfriend/girlfriend/partner)
and
assigned
the
equivalent
severity
of
stress
score,

thereby
leaving
51
items
to
be
scored
and
ranked.

In
order
to
score
the
unmatched
SLEs,
five
independent
CHS
raters
were

recruited
from
respondents
of
the
TND
three-‐year
follow-‐up
data
collection.
These

study
participants
were
selected
at
random
to
inquire
about
their
interest
in
helping

with
a
sub-‐study.
The
first
five
students
who
responded
were
selected
for
rating

assistance,
and
provided
ratings
in-‐person.
3

Raters
were
provided
with
2
lists:
(1)
a

3

Spearman
correlation
coefficients
on
severity
ratings
from
the
five
students
ranged
from

r=0.53
to
0.73,
indicating
acceptable
to
high
agreement.
However
because
correlation

coefficients
are
limited
by
not
being
able
to
take
into
account
the
agreement
in
ordering
of

items
(versus
agreement
in
scoring),
Bland-‐Altman
plots
were
constructed
(Bland
&
Altman,

1986)
to
ascertain
the
range
of
agreement
in
rater
coding,
with
agreement
defined
as
a

mean
bias
±2
standard
deviations,
and
whether
or
not
coding
for
each
rater
would
be

retained
or
additional
raters
would
be
needed.

Additional raters were not needed because
Bland-Altman analyses indicated that 95% limits of agreement between each of the five raters
ranged between 2 standard deviations of the mean differences comparing each rater to the other
four.

23

list
of
the
matched
SLEs
including
the
respective
life
change
unit
for
each
SLE
and

(2)
the
list
of
unmatched
SLEs
that
had
not
yet
been
assigned
a
score.
Raters
were

asked
to
assign
scores
to
each
item
on
the
unmatched
SLE
list
(list
#2),
based
on

how
much
stress
s/he
felt
would
be
needed
in
adjusting
to
the
particular
SLE.
Raters

were
asked
to
anchor
their
ratings
in
accordance
with
the
life
change
units
of
the

SLEs
that
were
already
matched
(from
list
#1).
Ratings
from
all
students
were

combined
to
create
an
average
severity
score
for
each
of
the
items.

Next,
the
SLEs

were
placed
in
rank
order,
according
to
their
average
severity
score.
The
rank

number
of
each
SLE
on
the
final
list
was
used
to
create
an
ordinal
severity
score

variable.

General
stress.

This
subjective
perception
of
stress
scale,
assessed
at

baseline,
was
comprised
of
4
items
that
were
adapted
from
the
Perceived
Stress

Scale
(Cohen,
Kamarck,
&
Mermelstein,
1983;
Cohen
&
Williamson,
1988).
Items

inquired
about
how
often
in
the
past
month
the
student
had
been
upset,
felt

difficulties
were
piling
up,
out
of
control,
or
stressed.
Response
options
ranged
from

1=Never
to
5=Very
often.
A
mean
of
the
4
items
was
calculated
to
represent
a

general
perceived
stress
measure
(Cronbach’s
alpha
=
.88).

Depression.

The
measure
for
depressive
symptoms,
collected
at
baseline,
was

based
on
the
Center
for
Epidemiologic
Studies
Depression
Scale
(CES-‐D),
validated

for
use
with
adolescents
(Radloff,
1977;
Radloff,
1991;
Sheehan
et
al.,
1995).

This

measure
provides
an
indicator
of
symptoms
of
depression
over
the
past
7
days
and

not
verification
of
a
clinical
diagnosis
for
the
disorder,
according
to
the
DSM-‐IV-‐TR.

24

This
depressive
symptoms
scale
was
calculated
by
averaging
five
survey
questions

that
each
have
a
response
option
provided
on
a
4-‐point
Likert-‐type
scale.
The

internal
consistency
of
these
items
was
high
(Cronbach’s
alpha=0.90).

Positive
affect.
The
items
assessing
positive
affect
at
two-‐year
follow-‐up
were

taken
from
the
original
14-‐item
Snaith-‐Hamilton
Pleasure
Scale
(SHAPS)
(Snaith
et

al.,
1995),
developed
to
assess
four
domains
of
hedonic
experience:

interest/pastimes,
social
interaction,
sensory
experience,
and
food/drink.
The

original
items
were
asked
in
reference
to
the
participant’s
ability
to
experience

pleasure
“in
the
last
few
days.”
However,
the
items
for
this
study
were
adapted
to

assess
“ability
to
experience
pleasure
in
general.”
Each
item
of
the
scale
is
worded

such
that
a
higher
score
indicates
greater
capacity
to
experience
pleasure.

Convergent
validity
of
the
SHAPS
has
been
demonstrated
by
its
correlation
with
the

positive
affect
subscale
of
the
Positive
and
Negative
Affect
Schedule
(PANAS;

Watson,
Clark,
&
Tellegen,
1988)
through
analysis
by
Snaith
et
al
(Snaith,
et
al.,

1995).
The
three
items
were
averaged
yielding
a
scale
with
good
internal

consistency
(alpha=0.75).

Motivation
to
improve.
Three
questions
were
used
to
assess
motivation-‐to-‐
improve
at
baseline,
a
construct
that
has
demonstrated
negative
associations
with

behaviors
such
as
cigarette
smoking
(McCuller
et
al.,
2006).
Questions
were
asked

regarding
motivation
to
improve
health
generally,
the
belief
one
could
improve,
and

having
the
energy
with
which
to
do
so
with
response
options
ranging
from
1=”Very”

25

to
4=”Not
at
all”.

Items
were
averaged
yielding
a
scale
with
good
internal

consistency
(alpha=0.73).

Future
Time
Perspective
(FTP).
FTP
was
measured
at
one-‐year
follow-‐up

using
7-‐items
taken
from
the
Future
Time
Perspective
scale
of
the
Zimbardo
Time

Perspective
Inventory
(ZTPI)
(Barnett,
et
al.,
In
Press;
P.
G.
Zimbardo
&
J.
N.
Boyd,

1999;
P.G.
Zimbardo
&
J.N.
Boyd,
1999).
Students
were
asked
to
identify
how
well

the
item
describes
their
beliefs
(e.g.,
“I
finish
projects
on
time
by
working
on
them
a

little
bit
every
day,”
“It
upsets
me
to
be
late
for
school
or
other
commitments,”
“I

keep
working
at
difficult,
boring
tasks
if
they
will
help
me
get
ahead”)
using
5-‐point

Likert
scales.

Cronbach’s
alpha
for
the
scale
was
.83.

Emerging
Adulthood.

This
was
assessed
with
items
from
the
Inventory
of
the

Dimensions
of
Emerging
Adulthood
(IDEA;
Reifman,
Arnett,
&
Colwell,
2003).
The

original
IDEA
is
a
31-‐item
instrument
used
to
assess
six
dimensions
that

characterize
the
period
of
emerging
adulthood:
time
of
identity
exploration,

experimentation,
possibility,
self-‐focus,
other-‐focus,
and
feeling
in-‐between.

Each

question
item
is
assessed
on
a
4-‐point
Likert
scale.

At
the
two-‐year
follow-‐up,
a
10-‐
item
version
of
the
scale
was
used,
including
items
selected
for
their
highest

loadings
in
exploratory
factor
analysis
(EFA)
to
represent
4
dimensions:

experimentation,
self-‐focused,
identity
exploration,
and
feeling-‐in-‐between.
A

principal
components
EFA
on
the
present
sample
showed
that
three
factors

emerged
although
the
internal
consistency
of
factors
two
and
three
was
not

acceptable
(Cronbach
alphas
were
.70,
42,
and
.44,
respectively)
(Lisha
et
al.,
2012).

26

Thus,
a
maximum
likelihood
EFA
was
conducted,
in
which
all
10
items
loaded
onto
a

single
factor.
The
10-‐item
one
factor
solution
showed
good
internal
consistency

(Cronbach’s
alpha
=
.79).
Thus,
emerging
adulthood
was
used
as
a
unitary
scale,

calculated
from
an
average
of
the
10
items.

Family
Conflict.
This
5-‐item
subscale,
assessed
at
baseline,
was
derived
from

the
original
scale
by
Bloom
(Bloom,
1985).

Items
on
the
subscale
assess

characteristics
of
family
functioning,
such
that
a
student
rates
how
well
a
statement

describes
their
family
(i.e.,
“Family
members
sometimes
get
so
angry
they
throw

things,”
“Family
members
rarely
criticize
each
other”).
The
items
were
averaged
to

create
a
mean
score
with
an
acceptable
internal
consistency
(Cronbach’s
alpha
=

.64).

Peer
Substance
Use.
Peer
substance
use
is
a
well-‐established
indicator
of

maladaptive
adjustment
by
its
relationship
with
poor
health-‐related
behaviors

(Sussman,
Dent,
&
McCullar,
2000).
Four
items
were
used
at
baseline
to
assess
each

of
the
subcategories
of
substance
use
among
peers:
cigarettes,
alcohol,
marijuana,

and
hard
drugs.
The
four
items
were
summed
yielding
a
scale
with
high
internal

consistency
(Cronbach’s
alpha=0.85).

Study
Condition.
A
covariate
was
included
in
order
to
control
for
study

condition
to
which
students
were
assigned.
Because
this
study
did
not
assess
effects

of
the
intervention,
and
previous
studies
have
not
shown
differences
in
substance

use
outcomes
between
the
two
intervention
conditions
(see
Sussman,
et
al.,
2012),

27

the
variable
for
study
condition
was
dichotomously
coded
as
TND-‐any
(either

intervention
arm)
or
Control.
4

Statistical
Analysis

All
analyses
were
performed
using
the
SAS
(v.9.3)
statistical
package.
PTG

was
evaluated
for
normal
distribution
and,
due
to
scores
being
negatively
skewed,

PTG
scores
were
reflected
to
a
positive
skew,
log-‐transformed,
and
reflected
again

to
restore
the
original
order
of
values
for
all
analyses.
Correlation
coefficients
were

calculated
between
key
variables
in
order
to
determine
zero-‐order
relationships.
To

avoid
problems
of
multicollinearity
of
Personal
and
Environmental
System

characteristics,
an
analyses
of
the
strength
of
relationships
showed
that
tolerance

levels
were
between
0.67
to
0.97
and
variance
inflation
levels
(VIF)
between
1.04

and
1.51.
Neither
posed
a
problem
for
the
analyses.
Means,
standard
deviations,
and

frequencies
for
selected
demographic
characteristics
and
key
variables
were

calculated.
Because
of
insufficient
numbers
in
the
race
categories
other
than

Hispanic
(35%),
race/ethnic
categories
were
recoded
to
Hispanic
or
non-‐Hispanic.

Sociodemographic
variables,
variables
assessing
the
number
and
severity
of

SLEs
and
Personal
System
and
Environmental
System
characteristics
were
entered

into
a
series
of
hierarchical
multi-‐level
regression
models
(PROC
MIXED).

All

models
were
run
as
multi-‐level
models,
with
students
nested
in
schools,
to

statistically
control
for
the
possibility
that
students
within
schools
are
more
similar

than
students
across
schools.
The
PTG
score,
a
log-‐transformed
continuous
variable,

4
Mean PTG scores did not significantly differ between treatment conditions (p=.85).

28

was
entered
as
the
dependent
variable
for
all
analyses.
In
order
to
test
Hypothesis
2

regarding
a
curvilinear
relationship
between
severity
of
SLEs
and
PTG,
the
variable

for
severity
of
SLEs
was
first
centered,
then
squared
to
create
the
quadratic
term,

and
then
both
the
linear
and
quadratic
terms
were
entered
into
the
regression

model
predicting
PTG
(Cohen,
1978;
Stimson,
Carmines,
&
Zeller,
1978).
All
models

included
treatment
condition
as
a
control
variable,
as
well
as
relevant

sociodemographic
variables
commonly
associated
with
PTG
(i.e.,
age,
gender,

race/ethnicity).

In
order
to
address
the
question
of
whether
gender
or
race/ethnicity

moderates
the
relationship
between
key
variables
and
PTG,
9
interaction
terms

were
created
between
each
moderator
and
each
personal
and
environmental

system
characteristic
according
to
methodology
outlined
by
Frazier
(Frazier,
Tix,
&

Barron,
2004b).
All
interaction
terms
concerning
gender
were
entered
into
a
single

model,
along
with
each
main
effect,
to
determine
its
relationship
to
PTG.
None
were

significant
at
the
p<.05
level.
Next,
all
interaction
terms
concerning
Hispanic

ethnicity
were
entered
into
a
single
model
along
with
each
main
effect
to
determine

its
relationship
to
PTG.
Only
one
interaction,
between
Hispanic
ethnicity
and
future

time
perspective
was
significant
at
the
p<.05
level.

Slopes
between
regression
lines

representing
the
relationships
between
Future
Time
Perspective
and
PTG
for
the

two
levels
of
ethnicity
were
compared
(Frazier,
Tix,
&
Barron,
2004a;
Aiken,
West,
&

Reno,
1991),
shown
in
Figure
2.

29

To
address
the
first
study
aim,
Model
1
was
run
with
age,
gender,
ethnicity,

and
parents’
education
(proxy
for
SES)
as
correlates.
To
address
the
second
study

aim,
Model
2
added
the
SLE
variables
(number
of
SLEs
experienced
in
the
past
two

years,
severity
of
stressfulness
of
the
most
life-‐altering
SLE
that
happened
within

the
past
two
years,
as
well
as
the
quadratic
variable
for
severity
of
stressfulness
to

test
the
curvilinear
relationship)
to
Model
1.
To
address
the
third
study
aim,
Model
3

added
Personal
System
characteristics
(general
stress,
depression,
motivation
to

improve,
positive
affect,
emerging
adulthood,
future
time
perspective)
and
the

significant
interaction
term
between
Hispanic
ethnicity
and
future
time
perspective

to
Model
2.
To
address
the
fourth
study
aim,
Model
4
added
Environmental
System

characteristics
(family
conflict,
peer
substance
use)
to
Model
3.
Lastly,
because
the

initial
models
were
exploratory
in
the
process
of
stepwise
regression,
a
final
model

was
constructed
retaining
all
control
variables
as
well
as
the
significant
predictors

at
a
p<.10
level
(Sun,
Shook,
&
Kay,
1996)
from
Model
4.

Results

Participant
Characteristics

Table
1
provides
the
demographic
characteristics
for
the
sample
(n=564).

Slightly
more
than
half
of
the
participants
were
male
(54%)
and
were
living
with

both
parents
(53%).
The
majority
of
participants
had
a
parent
who
completed
high

school
(65%)
and
self-‐identified
as
Latino
or
Hispanic
(65%).

Stressful
Life
Events,
Personal
and
Environmental
System
Characteristics

30

Table
2
provides
means,
standard
deviations,
and
ranges,
for
correlates
and

predictors
of
PTG
examined
in
this
study.
The
average
number
of
SLEs
reported
to

have
occurred
in
the
last
two
years,
per
individual
was
3.14
(SD=1.7).
During
the

two-‐year
period,
20%
of
the
sample
reported
having
experienced
between
1
and
3

SLEs
while
17%
reported
experiencing
4;
13%
reported
experiencing
5;
6%

reported
experiencing
6;
3%
reported
experiencing
7;
and
1%
reported

experiencing
8
or
more
SLEs
(data
not
shown).

The
general
types
of
SLEs
reported
by
participants
are
shown
in
Figure
1,

listed
in
order
of
prevalence.
Examples
of
SLEs
reported
in
the
“Other”
category

included
having
a
miscarriage,
joining
the
Army,
moving
away
from
home,
death
of
a

grandparent
or
cousin,
a
friend
passing
away,
getting
caught
stealing,
getting
jaw

broken
in
a
fight,
found
out
that
little
sister
was
deaf,
putting
cat
to
sleep,
separating

from
adopted
family,
car
getting
burglarized,
OD
on
acid
and
almost
died,
parents’

drug
use,
might
have
borderline
personality
disorder,
got
shot
at,
got
restraining

order
against
mom,
got
my
wallet
stolen,
quitting
drugs,
left
school
to
avoid
peer

pressure
of
drug
use,
did
not
want
to
mention,
among
others.
The
items
remained
in

the
umbrella
category
of
“Other”
rather
than
being
re-‐categorized
because
fewer

than
5
participants
reported
any
of
the
same
events
within
the
two-‐year
period.

Overall,
the
majority
of
SLEs
reported
fell
into
the
category
of
“Someone
in
my

family
had
a
serious
illness,
accident,
or
injury.”
However,
of
those
who
reported
an

SLE
in
this
category,
less
than
half
(40.3%)
indicated
the
event
was
the
most
life-‐
altering
of
all
SLEs
reported
in
the
past
two
years.

The
category
with
the
lowest

prevalence
in
the
last
two
years
was
“I
was
a
victim
of
a
violent
or
abusive
crime”.

31

However,
of
those
who
reported
an
SLE
in
this
category,
almost
a
third
(31.7%)

reported
that
SLE
had
been
most
life-‐altering
for
them
compared
to
any
other
SLEs

reported.

Post-‐traumatic
Growth

PTG
was
reported
only
among
those
who
reported
experiencing
an
SLE

within
the
past
two
years.
Participants
were
asked
to
respond
to
PTG
items
in

regard
to
the
SLE
they
designated
as
the
most
life-‐altering
among
all
SLEs
reported.

The
majority
of
students
reported
that
some
aspect
of
their
life
had
improved
in

their
lives
in
the
aftermath
of
having
experienced
an
SLE
with
the
mean
PTG
score
of

2.64
(SD=0.38;
range
1-‐3)
for
the
entire
sample.
Almost
a
third
of
the
students

reported
positive
changes
on
all
PTG
items
(31.7%)
while
0.18%
reported
only

negative
changes;
and
3.6%
reported
no
changes
on
all
items
(data
not
shown).

All

students
reported
positive
change
on
at
least
1
PTG
item;
25.4%
reported
negative

change
on
at
least
1
item;
and
67.6%
reported
no
change
on
at
least
1
item.

Regression
Analyses

As
shown
in
Table
3,
Model
1
demonstrated
that
demographic
characteristics

did
not
significantly
predict
PTG,
with
parents’
level
of
education
being
borderline

significant
(p<.10).

However,
these
variables
were
retained
in
all
subsequent

models
due
to
their
empirical
association
with
PTG
(see
reviews
by
Helgeson,
et
al.,

2006;
Linley
&
Joseph,
2004;
Meyerson
et
al.,
2011).
Model
2
demonstrated
that
the

number
of
stressful
life
events
experienced
was
inversely
related
to
PTG
whereas

there
was
no
relationship
between
severity
of
SLE
and
PTG.
Comparing
the
variance

explained
by
both
the
linear
and
quadratic
terms,
adding
the
quadratic
term
for

32

severity
of
SLE
did
not
account
for
any
additional
proportion
of
variance
in
PTG.
As

this
is
the
recommended
method
for
evaluating
the
elimination
of
quadratic
terms

in
regression
models
(Cohen,
1978;
Stimson,
et
al.,
1978),
the
term
was
excluded

from
remaining
models.
Thus,
adding
the
variable
of
number
of
SLEs
in
Model
2

explained
an
additional
2%
of
the
variance
in
PTG
beyond
the
3%
variance

explained
by
sociodemographic
variables
from
Model
1.
Model
3
demonstrated
that

more
strongly
identifying
with
being
in
the
stage
of
emerging
adulthood

significantly
predicted
PTG
(p<.05)
while
less
general
stress,
greater
positive
affect,

and
the
interaction
between
Hispanic
ethnicity
and
FTP
approached
significance

(p<.10)
in
predicting
PTG.
These
Personal
System
characteristics
added
explained

an
additional
8%
of
the
variance
in
PTG.
Model
4
demonstrated
that
Environmental

System
characteristics
did
not
predict
PTG,
nor
did
they
explain
any
additional

variance
in
PTG.
The
final
model
was
constructed
with
predictors
that
explained
a

total
of
13%
of
the
variance
in
PTG,
with
higher
PTG
being
predicted
by
fewer
SLEs

experienced,
less
general
stress,
a
higher
score
on
the
emerging
adulthood
scale,

and
the
interaction
between
Hispanic
ethnicity
and
FTP.
5

Positive
affect
was

borderline
significant
in
predicting
PTG
in
the
final
model.

Moderation

Interaction
terms
were
created
for
all
possible
variables
with
Hispanic

ethnicity.
The
only
significant
interaction
was
Hispanic
ethnicity
x
FTP,
diagrammed

in
Figure
1.
The
moderation
analysis
indicates
that
there
is
no
difference
in
the

5
Multi-level models were run, predicting PTG as the dependent variable. Intraclass correlation of
individual PTG scores that were nested within schools is .007, resulting in a VIF=1.17 and
DEFT=1.08. Thus, standard errors using multi-level models are corrected for, yielding SEs that
are 1.08 times higher than if traditional OLS models had been run.

33

pattern
of
predictors
for
PTG
by
Hispanic
ethnicity
in
this
sample.
However,
because

one
interaction
term
was
significant,
it
was
added
into
Model
3
as
a
predictor.
When

this
interaction
term
was
added
into
Model
3,
the
main
effect
between
FTP
and
PTG

was
no
longer
significant,
yet
the
interaction
remained
significant.

Discussion

Positive
psychosocial
growth
in
the
aftermath
of
having
experienced
a
highly

stressful
event
may
help
to
mitigate
long-‐term
psychological
and
emotional
distress.

No
prior
studies
have
examined
the
extent
to
which
PTG
is
reported
among
high-‐
risk,
older
youth.
Furthermore,
correlates
and
predictors
of
PTG
have
not
been

assessed
in
an
emerging
adult
sample.

In
particular,
assessing
the
impact
of
SLEs
on

PTG
among
emerging
adults
is
important
because
the
novelty
of
particular
events

faced
during
this
time
period
may
require
more
intensive
psychological
adjustment

than
for
older
adults.

As
the
development
of
PTG
varies
across
samples
and
types
of

SLEs,
the
present
study
fills
a
gap
in
knowledge
regarding
the
contributors
to
the

variation
in
PTG,
particularly
among
older
youth.

On
average,
students
reported
having
experienced
3
or
more
SLEs
in
the
past

two
years.
As
we
do
not
have
comparison
data,
it
is
unclear
whether
this
represents

a
norm
for
other
students
of
the
same
age
range.
However,
students
who
attend

CHSs
tend
to
exhibit
more
difficult
living
situations
than
their
counterparts
who

attend
RHSs,
including
emotional
and
physical
abuse
or
victimization,
loss
of
a

parent,
becoming
a
parent,
cycling
in-‐and-‐out
of
foster
care,
being
a
witness
to

violence,
or
other
occurrences
that
cause
them
to
feel
disconnected
from

mainstream
society
(Zweig
&
Institute,
2003).
Because
20%
of
students
opted
to

34

disclose
SLEs
that
were
not
included
on
the
survey
checklist,
instead
writing
in
their

own
item,
this
suggests
that
a
broader
checklist
containing
items
that
would
better

capture
events
occurring
in
the
lives
of
high-‐risk
emerging
adults
may
be
warranted

in
future
studies
(see
Cleary,
1981).
Although
some
researchers
argue
that

checklists
should
provide
a
general
categories
versus
a
detailed
list
of
events
(e.g.,

provide
the
category
of
“marriage”
versus
listing
“planning
a
wedding”,
“moving
in

with
spouse”;
see
Dohrenwend,
2006),
it
is
possible
that
if
checklist
items
were
not

as
specific,
students
may
not
have
reported
all
events
either
because
they
did
not

want
to
or
did
not
think
of
it
at
the
time.
Regardless,
providing
a
write-‐in
response

option
proved
useful
for
capturing
a
broad
range
of
SLEs
experienced
by

participants.

Overall,
the
majority
of
these
CHS
youth
reported
having
garnered
positive

change
resulting
from
their
SLE.

Adolescence,
and
particularly
emerging
adulthood,

characterizes
a
unique
time-‐point
in
development
and
carries
associated
stressors

(Arnett,
2000).

Authors
of
one
review
of
25
studies
conducted
among
children
and

adolescent
samples
noted
that
because
older
youth
begin
to
establish
their
own

cognitive
schemata
that
although
may
be
more
vulnerable
to
the
impact
of
SLEs,
it
is

possible
they
more
fluidly
integrate
meaning
of
the
SLE
into
their
post-‐trauma

identity,
and
thus
develop
greater
PTG
compared
to
children
or
adults
(Meyerson,
et

al.,
2011).
As
this
represents
the
first
study
on
levels
of
PTG
reported
among
older

youth,
future
studies
are
needed
to
assess
the
variation
in
levels
of
PTG
reported

across
age
groups.

35

Findings
regarding
the
number
of
SLEs
indicate
that
the
fewer
SLEs

experienced
in
the
past
2
years
is
positively
related
to
PTG,
supporting
Hypothesis

1.
Interestingly,
the
number
of
SLEs
reported
was
significantly
related
to
the

increasing
level
of
severity
of
the
most
life-‐altering
SLE.
This
may
be
due
to
the
fact

that
those
who
have
experienced
a
SLE
that
was
rated
higher
in
stress
severity
were

also
more
likely
to
experience
other
SLEs
that
were
related
to
the
initial
event.
For

example,
one
who
experiences
death
of
a
parent
or
guardian
may,
as
a
result,

experience
financial
difficulties,
changes
in
living
situations,
or
changes
in

relationship
status
as
a
result.

Thus,
the
number
of
SLEs
in
this
study
may
serve
as

one
indicator
of
cumulative
distress
that
these
vulnerable
youth
experience.
An

alternative
explanation
may
be
that
life
circumstances
have
predisposed
these
CHS

youth
to
experience
SLEs
in
both
greater
severity
and
quantity.
Future
studies
that

assess
dates
of
SLE
occurrence
as
well
as
use
a
comparison
group
of
RHS
youth
may

help
to
decipher
this.

Findings
regarding
severity
of
the
most
life-‐altering
SLE
showed
that
neither

the
linear
nor
quadratic
relationship
between
severity
of
SLEs
and
PTG
were

significant.

Thus,
Hypothesis
2,
in
which
a
curvilinear
relationship
(inverted
U-‐
shape)
between
severity
of
SLE
and
PTG
was
proposed,
was
not
supported.
A

possible
explanation
for
this
is
that
the
severity
rating
for
SLEs
represents
an

objective
rating
of
severity
of
SLE
and
not
a
perceived
severity
score
designated
by

the
participants
themselves.

The
importance
of
this
distinction
partly
rests
on
the

notion
that
SLEs
may
elicit
different
stress
reactions
from
individuals
based
on

certain
variables
such
as
the
chronicity
or
novelty,
the
proximal
nature
of
the

36

stressor,
other
stressors,
personal
and
contextual
variables.

For
example,
the
break-‐
up
of
a
relationship
may
require
different
levels
of
psychosocial
adjustment

depending
on
variables
such
as
this
being
an
individual’s
first
versus
fifth

experience
of
a
break-‐up,
the
intensity
of
the
relationship,
who
initiated
the
break-‐
up,
social
support
received
in
the
wake
of
the
break-‐up,
among
other
variables.

These
factors
would
impact
the
severity
rating
of
the
SLE
as
well
as
the
relationship

between
severity
and
PTG.

Therefore,
a
measure
of
perceived
severity
should
be

considered
for
future
studies
attempting
to
characterize
the
relationship
between

severity
and
PTG.

Looking
beyond
the
impact
of
stress
on
PTG,
the
Personal
System

characteristic
that
positively
predicted
PTG
in
the
final
model
was
Emerging

Adulthood.
In
contrast,
depression
and
motivation
did
not
predict
PTG,
while

positive
affect
was
borderline
significant.
Thus
Hypothesis
3
was
only
partially

supported.
With
respect
to
Emerging
Adulthood,
this
study
provides
novel
evidence

that
this
stage
may
positively
coincide
with
components
of
positive
psychological

growth.
While
emerging
adults
may
be
more
pessimistic
about
society,
they
may

also
be
more
optimistic
about
their
own
goals,
prospective
relationships,
and
job

opportunities;
thus
they
perceive
their
lives
as
being
ripe
with
possibilities
(Arnett,

2004;
Hornblower,
1997;
Lisha,
et
al.,
2012).
With
such
conceptual
overlap
in

components
encompassed
by
PTG,
it
would
be
surprising
to
find
no
relationship

between
the
two.

Regarding
the
lack
of
relationship
between
pre-‐existing
depression
and
PTG,

this
finding
is
not
surprising
given
previous
studies
that
also
reported
null
findings

37

(Arpawong,
et
al.,
2012;
Cordova
et
al.,
2001;
Milam,
et
al.,
2004).
In
contrast,
the

weak
relationship
between
positive
affect
and
PTG
was
unexpected
as
previous

studies
among
younger
children
and
adults
have
shown
a
positive
relationship

(Currier,
Hermes,
&
Phipps,
2009;
Helgeson,
et
al.,
2006).

The
lack
of
relationship

between
PTG
and
depression
and
weak
relationship
between
PTG
and
positive

affect
in
this
study
suggest
that
the
development
of
PTG
may
not
be
dependent
upon

mood
states
among
older
at-‐risk
youth.

Rather,
their
stage
of
life
may
be
more

pertinent
to
positive
psychological
growth
than
mood.
Similarly,
the
lack
of

relationship
between
family
conflict
and
peer
substance
use
may
be
due
to
their

being
more
distal
to
other
variables
that
are
more
relevant
to
PTG.
For
example,

family
conflict
may
not
explain
any
additional
variance
in
PTG
after
having
added

general
stress
to
the
model.
Similarly,
peer
substance
use
may
not
have
explained

any
additional
variance
in
PTG
after
SLE
variables
had
already
been
added.
Thus,

these
findings
suggest
that
these
contextual
variables
may
be
less
relevant
when

predicting
PTG
among
older
youth.
Future
studies
may
consider
including
other

contextual
variables,
such
as
levels
of
family
and
social
support,
that
have
been

shown
to
positively
predict
PTG
among
other
young
samples
(Meyerson,
et
al.,

2011).

Lastly,
our
exploratory
aim
yielded
one
significant
interaction
in
the

prediction
of
PTG.
Both
variables,
being
of
Hispanic
ethnicity
and
FTP
encompass

factors
that
make
conceptual
sense
in
their
contribution
to
PTG.
First,
it
is
consistent

with
several
other
studies
conducted
among
adolescents
(e.g.,
Milam,
et
al.,
2005;

Milam,
2006;
Powell
et
al.,
2003;
Tedeschi
&
Calhoun,
2004;),
that
Hispanics

38

generally
report
higher
levels
of
PTG
compared
to
non-‐Hispanics
(depicted
in
Figure

2).
Despite
there
being
a
high
level
of
diversity
within
the
Hispanic
grouping,
given

historical
experiences
and
migration
background,
there
tend
to
be
common
cultural

characteristics,
norms,
and
values.
Those
characteristics,
norms,
and
values
may
be

driving
the
interaction
between
Hispanics
and
FTP.

The
highest
levels
of
PTG

reported
among
Hispanics
who
also
have
high
levels
of
FTP
may
be
due
to
the
fact

that
Hispanics
conceptualize
their
own
goals
and
commitments
through
a
family

lens
rather
than
solely
with
respect
to
the
individual.
Item-‐level
analysis
indicate

Hispanics
identify
better
than
non-‐Hispanics
with
the
FTP
item
that
inquires
about

wanting
to
meet
obligations
to
friends,
parents,
teachers
and
other
authority
figures.

Similarly,
Hispanics
identified
better
with
the
statement
“Finishing
homework
and

doing
other
jobs
at
home
comes
before
play.”
Overall,
Hispanics
who
reported
lower

levels
of
FTP
still
reported
higher
levels
of
PTG
compared
to
non-‐Hispanics,
yet
at

lower
levels
than
Hispanics
with
high
FTP.
This
may
be
due
to
the
cultural
norm
of

relying
less
on
seeking
independence
during
this
life
stage
than
non-‐Hispanics,

rather
relying
on
family
and
spiritual
values
upon
making
decisions
for
their
future,

and
thus
constructing
a
life
narrative.
This
could
result
in
Hispanics
reporting

higher
PTG
in
the
areas
of
relating
to
others
and
spiritual
change
following
SLEs

(Berger
&
Weiss,
2010;
Skogrand,
Hatch,
&
Singh,
2005).

Nevertheless,
that
PTG

may
be
influenced
by
both
cultural
interpretations
of
change
in
the
aftermath
of

SLEs
and
unique
perspectives
during
the
developmental
stage
of
Emerging

Adulthood,
research
may
benefit
from
further
study
that
characterizes
these
culture

and
stage
specific
influences
on
psychological
growth.

39

Limitations

One
limitation
of
this
study
concerns
self-‐disclosure
of
SLEs.
Some

participants
may
not
have
disclosed
the
more
traumatic
SLEs.
For
all
students

surveyed
at
two-‐year
follow-‐up
time
point,
80.2%
reported
having
experienced
an

SLE
and
thus
were
included
in
the
analytic
sample
for
this
study.
It
is
unknown
why

19.8%
of
the
sample
did
not
report
any
SLEs,
either
because
they
truly
did
not

experience
any
or
were
reluctant
to
report
them.
Future
research
may
consider

using
different
methods
for
inquiring
about
SLEs,
such
as
a
Traumatic
Life
Event

Questionnaire
(TLEQ)
which
includes
more
descriptive
categories
of
life
events
(e.g.,

physical
abuse
by
an
intimate
partner,
being
stalked,
robbery
involving
a
weapon,

being
threatened
with
death
or
serious
bodily
harm;
see
Kubany
et
al.,
2000).
A

second
limitation
is
the
specific
time
point
at
which
the
most
life-‐altering
SLE

occurred
was
not
assessed.
We
assessed
SLEs
that
occurred
anytime
within
the
past

two-‐years.

As
some
evidence
shows
that
the
age
at
the
time
of
SLE
occurrence
may

impact
the
level
of
PTG,
future
studies
may
wish
to
include
a
variable
that
assesses

age
at
the
time
the
event
occurred
rather
than
using
age
at
time
of
assessment
in

order
to
determine
if
age
at
event
is
a
stronger
predictor
of
PTG.

Third,
the

additivity
of
events
(sum)
may
not
adequately
reflect
the
cumulative
level
of
stress

experienced
over
a
finite
period
of
time
as
some
events
may
be
insignificant
with

regard
to
distress
levels
whereas
others
that
may
be
pivotal.
However,
in
this
study,

PTG
was
assessed
with
regard
to
the
SLE
participants
thought
was
most
life-‐
altering.
Also,
as
previously
mentioned,
the
relationship
between
severity
of
SLE

and
PTG
may
be
better
characterized
by
including
a
perceived
severity
variable

40

obtained
from
the
participants
themselves.

Lastly,
it
is
possible
that
mean
PTG

scores
in
this
sample
were
higher
than
would
be
reported
among
other
CHS
youth

because
of
an
impact
of
the
substance
use
intervention.
Although
this
study
was
not

designed
to
assess
what
components
of
the
intervention
may
have
influenced
PTG

among
the
sample,
results
of
this
study
provide
promising
evidence
that
CHS
youth

may
be
adapting
relatively
well
to
the
stressful
events
that
arise
from
living
in

higher-‐risk
and
higher
stress
environments.

Implications
and
Future
Research
Directions

Despite
limitations,
one
novelty
of
this
study
is
the
inclusion
of
high-‐risk

youth.
Prior
research
has
shown
that
social
environmental
risk
factors
may

exacerbate
impact
of
SLEs
on
health
outcomes
for
youth
(e.g.,
feeling
marginalized,

socially
isolated,
and
socioeconomic
status
inconsistencies;
Caplan,
1974).

However,
evidence
from
this
study
demonstrates
that
youth
who
are
more
likely
to

experience
these
social
risk
factors
may
be
adapting
quite
well
psychologically,

despite
experiencing
a
number
of
acutely
stressful
events.

Because
multiple
studies

demonstrate
negative
psychological
effects
of
cumulative
stress
from
experiencing

multiple
SLEs,
implications
of
these
findings
are
that
the
development
of
PTG
may

develop
in
contrast
to
the
deleterious
mental
health
effects
of
accumulated
stress.

Further
studies
need
to
assess
the
long-‐term
impact
of
PTG
on
mitigating
those

deleterious
outcomes
(e.g.,
depression,
anxiety,
post-‐traumatic
stress)
among

vulnerable,
older
youth.
As
this
study
attests,
one
factor
that
may
be
contributing
to

PTG
is
developmental
stage
while
another
may
be
having
future
time
orientation,

particularly
for
Hispanics.
Thus,
one
future
research
direction
includes
examining

41

the
relationship
between
PTG
and
indicators
of
developmental
stage
among
high-‐
risk
samples
with
a
broader
range
of
age
categories,
using
a
prospective
study

design
in
order
to
assess
how
changing
developmental
stage
impacts
the
level
of

PTG
reported.
Other
directions
for
this
research
include
examining
factors
that

indicate
a
perception
of
social
status
and
marginality,
social
support,
coping
skills,

cognitive
processing,
ethnic
and
cultural
differences,
perceived
severity
of
SLEs,
and

time
since
SLE
as
possible
mediators
between
the
SLE
and
PTG
are
warranted
for
a

clearer
understanding
how
PTG
develops
in
more
vulnerable
groups.

In
conclusion,
theoretical
frameworks
posit
that
PTG
develops
from
some

definable
life
events
although
it
is
possible
that
PTG
develops
through
the
chronicity

of
stress
and
life
circumstances
relating
to
the
SLE.

For
instance,
the
break-‐up
of
a

relationship
itself
may
be
the
eliciting
event
cited
yet
the
social
support
and
changed

relationships
with
others
in
the
aftermath
of
the
break-‐up
may
be
the
cause
for

developing
PTG.
Thus,
although
many
problems
have
been
noted
with
using
life

events
checklists
to
simply
evaluate
the
occurrence
of
a
life
event,
or
the
number
of

events
experienced,
this
method
is
limited
in
the
information
that
can
be
gleaned.
It

is
the
interpretation
of
the
event
impact
on
some
aspect
of
well-‐being
that
provides

more
usable
data
for
researchers.
Findings
from
this
study
attest
that
older
youth

are
able
to
attribute
aspects
of
positive
change
in
their
lives
to
specific
SLEs
and

certain
personality
variables
may
better
predict
the
development
of
PTG
in

response
to
a
life
stressor.

42

Table
1.
Selected
Sample
Characteristics
(n=564)

Variable

%
or

Mean
(SD)

Gender

Male
54.4

Female
45.6

Age
at
2-‐year
follow-‐up
(range
16-‐22
years)
18.78
(0.90)

Race/Ethnicity

Asian
or
Asian
American
2.9

Latino
or
Hispanic
65.3

African
American
or
Black
3.4

White,
Caucasian,
Anglo,
European
American;
not
Hispanic
11.9

American
Indian
or
Native
American
0.4

Mixed:
My
parents
are
from
two
different
groups
14.5

Other
1.6

Highest
education
completed
by
either
mother
or
father

Did
not
complete
8th
grade
9.6

Did
not
complete
high
school
(12th
grade)
25.0

Completed
high
school
(received
a
diploma)
26.5

Some
college
or
job
training
(1
to
3
years)
20.2

Completed
college
(4
years)
13.9

Attended
or
completed
graduate
school
(Doctor,
Lawyer)
4.8

Post-‐traumatic
Growth
2.64
(0.38)

Notes.

Age,
peer
substance
use,
number
of
stressful
life
events,
and
Post-‐traumatic
Growth

were
assessed
at
2-‐year
follow-‐up.
All
other
variables
were
assessed
at
baseline.

43

Table
2.
Correlates
and
Predictors
of
Post-‐Traumatic
Growth

Variable
Mean
(SD)
Range

Stressful
Life
Events
(SLEs)

Number
of
SLEs
3.14
(1.70)
1-‐8

Severity
of
SLE
that
was
most
life-‐
altering
a

27.08
(13.94)
1-‐60

Personal
System
Characteristics

General
Stress
3.01
(1.05)
1-‐5

Depression
1.78
(0.90)
1-‐4

Motivation
to
Improve
3.39
(0.62)
1-‐4

Positive
Affect
9.86
(1.49)
3-‐12

Emerging
Adulthood
3.57
(0.39)
1-‐4

Future
Time
Perspective
3.65
(0.87)
1-‐5

Environmental
System
Characteristics

Family
Conflict
2.99
(0.59)
1-‐4

Peer
Substance
Use
3.71
(1.55)
1-‐6

Notes.

a

Value
indicates
1=most
stressful
to
60=least
stressful

44

Table
3.
Regression
Models
for
the
Associations
of
Socio-‐Demographic

Characteristics,
Stressful
Life
Events,
Personal
System
and
Environmental
System

Characteristics
with
Posttraumatic
Growth

Variable
β

SE
R
2

Model
1:
Demographics

0.03

Age
0.010

0.05

Female
0.096

0.09

Hispanic
Ethnicity
0.151

0.10

Parents'
education

0.064
#
0.04

Model
2:
Add
Stressful
Life
Events
(SLEs)

0.05

Number
of
Stressful
Life
Events
-‐0.090
***
0.02

Severity
of
SLE
0.001

0.00

Quadratic
Severity
of
SLE
0.000

0.00

Model
3:
Add
Personal
System
Characteristics

0.13

General
stress
-‐0.097
#
0.05

Depression
-‐0.025

0.06

Motivation
to
improve
0.103

0.08

Positive
Affect
0.055
#
0.03

Emerging
Adulthood
0.401
**
0.13

Future
Time
Perspective
0.011

0.09

Hispanic
x
Future
Time
Perspective
0.183
#
0.11

Model
4:
Add
Environmental
System
Characteristics

0.13

Family
conflict
0.022

0.08

Peer
substance
use
0.007

0.03

Final
Model:
Predicting
Post-‐traumatic
Growth

0.13

Age
-‐0.065

0.05

Female
0.101

0.09

Hispanic
Ethnicity
0.090

0.10

Parents'
education

0.034

0.04

Number
of
Stressful
Life
Events
-‐0.090
***
0.03

General
stress
-‐0.108
*
0.04

Positive
Affect
0.053
#
0.03

Emerging
Adulthood
0.427
***
0.12

Future
Time
Perspective
0.026

0.09

Hispanic
x
Future
Time
Perspective
0.187
*
0.11

Notes.

All
models
include
age,
gender,
Hispanic
ethnicity,
parents'
education,
and
treatment

condition
as
control
variables.
All
continuous
variables
were
standardized
to
the
global

mean.
Model
1
F
=
3.51**;
Model
2
F
=
4.53***;
Model
3
F
=
5.60***;
Model
4
F
=
5.36***;

Final
Model
F
=
6.91***.

#
p
<
.10;
*
p
<
.05;
**
p
<
.01;
***
p
<
.001

44

Figure
1.

Frequencies
of
Stressful
Life
Events
Reported

Categories
of
Stressful
Life
Events
(SLEs)
are
listed
in
order
of
highest
to
lowest
prevalence
with
the
“Other”
category

capturing
a
range
of
SLEs
that
were
written
in
by
the
participant.
The
percentage
of
students
reported
having
experienced
an

SLE
in
the
past
two
years,
in
each
of
the
categories
is
shown
(light
bars).
Of
the
students
who
reported
experiencing
an
SLE
in

each
category,
the
percentage
who
indicated
the
SLE
of
that
category
was
most
life-‐altering
is
also
shown
(dark
bars).

31.7%
15.3%
17.5%
29.1%
63.6%
25.8%
24.0%
24.9%
40.3%
11.4%
24.4%
29.2%
34.8%
40.3%
44.0%
48.7%
49.9%
66.2%
0.0% 20.0% 40.0% 60.0% 80.0%
Vic0m%of%a%violent%or%abusive%crime.%
Disciplined%or%suspended%from%school%or%work.%
Did%not%have%enough%money%for%basics%
Someone%in%my%family%or%I%was%arrested.%
Other%[wriJen%in%by%par0cipant].%
New%person%joined%the%household.%
Broke%up%with%my%partner.%
A%lot%of%arguments%at%home.%
Family%member:%serious%illness,%accident,%or%injury.%
Percentage)of)students)who)experienced)the)type)of)SLE)
Types)of)Stressful)Life)Events)(SLEs))Reported)
%%repor0ng%SLE% %%reported%as%most%lifeValtering%SLE%

45

Figure
2.
Moderation
of
the
Relationship
Between
PTG
and
Future
Time
Perspective

by
Hispanic
Ethnicity

Hispanic
ethnicity
moderates
the
relationship
between
Future
Time
Perspective

(FTP)
and
Post-‐traumatic
Growth
(PTG)
at
p<.05.
Hispanics
with
higher
levels
of

FTP
experienced
the
highest
levels
of
PTG
compared
to
Hispanics
with
lower
levels

of
FTP.
In
contrast,
no
difference
was
seen
in
PTG
among
non-‐Hispanics
with

varying
levels
of
FTP.
Overall,
Hispanics
reported
higher
levels
of
PTG
than
non-‐
Hispanics.

2.20$
2.30$
2.40$
2.50$
2.60$
2.70$
2.80$
low$ mid$ high$
Post%trauma*c,Growth,
Levels,of,Future,Time,Perspec*ve,
Hispanic$
Non:Hispanic$
p:value$for$the$
interacAon$<$.05$

46

CHAPTER
3:
STUDY
2

Posttraumatic
Growth
and
Change
in
Substance
Use
Behaviors

Substance
use
is
one
of
the
most
problematic
health
concerns
for
adolescents

and
young
adults
in
the
U.S.

It
has
been
estimated
that
by
the
10
th

grade,
over
27%

of
youth
have
smoked
cigarettes,
54%
have
tried
alcohol,
35%
have
been
drunk
on

alcohol,
34%
have
tried
marijuana,
and
15%
have
used
illicit
drugs
in
their
lifetime

(Johnston
et
al.,
2013).
Adolescents
who
use
or
misuse
substances
have
a
higher

likelihood
of
having
experienced
highly
stressful
and
traumatic
events
in
their
past

(e.g.,
childhood
sexual
abuse,
witnessing
violence,
natural
disaster)
as
substances

have
long
been
used
as
a
method
of
coping
and
relief
from
distress
(e.g.,
Holahan,
et

al.,
2001;
Wills,
1986).
Unsuccessful
coping
with
stressful
life
events
(SLEs)
and
the

resulting
emotional
distress
are
consistent
predictors
of
earlier
and
more
frequent

substance
use
among
adolescents
(e.g.,
Booker
et
al.,
2004;
Dube
et
al.,
2006;

Newcomb
&
Harlow,
1986;
Unger
et
al.,
1998;
Wagner,
et
al.,
2009;
Wills,
1986).

However,
use
of
any
of
the
three
most
accessible
drugs—tobacco,
alcohol
or

marijuana—during
adolescence
increases
the
likelihood
that
an
individual
will

develop
a
substance
use
dependence
disorder
later
in
life
(Palmer
et
al.,
2009).

Some
youth
are
at
greater
risk
than
others
for
engaging
in
substance
use

behaviors
and
experiencing
higher
levels
of
stress/trauma.
In
particular,
students

who
attend
continuation
high
schools
6

(CHSs)
experience
greater
levels
of

stress/trauma
than
their
regular
high
school
(RHS)
counterparts,
including

6
Continuation High Schools may be called alternative, contract, or community high schools in states other
than California. Generally, students in these schools have left regular high school because of excessive
truancy, poor academic performance, drug use, violence, other illegal activity, or disruptive behavior
(Rohrbach et al., 2005).

47

emotional
and
physical
abuse
or
victimization,
loss
of
a
parent,
cycling
in-‐and-‐out
of

foster
care,
being
a
witness
to
violence,
and
other
occurrences
that
cause
them
to

feel
disconnected
from
mainstream
society
(Zweig
&
Institute,
2003).

Overall,
CHS

students
report
a
higher
prevalence
of
tobacco,
alcohol,
and
marijuana
use

(Rohrbach,
et
al.,
2005;
Sussman,
et
al.,
1995).

Although
the
relationship
between
self-‐reported
SLEs
and
substance
use

disorders
has
been
well
established
(Wagner,
et
al.,
2009;
Wiechelt,
2007;
Wills,

1986;
Wills,
et
al.,
1992),
not
all
adolescents
exhibit
maladaptive
behaviors
after

having
experienced
a
highly
stressful
life
event.
Instead,
many
youth
undergo
a

process
of
reevaluation
and
redefinition
of
their
life’s
priorities,
allowing
them
to

successfully
adapt
despite
their
high-‐risk
environment
and
potentially
more

vulnerable
backgrounds
(Austin,
2004;
Masten,
2004)
such
that
they
emerge
in
the

aftermath
of
a
traumatic
experience
with
a
more
positive
perspective
on
life.
Such

individuals
develop
Post-‐traumatic
Growth
(PTG).

PTG
has
been
defined
as
having
garnered
positive
life
changes
and
developed

a
level
of
psychological
functioning
and
awareness
beyond
pre-‐trauma
level
as
a

result
of
struggling
with
and
managing
a
highly
stressful
event
(Calhoun
&
Tedeschi,

2001;
Tedeschi
&
Calhoun,
1995).

Manifestations
of
PTG
include
a
greater

investment
in
and
appreciation
for
life,
improved
interpersonal
relationships,
a

greater
sense
of
one’s
spirituality,
and
an
augmented
sense
of
personal
strength

(Tedeschi
&
Calhoun,
1996).
PTG
is
a
multi-‐dimensional
construct
involving

cognitive,
emotional,
and
psychosocial
change.

48

Several
theories
can
explain
the
phenomenon
of
PTG.
Joseph’s
person-‐
centered
theory
(Joseph,
2003;
Joseph
&
Linley,
2006)
posits
that
individuals
are

intrinsically
motivated
to
become
fully
functioning;
this
means
that
in
the
aftermath

of
SLEs
they
strive
to
become
fully
accepting
of
themselves,
find
purpose
and

meaning
in
life,
experience
life
as
a
process,
find
value
in
trusting
relationships,
and

thereby,
develop
PTG.
In
addition,
fully
functioning
individuals
accommodate
new

experiences,
including
the
trauma-‐related
ones,
in
order
to
develop
their
new
sense

of
an
improved
self
who
functions
at
a
higher
level
than
the
pre-‐trauma
self
(Joseph,

2003;
Joseph
&
Linley,
2006).
Yet,
there
also
may
be
illusory
components
of
PTG

such
that
reports
of
greater
growth
may
reflect
exaggerations,
or
mild
distortions,
of

post-‐SLE
improvement
as
a
palliative
strategy
to
help
one
regain
self-‐esteem
and
a

sense
of
control
over
circumstances
surrounding
the
event
(see
Cognitive

Adaptation
Theory:
Taylor,
1983;
Taylor
&
Armor,
1996;
Taylor
et
al.,
2000;
and

Janus-‐Face
model:
Maercker
&
Zoellner,
2004;
Zoellner
&
Maercker,
2006),
while

still
exhibiting
risky
behavior
such
as
substance
use.

Because
those
who
strive
to

become
fully
functioning
will
find
certain
behaviors
as
incongruent,
or
threatening,

to
their
perception
of
their
improved
post-‐trauma
self,
it
follows
that
such

individuals
would
likely
engage
in
fewer
health-‐compromising
behaviors.

It
is

unclear
whether
developing
PTG
in
the
aftermath
of
a
significant
SLE
reflects
efforts

to
be
fully
functioning,
and
is
associated
with
fewer
substance
use
behaviors,
or
if

reports
on
PTG
represent
an
illusory
psychological
perspective
that
does
not

coincide
with
a
change
towards
better
health
behaviors.

49

Many
of
the
prior
studies
on
the
relationship
between
PTG
and
health-‐
compromising
behaviors
have
been
conducted
with
adult
samples.
For
example,

Siegel
and
Scrimshaw
(Siegel
&
Schrimshaw,
2000)
interviewed
54
women
living

with
HIV/AIDS
and
found
that
many
had
made
a
number
of
changes
in
their
lives

after
being
diagnosed;
that
being
diagnosed
with
their
disease
served
as
a
“wake-‐up

call”.

Women
reported
behavioral
changes
including
reduced
use
of
alcohol
and

other
drugs,
less
risky
sex,
improved
stress
management,
and
change
to
a
healthier

diet.
Similarly,
Updegraff
et
al
(Updegraff
et
al.,
2002)
found
that
among
189
HIV-‐
positive
women,
many
had
become
less
involved
with
drugs
and
alcohol
post-‐
diagnosis.
In
a
study
by
Tsourtos
et
al
(Tsourtos
et
al.,
2011),
successful
ex-‐smokers

reported
that
specific
traumatic
events
in
their
lives,
such
as
the
break-‐up
of
a
long-‐
term
relationship,
had
provided
them
with
the
necessary
motivation
to
quit.

Milam

(Milam,
2006)
reported
that
among
adults
diagnosed
with
HIV/AIDS,
PTG
was

inversely
related
to
alcohol
use
(r=-‐.14,
p<.01).

Stump
and
Smith
(Stump
&
Smith,

2008)
demonstrated
that
among
adults
reporting
various
types
of
trauma,
there
was

an
inverse
relationship
between
PTG
and
substance
use
(r=-‐.28,
p<.05).
Similarly,

Urcuyo
et
al
(Urcuyo
et
al.,
2005)
reported
that
among
breast
cancer
patients,
PTG

was
inversely
related
to
substance
use
(b=-‐.13,
p<.05).
Thus,
although
there
is
some

evidence
for
an
inverse
relationship
between
PTG
and
substance
use
among
adults,

relatively
little
is
known
about
these
relationships
among
adolescents.

Only
two
quantitative
studies
have
looked
at
the
direct
relationship
between

PTG
and
substance
use
among
youth.
The
first
study,
conducted
among
RHS

students
with
an
average
age
of
15.8
years
(SD=1.52),
found
that
PTG
was
inversely

50

related
to
substance
use
(a
composite
index
of
tobacco,
alcohol,
and
marijuana)

(Milam,
et
al.,
2004).

The
second
study
reported
an
inverse
relationship
between

PTG
and
alcohol
use
(r=-‐.15,
p<.001)
and
inverse
but
non-‐significant
relationships

between
PTG
and
cigarette
smoking
and
marijuana
use
(Milam,
et
al.,
2005).

However,
the
sample
was
comprised
of
younger
adolescents
in
8
th

grade,
an
average

age
of
13.5
years
old
(SD=0.52),
who
reported
relatively
low
prevalence
rates
of
use:

5.8%
for
past
30-‐day
cigarette
use,
10.3%
for
prior
year
marijuana
use,
and
34.4%

for
prior
year
alcohol
use
(Milam,
et
al.,
2005).
The
current
study
aims
to
expand
the

empirical
literature
by
testing
the
hypothesis
that
in
accordance
with
the
person-‐
centered
theory,
older
at-‐risk
youth
who
report
higher
levels
of
PTG
in
the

aftermath
of
a
life-‐altering
SLE
will
reduce
their
substance
use
behaviors
over
time.

Method

Participants

Participants
were
enrolled
in
a
randomized
controlled
trial
of
Project

Towards
No
Drug
Abuse
(TND),
a
12-‐lesson
drug-‐abuse
prevention
curriculum
that

targets
youth
in
CHSs.
Project
TND
has
been
evaluated
in
seven
randomized
trials

that
have
shown
short
and
long-‐term
effects
on
reducing
cigarette
smoking
and

other
drug
use
among
teens
(Sussman,
et
al.,
2012).
The
current
trial
(Sussman,
et

al.,
2012)
examined
the
efficacy
of
a
booster
component
that
utilizes
motivational

interviewing
techniques.
Twenty-‐four
CHSs
were
randomly
assigned
to
one
of
three

experimental
conditions:
control,
TND
only,
or
TND
plus
motivational
interviewing

booster.
A
total
of
1704
(71.1%)
of
students
enrolled
in
classes
selected
from
the
24

CHSs
consented
to
participate
in
the
intervention
study,
for
which
results
are

51

reported
elsewhere
(see
Sussman,
et
al.,
2012).

Reasons
for
non-‐participation

include
parent
decline
of
consent
(0.8%),
student
decline
of
consent
or
assent

(5.1%),
or
parental
non-‐response
(23.4%).

Data
Collection

Data
for
this
study
were
collected
before
program
implementation
(baseline)

and
at
two-‐year
follow-‐up.

Data
were
collected
in
accordance
with
IRB
practices
at

the
University
of
Southern
California
(USC).
Informed
consent
was
obtained
from

students
who
were
at
least
18
years
of
age.
For
those
under
18,
informed
consent

was
obtained
from
parents,
in
addition
to
student
assent.
Trained
data
collectors

administered
a
paper
and
pencil
survey
in
one
50-‐minute
classroom
period
at
the

baseline.
Students
who
provided
consent
but
were
absent
the
day
of
survey

administration
received
a
telephone
call
and
were
given
the
option
to
complete
the

survey
verbally
at
that
time.
Of
the
1704
participants
who
were
consented,
1676

completed
the
baseline
survey.

For
the
two-‐year
follow-‐up
data
collection,
703

(41.9%)
of
students
completed
surveys
that
were
administered
by
telephone

(76.3%),
in-‐person
(at
school
or
via
home
visit;
8.8%),
or
by
mail-‐back
(14.8%).
For

this
study,
the
analytic
sample
was
comprised
only
of
students,
from
both

intervention
and
control
groups,
who
reported
having
experienced
a
SLE
within
the

past
two-‐years
and
answered
PTG
items
referring
to
the
SLE
(n=564).

Measures

Study
Condition.
A
covariate
was
included
in
order
to
control
for
the
study

condition
to
which
students
were
assigned.
Because
this
study
did
not
assess
effects

52

of
the
intervention,
and
previous
studies
have
not
shown
differences
in
substance

use
outcomes
between
the
two
intervention
conditions
(see
Sussman,
et
al.,
2012),

the
variable
for
study
condition
was
dichotomously
coded
as
TND-‐any
(either

intervention
arm)
or
Control.
7

Demographics.

Socio-‐demographic
information
was
collected
at
baseline
for

age
(in
years),
gender,
race/ethnicity
categories
(Asian
or
Asian
American;
Latino
or

Hispanic;
African
American
or
Black;
White,
Caucasian,
Anglo,
European
American;

not
Hispanic;
American
Indian
or
Native
American;
Mixed:
My
parents
are
from
two

different
groups;
Other),
and
socioeconomic
status
(a
single
variable
reflecting

either
mother’s
or
father’s
highest
educational
attainment,
whichever
was
higher).

Additional
items
assessed
current
living
and
job
situation
(live
with
both
parents;

live
with
a
boyfriend/girlfriend/partner;
currently
married;
currently
a
parent;
have

a
job).

Stressful
Life
Events
(SLEs).
The
SLE
checklist
included
in
the
2-‐year
follow-‐up

survey
was
derived
from
an
abbreviated
(18-‐item)
version
of
the
Adolescent

Negative
Life
Events
Inventory
(Wills,
1986;
Wills
&
Cleary,
1996)
that
was
used
in
a

previous
study
among
adolescents
(mean
age=14.4
years
±
0.8)
(Rohrbach,
et
al.,

2009).

For
the
present
study,
we
included
a
checklist
of
the
8
life
events
that
were

most
commonly
reported
among
adolescents
in
the
Rohrbach
et
al.,
(2009)
study.

Wording
for
some
items
was
altered
in
order
to
be
more
relevant
to
this
older

adolescent
population
(mean
age
at
the
time
of
the
2-‐year
follow-‐up
survey
=
18.8
±

7
Sensitivity analysis demonstrated that results of this study were the same when treatment condition was
coded dichotomously (0=control, 1=intervention) or categorically with 3-levels (0=control, 1=intervention
only, 2=intervention+MI booster).

53

9.3).
For
example,
“My
parents
had
problems
with
money”
was
changed
to
“I
did
not

have
enough
money
for
basics
(like
food)”
and
“I
had
a
lot
of
arguments
with
my

parents”
was
changed
to
“There
were
a
lot
of
arguments
that
happened
at
home.”

Participants
were
provided
with
a
checklist
of
the
8
stressful
life
events
and
asked

to
indicate
which
events
they
had
experienced
within
the
past
two
years
(1=yes
or

2=no
to
each
item).
A
ninth
question
allowed
for
participants
to
indicate
that
they

had
experienced
other
events
not
listed
in
the
checklist
with
a
free-‐entry
field
for

them
to
write
in
the
event(s).

Responses
were
summed
to
create
a
score
of
the
total

number
of
stressful
life
events
experienced
within
the
past
two
years.
Subsequently,

participants
were
asked
to
indicate
which
of
the
events
listed
(including
anything

listed
in
the
“Other”
category)
affected
their
life
the
most.

Post-‐traumatic
Growth.
The
instrument
used
to
assess
Post-‐traumatic
Growth

at
2-‐year
follow-‐up
was
based
on
an
11-‐item
Post-‐traumatic
Growth
Inventory

(PTGI),
a
modification
of
the
original
inventory
by
Tedeschi
and
Calhoun
(Tedeschi,

1995;
Tedeschi
&
Calhoun,
1996).
The
11-‐item
version
of
the
scale
has
been
used

previously
among
both
adolescent
and
adult
samples
(Arpawong,
et
al.,
2012;

Milam,
2006;
Milam,
et
al.,
2005;
Milam,
2004).
We
selected
8
items
from
the
11-‐
item
PTGI
based
on
their
high
factor
loadings
on
the
first
unrotated
factor
at
or

above
0.66
with
an
Eigenvalue
of
5.44.
Participants
were
asked
to
respond
to
items

in
reference
to
the
SLE
they
designated
as
most-‐life
altering
and
occurring
within

the
past
two
years.
To
avoid
the
potential
bias
from
participants
only
being
able
to

report
positive
valenced
change
that
may
have
resulted
from
their

stressful/traumatic
event,
items
were
modified
to
allow
for
response
choices
of

54

negative
change,
no
change,
or
positive
change
(3-‐point
scale).
A
composite
score,

averaging
responses
on
all
8
items,
was
used
for
this
study.

Internal
consistency
of

this
scale
was
high
(Cronbach
alpha=0.81).

Peer
Substance
Use.
use
is
a
well-‐established
indicator
of
maladaptive

adjustment
by
its
relationship
with
poor
health-‐related
behaviors
(Sussman,
Dent,
&

McCullar,
2000).
Four
items
were
used
at
baseline
to
assess
each
of
the

subcategories
of
substance
use
among
peers:
cigarettes,
alcohol,
marijuana,
and

hard
drugs.
The
four
items
were
averaged
yielding
a
scale
with
high
internal

consistency
(Cronbach’s
alpha=0.85)

Substance
Use.
Items
assessing
substance
use,
used
as
dependent
variables
in

this
study,
were
measured
at
both
baseline
and
2-‐year
follow-‐up.
Eight
separate

substance
use
variables
were
assessed
through
use
of
cigarettes
(2
outcomes),

alcohol
(3
outcomes),
marijuana
(1
outcome),
hard
drugs
(1
outcome),
and
overall

substance
abuse
(1
outcome).

Cigarette
use
was
measured
by
2
single-‐item
continuous
variables,
and
used

as
two
separate
outcomes:
average
daily
cigarette
use
(“How
many
cigarettes
do
you

smoke
per
day
on
average?”)
and
past
month
use
of
cigarettes.
To
assess
past
month

use,
the
question
“How
many
times
have
you
used
each
of
these
drugs
in
the
last

month
(last
30
days)?”
was
posed
with
a
checklist
of
substances
(e.g.,
cigarettes,

alcohol,
drunk
on
alcohol,
marijuana,
cocaine,
hallucinogens,
etc.).
Response
options

were
provided
to
indicate
0
to
over
100
times
(1=0
times,
2=1-‐10
times,
3=11-‐20

times,…,
12=Over
100
times).
Alcohol
use
was
measured
by
3
continuous
variables,

55

used
as
separate
outcomes:
drinking
alcohol
in
the
past
month,
getting
drunk
in
the

past
month,
and
binge
drinking
in
the
past
month
(“How
many
days
have
you
had
5

or
more
alcoholic
drinks
within
a
5
hour
period
over
the
last
30
days?”).
Marijuana

use
was
measured
using
1
continuous
variable:
using
marijuana
in
the
past
month.

For
hard
drug
use,
the
responses
to
8
questions
regarding
past
month
use
of
cocaine,

hallucinogens,
stimulants,
inhalants,
ecstasy,
pain
killers,
tranquilizers,
or
other

hard
drugs
were
summed
to
create
a
hard
drug
use
index
(Cronbach
alpha
=.73).

Finally,
a
dichotomous
index
of
overall
substance
abuse
(yes/no)
in
the
past
year

was
created
using
4
questions
(e.g.,
“In
the
last
12
months,
have
you
kept
using

alcohol
or
drugs
even
though
it
was
keeping
you
from
meeting
your
responsibilities

at
work,
school,
or
home?”),
serving
as
proxy
items
of
the
DSM-‐IV
substance
abuse

disorder
categories.
Responses
from
the
4
items
were
summed
into
a
single
variable

(Cronbach
alpha
=.66),
and
if
the
score
was
1
or
more,
the
participant
was
coded
as

having
a
substance
use
disorder.
The
reliability
of
the
substance
use
item
format

used
has
been
demonstrated
previously
(Graham
et
al.,
1984;
Needle
et
al.,
1983;

Stacy
et
al.,
1990).
For
analysis,
all
outcome
variables
were
log-‐transformed
due
to

the
data
not
being
normally
distributed.

Statistical
Analysis

All
analyses
were
performed
using
the
SAS
(v.9.1.3)
statistical
package.

Because
the
distribution
of
the
PTG
variable
was
negatively
skewed,
PTG
was

reflected
and
log-‐transformed
for
all
analyses.
Correlation
coefficients
were

calculated
between
key
variables.
Means,
standard
deviations,
and
frequencies
for

56

selected
demographic
characteristics
and
key
variables
were
calculated.
Because
of

insufficient
numbers
in
the
race
categories
other
than
Hispanic
(35%),

race/ethnicity
was
recoded
to
Hispanic
or
non-‐Hispanic.
The
PTG
score,
a
log-‐
transformed
continuous
variable,
was
entered
as
the
dependent
variable
for
all

analyses.

To
account
for
possible
differential
attrition
on
important
baseline
variables

in
the
analytic
models,
a
propensity-‐to-‐attrition
score
(PTA)
was
calculated
for
each

participant
retained
in
the
sample
(vs.
those
lost-‐to-‐follow-‐up
at
2
years)
and

included
as
a
covariate
in
regression
models
such
that
results
could
be
interpreted

as
if
there
was
no
imbalance
in
attrition
on
key
variables
within
the
sample.
The

PTA
score
was
calculated
by
associating
the
difference
on
key
variables
(18

variables)
to
actual
attrition
status
(0=not
retained
in
the
sample,
1=retained
in
the

sample)
from
baseline
to
2-‐year
follow-‐up
in
a
logistic
regression
analysis.
The

variables
that
were
significantly
associated
with
attrition
were
age,
whether
the

participant
lived
with
both
parents,
and
a
4-‐item
scale
on
attitudes
of
drug
use
(i.e.,

if
they
used
drugs,
they
would
feel
wrong,
guilty
or
ashamed;
see
Sussman,
Dent,
&

Galaif,
1997);
these
variables
were
included
in
the
calculation
of
the
PTA
score.
This

method
has
been
used
previously
to
control
for
the
effects
of
differential
attrition

(Berger,
2005;
Grunkemeier
et
al.,
2002;
Sun
et
al.,
2007;
Sussman
et
al.,
2011).

Multilevel
regression
(PROC
MIXED)
models
were
run,
controlling
for

covariates,
to
examine
the
primary
study
hypothesis
(whether
higher
PTG
predicts
a

change
towards
less
substance
use
over
time).
To
model
change
in
substance
use,

57

change
scores
were
created
by
subtracting
the
frequency
of
use
at
baseline
from
the

frequency
of
use
at
two-‐year
follow-‐up.

Also,
models
controlled
for
baseline
by

entering
baseline
frequency
of
use
of
the
substance
as
one
of
the
independent

variables.
The
independent
variables
of
interest
for
these
models,
number
of
SLEs

and
PTG,
were
both
entered
into
the
models
as
continuous
variables,
predicting

substance
use
outcomes.
The
models
included
a
propensity-‐to-‐attrition
score,

intervention
condition,
socio-‐demographic
variables
(i.e.,
age,
gender,
and

race/ethnicity,
parents’
education
as
a
proxy
for
socio-‐economic
status),
and
peer

substance
use
as
covariates.
Additionally,
analyses
included
random
effects

modeling
which
is
important
due
to
the
nested
structure
of
the
data
(i.e.,
students

being
nested
within
schools).

Results

Attrition
Analysis

Of
the
1,676
students
who
completed
a
survey
at
baseline,
703
students

completed
the
2-‐year
follow-‐up
survey
(58.1%
attrition
rate).
To
assess
the
impact

of
attrition,
the
group
retained
was
compared
to
the
group
that
was
lost-‐to-‐follow-‐
up
at
the
two-‐year
data
collection.

Groups
were
compared
for
all
variables
used
in

this
study
using
the
Student
t-‐test
or
chi-‐square
test
in
order
to
detect
statistically

significant
differences
between
samples
at
the
p-‐value
alpha
of
0.05
(two-‐tailed).

The
group
retained
at
two-‐year
follow-‐up
was
similar
to
the
group
lost-‐to-‐follow-‐up

on
all
variables
except
they
were
younger,
more
likely
to
live
with
both
parents,
and

58

had
more
negative
attitudes
towards
drug
use
at
baseline
(p<.0001).
Therefore,

these
variables
were
included
in
the
calculation
of
the
PTA
score.

Participant
Characteristics

Slightly
more
than
half
of
the
participants
were
male
(54%)
and
living
with

both
parents
(53%).
The
majority
of
participants
had
a
parent
who
completed
high

school
(65%)
and
self-‐identified
as
Latino
or
Hispanic
(65%).
On
average,
students

reported
that
at
least
3
of
their
5
closest
friends
used
cigarettes,
alcohol,
marijuana,

or
hard
drugs
in
the
last
30
days.
Also,
students
reported
experiencing
a
mean
of
3

SLEs
in
the
last
two
years.
The
most-‐life
altering
SLEs
reported
in
order
of
greatest

to
least
frequency
were
someone
in
the
family
having
a
serious
illness,
accident,
or

injury
(28%);
conflict
at
home
(13%);
relationship
problem
(12%);
being
or
having

someone
in
the
family
be
arrested
(11%);
having
a
new
person
join
the
household

(11%);
not
having
enough
money
for
basics
such
as
food
(6%);
job
or
school

change/problem
(6%);
being
a
victim
of
a
violent
or
abusive
crime
(4%);
personal

injury,
illness,
accident
or
change
in
health
status
(2%);
death
of
an
extended
family

member
(2%);
being
displaced
from
home
(2%);
injury
or
death
of
a
friend
(2%);
as

well
as
other
SLEs,
of
which
each
was
reported
by
less
than
1%
of
participants

(pregnancy,
miscarriage
of
self
or
partner;
change
in
religious
faith;
death
of
a

parent
or
both;
witnessing
a
violent
crime;
getting
robbed).

The
majority
of
students
reported
that
some
aspect
of
their
life
had
improved

in
the
aftermath
of
having
experienced
the
most-‐life
altering
SLE
of
the
past
two

years,
demonstrated
by
a
mean
PTG
score
of
2.64
(SD=0.38),
on
a
scale
of
1
to
3.

59

Participants
were
most
likely
to
report
positive
changes
on
items
in
order
of

greatest
to
least
frequency:
my
own
inner
strength
(83%),
appreciation
for
the
value

of
my
own
life
(77%),
direction
for
my
life
(75%),
handling
my
difficulties
(72%),

involvement
in
things
that
interest
me
(69%),
my
compassion
for
others
(69%),
my

sense
of
closeness
with
others
(65%),
and
my
understanding
of
spiritual
matters

(56%).

Table
4
provides
means,
standard
deviations,
and
frequencies
for
substance

use
behaviors
assessed
at
two-‐year
follow-‐up.

Students
were
most
likely
to
use

alcohol
(58%),
then
cigarettes
(38%),
then
marijuana
(34%)
in
the
past
month;

however,
the
number
of
times
of
use
in
the
past
month,
among
those
who
reported

any
use
in
the
past
month,
was
most
frequent
for
cigarettes
(5.2±3.9),
then

marijuana
(3.9±3.1),
hard
drugs
(2.8±2.3),
and
alcohol
(2.4±1.3).

Multi-‐level
Regression
Models

Several
sociodemographic
characteristics
predicted
an
increase
in
use
of

certain
substances.
Being
male
predicted
greater
frequency
in
the
use
of
cigarettes,

alcohol,
and
marijuana
use
as
well
as
substance
abuse;
being
of
non-‐Hispanic

ethnicity
predicted
greater
frequency
in
daily
and
30-‐day
cigarette
use
and

marijuana
use;
and
higher
parental
education
predicted
more
binge
drinking

(p’s<.05).
Regarding
substance
use
variables,
baseline
was
a
strong
predictor
of
use

of
all
substances
2-‐years
later
(p’s<.01).
Greater
peer
substance
use
predicted
a

greater
use
of
alcohol
and
marijuana
(p’s<.05),
and
weakly
predicted
greater

frequency
of
getting
drunk
on
alcohol,
binge
drinking,
and
hard
drug
use
(p<.10).

60

Table
5
shows
the
results
of
hypotheses
testing.
Experiencing
a
higher

number
of
SLEs
predicted
greater
frequency
(times
per
month)
of
cigarette,
alcohol,

marijuana,
and
hard
drug
use,
higher
frequency
of
getting
drunk
on
alcohol,
and

greater
substance
abuse
at
two-‐year
follow-‐up
(p’s<.05).
The
relationship
between

higher
number
of
SLEs
and
higher
average
number
of
cigarettes
used
per
day,

among
those
who
had
smoked
in
the
past
month,
approached
significance
(p<.10);

however,
a
higher
number
of
SLEs
did
not
predict
more
frequent
binge
drinking
in

the
past
month.
Overall,
results
supported
the
assertion
that
greater
PTG
predicted

a
change
towards
lower
frequency
in
substance
use
over
time
for
several

substances.
A
higher
PTG
score
predicted
lower
frequency
in
alcohol
and
marijuana

use,
reduced
frequency
of
getting
drunk
on
alcohol,
less
binge
drinking,
as
well
as

less
past
year
substance
abuse
(p’s<.05).
However,
PTG
did
not
impact
frequency
in

use
for
cigarettes,
either
average
daily
use
or
past
30-‐day
use,
or
hard
drug
use.
8

Discussion

This
is
the
first
longitudinal
study
to
demonstrate
that
PTG
is
associated
with

a
change
towards
less
substance
use
over
time.
Positive
psychosocial
adjustment
to

a
life-‐altering
experience
may
counteract
the
negative
impact
of
stress
from
SLEs
on

substance
use
behaviors
among
high-‐risk
youth.
Thus
fostering
PTG
among
older

8
Regression results modeling change scores as the dependent variable were the same as when frequencies
of use at two-year follow-up were used as the dependent variable. Also, these results were supported by an
examination of differences in mean PTG scores when the dependent substance use variable was coded
categorically with respect to change in use from baseline to two-year follow-up (0=no use maintained,
1=reduced/quit use, 2=stayed at the same level of use, and 3=increased/initiated use). Directions of
relationship with PTG were as hypothesized in that participants categorized as ‘increasing/initiating use’
had lower mean PTG scores whereas participants categorized as ‘reducing/quitting use’ had higher mean
PTG scores. This pattern of relationships was true for all substances except for change in frequency of
cigarette use in the past 30 days and average cigarettes smoked per day.

61

youth
may
be
one
approach
for
augmenting
the
efficacy
of
substance
use
prevention

programs.

Developing
a
higher
level
of
PTG,
as
a
result
of
experiencing
a
life-‐altering

event,
predicts
a
change
towards
lower
frequency
of
substance
use
over
time.

Higher
PTG
predicted
changes
towards
less
use
of
alcohol
and
marijuana,
getting

drunk
on
alcohol,
binge
drinking
and
past
year
substance
abuse.

In
order
to
develop

PTG,
one
needs
to
have
experienced
an
event
significant
enough
to
have
shattered

one’s
assumptions
about
the
world
and
one’s
place
in
it.
Successfully
managing
the

stress
from
such
an
event
then
facilitates
cognitive
processing
and
re-‐building
of
a

more
positive
perspective
such
that
one
is
able
to
function
at
a
higher
level
than
the

pre-‐crisis
self.
Because
PTG
was
associated
with
changes
in
self-‐reported
behavior

over
a
two-‐year
period,
this
suggests
that
any
self-‐enhancing
perceptions
of
the

post-‐trauma
self
in
this
sample
were
not
merely
illusory
or
transitory.
Rather,
in

support
of
the
person-‐centered
theory,
participants
in
this
study
demonstrated

congruence
between
improved
psychological
functioning
(i.e.,
PTG)
and
improved

behaviors.

Experiencing
SLEs
contributed
to
greater
use
of
cigarettes,
alcohol,

marijuana,
hard
drugs,
getting
drunk
on
alcohol
and
substance
use.
The
finding
is

consistent
with
prior
research
findings
among
younger
adolescents
that
show
SLEs

are
related
to
higher
substance
use
rates
overall
(e.g.,
Booker,
et
al.,
2004;
Dube,
et

al.,
2006;
Low
et
al.,
2012;
Newcomb
&
Harlow,
1986).
Therefore,
the
method
in

which
youth
attempt
to
release
or
avoid
stress
stemming
from
SLEs
is
an
important

62

modifiable
target
for
intervention/
prevention
programs,
which
can
include
skills

for
engaging
in
alternate
behaviors
and
activities
to
relieve
stress.

A
common
approach
of
efficacious
school-‐based
programs
for
at-‐risk,
older

youth
is
the
incorporation
of
modules
that
focus
on
coping
with
stress
and

decreasing
stress
levels
(Sussman
et
al.,
2004;
Sussman
&
Sun,
2009).
Future

modules
may
be
more
effective
if
they
include
recognizing
cues
to
emotional
and

mental
distress
stemming
from
an
SLE,
facilitating
cognitive
processing,
as
well
as

augmenting
skills
with
which
to
engage
in
activities
that
have
demonstrated
effect

on
promoting
PTG,
such
as
expressive
writing,
physical
activity,
or
meditation

(Cameron
et
al.,
2007;
Sabiston,
McDonough,
&
Crocker,
2007;
Smyth,
Hockemeyer,

&
Tulloch,
2008).
Augmenting
such
skills
in
order
to
facilitate
PTG
may
assist
with

buffering
the
impact
of
SLEs
on
substance
use.

There
was
no
association
between
PTG
and
cigarette
or
hard
drug
use.
For

cigarettes,
this
corroborates
the
study
conducted
among
younger
adolescents
that

showed
a
relationship
between
PTG
and
less
alcohol
use,
but
not
less
cigarette
use

(Milam,
et
al.,
2005).

One
explanation
for
the
lack
of
relationship
between
PTG
and

cigarette
use
may
be
that
continuing
to
smoke
cigarettes
may
not
elicit
the
same

level
of
cognitive
dissonance
with
the
perception
of
an
improved
sense
of
self
as

does
continuing
to
use
other
substances.

One
method
of
testing
this
speculation

may
be
to
inquire
about
perceived
social
norms
for
smoking
cigarettes
and
use
of

other
substances
among
these
youth
to
assess
the
difference.
One
explanation
for

the
lack
of
relationship
between
PTG
and
hard
drug
use
may
be
that
there
was
a

relatively
low
prevalence
of
hard
drug
use
overall
(13%).

63

Although
prevalence
of
hard
drug
use
was
relatively
low
compared
to
other

substance
use
among
this
sample,
on
average,
this
sample
reported
higher
rates
of

substance
use
than
national
averages.

According
to
nationally
representative

surveys
of
older
youth,
age
18
and
older,
30-‐day
prevalence
rates
were:
31.9%
for

cigarette
use,
48.9%
for
alcohol
use,
33.3%
for
binge
drinking,
18.5%
for
marijuana

use
(SAHMSA,
2011),
28.1%
for
having
gotten
drunk
on
alcohol,
and
8.4%
for
hard

drugs
use
in
the
last
30
days
(Johnston,
et
al.,
2013).

As
these
comparisons
support

the
perception
that
these
CHS
youth
represent
a
high-‐risk
sample
with
regard
to

substance
use,
integrating
assessments
of
and
promoting
positive
psychosocial

adjustment
may
be
useful
to
augment
efforts
aimed
at
lowering
substance
use

among
those
attending
CHSs.

Limitations

The
generalizability
of
these
findings
is
applicable
to
older,
mostly
Hispanic

youth
who
attend
continuation
or
alternative
high
schools.

In
addition,
the
data
for

these
studies
are
based
on
self-‐reports,
which
are
subject
to
memory
lapses
or

selective
disclosure
on
the
types
or
number
of
SLEs
experienced
(Dohrenwend,

2006).
Also,
because
those
who
did
not
report
experiencing
an
SLE
could
not
be

included
in
the
sample,
we
are
uncertain
whether
the
level
of
PTG
reported
would

represent
all
older,
at-‐risk
youth.

Similarly,
there
is
no
corroborating
evidence
to

validate
levels
of
positive
growth
reported
by
participants
(e.g.,
reports
by

significant
others,
a
trusted
family
member)
and
levels
of
growth
may
be
subject
to

issues
of
social
desirability.
However,
the
associations
between
PTG
and
reduced

64

substance
use
suggest
that
positive
growth
is
evident
and
manifested
in
less
drug

use.

Future
Research

Future
directions
for
this
research
include
examining
these
relationships

among
different
samples
of
youth
comprised
of
varying
age
ranges
and
race/ethnic

compositions
and
including
a
secondary
informant
on
reports
of
PTG.
Including

more
extensive
checklists
of
both
positive
and
negative
valenced
SLEs
or
using
a

semi-‐structured
interview
format
to
assess
SLEs
may
be
useful.

Also,
there
may
be

other
factors
contributing
to
the
level
of
positive
psychosocial
adjustment
that
need

to
be
accounted
for
in
future
studies,
given
the
low
proportion
of
variance
in
change

in
substance
use
explained
in
each
of
our
models.
Because
alcohol
misuse
and

dependence
indicators
in
early
to
late
adulthood
may
be
influenced
by
interactions

between
stress
susceptibility
and
SLEs
encountered
during
childhood
(e.g.,
Dawson,

Grant,
&
Li,
2007;
Lee
et
al.,
2012;
Young-‐Wolff,
Kendler,
&
Prescott,
2012),
the

inclusion
of
assessments
of
coping
with
SLEs,
evaluating
interactions
between
prior

SLEs
and
coping
with
subsequent
SLEs
are
needed.

In
conclusion,
this
study
finds
SLEs
to
be
related
to
an
increase
in
use
of
all

substances
from
baseline
to
two-‐year
follow-‐up.
Positive
psychosocial
adjustment
to

a
life-‐altering
SLE,
indicated
by
self-‐reported
PTG,
predicted
a
decrease
in
use
of

alcohol
and
marijuana,
and
substance
abuse.
The
results
have
implications
for

substance
use
interventions.
Collectively,
findings
from
the
present
study
and
prior

studies
demonstrate
that
individuals
may
seek
to
achieve
congruence
between

thought
and
action
such
that
having
undergone
positive
psychological

65

transformation
following
extreme
stress
manifests
in
less
use
of
certain
substances.

Because
PTG
can
be
augmented
through
brief
cognitive-‐behavioral
stress
reduction

approaches
(Cryder
et
al.,
2006;
Garland
et
al.,
2007;
Lechner
&
Antoni,
2004),

facilitating
PTG
represents
a
unique,
potentially
salutogenic,
intervention
target
that

may
help
to
counteract
the
negative
effect
of
SLEs
on
substance
use
among
higher

risk
youth.

66

Table
4.
Prevalence
of
Substance
Use
Behaviors
Among
the
CHS
Sample
at
Two-‐
Year
Follow-‐Up

Substance
Use
Variable

%
or

Mean
(SD)
Range

Cigarette
use
(past
month)
38
-‐-‐

Alcohol
use
(past
month)
58
-‐-‐

Drunk
on
alcohol
(past
month)
34
-‐-‐

Binge
drinking
(past
month)
36

Marijuana
use
(past
month)
34
-‐-‐

Hard
drug
use
(past
month)
14
-‐-‐

Substance
abuse
(past
year)

31
-‐-‐

Average
number
of
cigarettes
smoked
per
day

(among
those
who
smoked
at
least
once
in
the

past
month)
6.07
(5.95)
1-‐40

Number
of
times
smoked
cigarettes
(past

month)
32.71
(19.46)
1-‐90

Number
of
times
drank
alcohol
(past
month)
4.78
(2.34)
1-‐100

Number
of
times
drunk
on
alcohol
(past
month)
6.66
(0.86)
1-‐50

Number
of
days
binge
drinking
(past
month)
4.68
(5.38)
1-‐30

Number
of
times
used
marijuana
(past
month)
19.10
(11.63)
1-‐100

Number
of
times
used
hard
drugs
(past
month)
8.29
(3.43)
1-‐100

Number
of
times
abused
substances
(past
year)

1.77
(0.89)
1-‐4

Notes.

Frequencies
of
substance
use
variables
were
only
calculated
among
those
who

reported
using
in
the
past
month.

67

Table
5.
Regression
Models
Showing
the
Impact
of
SLEs
and
PTG
on
Change
in
Frequency
of
Substance
Use
Behaviors

Substance
use
change

a

Number
of

stressful
life
events

(SLEs)

Post-‐traumatic

Growth
(PTG)

Model
F

d

β
b

SE

c

β
b

SE

c

Average
daily
cigarette
use
0.08
#
0.02
0.00

0.04
3.16
**

Cigarette
use
(past
30
days)
0.07
**
0.02
0.04

0.04
12.71
***

Alcohol
use
(past
30
days)
0.04
*
0.02
-‐0.06
*
0.03
89.99
***

Drunk
on
alcohol
(past
30
days)
0.03
*
0.02
-‐0.08
**
0.03
99.85
***

Binge
drinking
(past
30
days)
-‐0.02

0.04
-‐0.18
**
0.06
14.35
***

Marijuana
use
(past
30
days)
0.06
**
0.02
-‐0.07
*
0.03
51.27
***

Hard
drug
use
(past
30
days)
0.05
**
0.02
0.01

0.03
109.88
***

Substance
abuse
(past
12

months)
0.14
***
0.03
-‐0.13
**
0.05
7.04
***

Notes.

a

Dependent
variables
for
substance
use
were
modeled
as
change
in
frequency
of
use
from
baseline
to
two-‐year
follow-‐up

(number
of
times
or
days
use
in
the
past
30
days)
except
for
average
daily
cigarettes
(assessed
average
number
of
cigarettes

smoked
per
day)
and
substance
abuse
(assessed
substance
abuse
within
the
past
12
months).

b

β=Standardized
beta.

c

SE=Standard
error.

d

All
models
are
controlled
for
age,
gender,
ethnicity,
parents'
education,
peer
substance
use,
baseline
use,
propensity-‐to-‐
attrition
score,
and
treatment
condition.

#
p
<
.10;
*
p
<
.05;
**
p
<
.01;
***
p
<
.001

68

CHAPTER
4:
CONCLUSION

Research
surrounding
positive
psychosocial
adjustment
to
SLEs
is
promising,

and
this
is
demonstrated
by
the
recent
surge
in
research
on
Post-‐traumatic
Growth

and
related
constructs
in
the
empirical
literature
over
the
past
two
decades.
A

review
of
the
current
literature
indicates
that
PTG
represents
a
resiliency
construct

and
developing
PTG
in
the
aftermath
of
having
experienced
significantly
stressful

life
events
may
have
salutary
benefits
to
health
outcomes.

The
objectives
of
these

dissertation
studies
were
to
answer
the
questions
of
(1)
what
predicts
the

development
of
PTG,
including
characteristics
of
stressful
life
events,
one’s
personal

and
environmental
system?;
and
(2)
how
do
SLEs
and
PTG
impact
change
in

substance
use
behaviors
over
time?
This
dissertation
expands
the
current
literature

by
conducting
novel
examinations
on
the
predictors
of
PTG,
including
indicators
of

developmental
stage
and
influence
of
cultural
context,
as
well
as
the
relationship

between
PTG
and
change
in
substance
use
behaviors
over
a
two-‐year
period
among

a
high-‐risk,
older
youth
sample.

Findings
of
these
studies
have
implications
for

prevention
interventions,
most
particularly
substance
use
interventions.

Also,
given

certain
limitations
and
the
implications
of
the
findings,
there
are
ample
possibilities

for
future
research
on
PTG.

Implications
for
Preventive
Interventions

Implications
of
PTG
for
prevention
interventions
have
far-‐reaching
potential,

from
public
health
work
(e.g.,
interventions
for
major
disasters,
or
man-‐made

destruction),
to
behavioral
research
(e.g.,
community
or
school-‐based
interventions),

69

to
clinical
settings
(e.g.,
individual
client-‐centered
therapy,
group-‐level
primary
and

secondary
interventions).
Cognitive
transformation,
of
which
PTG
is
a
measure,
has

demonstrated
relationships
with
enhanced
ability
to
adapt
to
adverse

circumstances
(Tebes
et
al.,
2004).
Thus,
it
is
considered
a
possible
agent
with

which
to
strengthen
resilience,
and
thus
one’s
ability
to
cope
effectively
with
life

challenges.
Furthermore,
interventions
that
foster
PTG
have
shown
that
developing

greater
PTG
confers
not
only
benefits
on
psychosocial
functioning,
but
also
benefits

in
biological
and
immune
functioning.

Such
findings,
taken
together
with
findings

from
this
dissertation
work,
suggest
several
implications
for
preventive

interventions.

Thus
far,
a
handful
of
studies
exist
on
interventions
that
specifically
measure

PTG
and
related
outcomes,
all
with
promising
results.
Among
adults,
PTG

interventions
were
first
implemented
in
the
clinical
settings
among
individuals

diagnosed
with
cancer.
In
the
first
randomized
control
trial
conducted
to
assess

intervention-‐related
results
specifically
on
PTG,
a
cognitive-‐behavioral
stress

management
(CBSM)
program
was
implemented
for
women
being
treated
for
early-‐
stage
breast
cancer
(Antoni
et
al.,
2001).
Study
aims
were
to
decrease
the

prevalence
of
depression
while
enhancing
PTG
among
participants.
Antoni
et
al

demonstrated
that
the
CBSM
intervention
not
only
succeeded
in
enhancing

measured
levels
of
PTG,
but
a
follow-‐up
study
demonstrated
that
the
women

experienced
physiological
benefits
as
well
such
as
greater
lymphocyte
proliferative

responses
suggesting
faster
immune
system
recovery
at
3-‐month
follow-‐up

(McGregor,
et
al.,
2004).

Similarly,
other
studies
have
demonstrated
diminished

70

distress
among
intervention
groups
when
conducted
with
breast
cancer
patients

(Phillips
et
al.,
2008;
Garland,
et
al.,
2007),
prostate
cancer
survivors
(Penedo
et
al.,

2006),
survivors
of
motor
vehicle
accidents
(Zoellner
et
al.,
2011),
and
adults
who

had
reported
experiencing
personal
trauma
(e.g.,
were
crime
victims,
sexual
abuse

victims,
bereaved
parents)
up
to
8
years
prior
(Knaevelsrud,
Liedl,
&
Maercker,

2010).
Thus,
findings
from
the
dissertation
studies
as
well
as
current
literature
on

PTG
interventions
demonstrates
that
there
are
identifiable
targets
with
which
to

foster
PTG
(i.e.,
future
time
perspective,
motivation,
stress
management
skills)
and

that
higher
PTG
may
be
beneficial
in
promoting
other
health
outcomes.

In
particular,
results
from
Study
2
taken
together
with
prior
research

findings
discussed
in
Chapter
3
have
promising
implications
for
substance
use

interventions.

As
greater
PTG
was
related
to
less
substance
use
over
time,
PTG
may

be
viewed
as
a
possible
mediator
of
other
constructs
that
assist
in
recovery
from

substance
misuse
and
addictive
behaviors.

For
example,
enhancing
PTG
through

intervention
may
also
strengthen
an
individual’s
spiritual
beliefs,
and
thereby

compel
them
to
reduce
behaviors
that
do
not
align
with
those
beliefs
(Sussman
et
al.,

Provisionally
accepted).

Also,
as
previously
mentioned
in
Chapter
3,
the
more

efficacious
school-‐based
programs
for
at-‐risk,
older
youth
tend
to
include
modules

that
focus
on
coping
with
stress
and
decreasing
overall
stress
levels
(Sussman,
et
al.,

2004;
Sussman
&
Sun,
2009).

However,
these
modules
may
be
strengthened
by

included
modalities
to
cope
specifically
with
acute
stress
from
life
events.
As
PTG

can
be
fostered
through
brief
cognitive-‐behavioral
stress
reduction
approaches

(Cryder,
et
al.,
2006;
Garland,
et
al.,
2007;
Lechner
&
Antoni,
2004),
it
represents
a

71

malleable
strength-‐based
intervention
target
that
may
assist
youth
in
establishing
a

type
of
resilience
to
SLEs.

Thus,
integrating
assessments
of
and
the
promotion
of

PTG
into
substance
use
intervention
modules,
may
facilitate
a
reduction
in

substance
use
among
youth,
particularly
those
who
frequently
experience

significant
SLEs.

Limitations

Several
limitations
have
been
highlighted
regarding
the
studies
presented
in

this
dissertation.
From
Study
1,
limitations
include
potential
problems
of
lack
of
self-‐
disclosure
of
SLEs
experienced
within
the
past
two
years;
that
the
specific
time-‐
point
that
the
most
life-‐altering
SLE
occurred
was
not
assessed;
a
perceived
severity

measure
of
the
most
life-‐altering
SLE
was
not
included;
and
other
measures
of

coping
or
adjustment
to
SLEs
were
not
included
for
their
prediction
of
PTG.

From

Study
2,
limitations
not
aforementioned
in
Study
1
include
the
generalizability
of

findings
among
this
high-‐risk
older
youth,
mostly
Hispanic,
sample
to
other
samples

and
that
all
data
relied
on
self-‐report.

In
addition,
broader
limitations
for
research
can
be
drawn
in
order
to

highlight
potential
areas
for
further
research.
First,
these
studies
were
not
designed

to
assess
simultaneous
levels
of
distress
(i.e.,
post-‐traumatic
stress
symptoms,

anxiety,
depression)
that
may
be
incurred
from
experiencing
highly
stressful
life

events.
Thus,
the
extent
to
which
these
highly
vulnerable
older
youth
developed

problems
of
distress
from
the
SLEs
is
unclear,
as
is
the
relationship
between
distress

and
impact
on
substance
use
changes
over
time.
Second,
the
studies
were
not

designed
to
examine
influences
of
stress
susceptibility
and
earlier
SLEs,
during

72

childhood,
on
the
development
of
PTG.

Thus,
it
is
unclear
how
the
interactions

between
prior
life
stress
and
stress
susceptibility
influenced
either
the
development

of
PTG
or
the
substance
use
behaviors
for
the
sample
in
these
studies.
Third,
these

studies
were
designed
to
assess
PTG
and
change
in
substance
use
behaviors
during

a
specific
developmental
time
period
in
life.

Therefore,
it
is
unknown
how
the

development
of
PTG
and
changes
in
substance
use
behaviors
during
emerging

adulthood
would
impact
other
constructs
of
psychosocial
adjustment,
PTG
and

substance
use
later
in
adulthood.

Lastly,
these
studies
were
not
designed
to
assess

more
comprehensive
measures
of
cultural
context
that
may
impact
the
degree
to

which
PTG
is
reported.
Thus,
it
is
unclear
as
to
why
those
who
identified
as
Hispanic

or
Latino
generally
reported
higher
levels
of
PTG
than
those
of
other
ethnicities.

Future
Research
Directions

Given
the
implications
of
this
research
and
the
limitations
noted,
the

adaptive
significance
of
PTG
and
potential
research
areas
may
have
much
wider

implications
to
overall
health
and
prevention
research
(e.g.,
improved
self-‐efficacy

or
hardiness,
higher
resistance
to
distress,
immunological
and
biological

improvements).

Findings
from
these
studies
have
implications
for
further
research

on
both
mental
health
and
health
behavior
outcomes.

From
prior
research,
PTG
has
been
examined
for
its
relationship
to
other

mental
health
constructs
over
time.
For
example,
in
a
longitudinal
study
conducted

among
breast
cancer
patients,
investigators
found
that
PTG
predicted
lower
distress

and
depression
between
4
and
7
years
later
(Carver
&
Antoni,
2004).

In
another

longitudinal
study
conducted
among
HIV/AIDS
patients,
PTG
predicted
lower
levels

73

of
depression
over
time,
assessed
approximately
1.6
year
later
(Milam,
2004).

Because
studies
described
in
this
dissertation
did
not
examine
PTG
with
respect
to

mental
health
outcomes,
an
area
of
future
research
among
high-‐risk
youth
is
to

examine
the
relationship
between
PTG
and
indicators
of
distress
(e.g.,
depression,

anxiety,
post-‐traumatic
stress
symptoms)
and
other
outcomes
commonly
observed

as
a
result
of
experiencing
significant
SLEs.
Such
studies
may
help
to
elucidate

mechanisms
by
which
older
youth
are
able
to
positive
adapt
to
life
events
despite

being
surrounded
by
potentially
more
distressing
environments.

In
addition,
assessing
the
time-‐point
of
when
the
most
life-‐altering
SLE

occurred
and
repeat
measures
of
PTG
may
be
useful
in
order
to
better
distinguish

the
mechanistic
pathway
of
positive
psychosocial
adjustment.
For
example,
it
is

possible
that
the
Cognitive
Adaptation
Theory
(i.e.,
PTG
represents
an
illusory

exaggeration
of
post-‐trauma
improvement)
is
true
earlier
on,
but
then
changes
in

behavior
follow
to
achieve
psychological
congruence,
in
support
of
the
Person-‐
Centered
Theory
(i.e.,
individuals
strive
to
become
fully
functional,
with

consistencies
in
both
cognition
and
behavior).

Thus,
it
may
be
true
that
both

constructive
and
illusory
components
are
reflected
in
PTG,
supporting
the
Janus-‐
Face
Model
of
PTG,
that
there
are
both
illusory
and
functional
components
to
PTG

(see
Maercker
&
Zoellner,
2004;
Zoellner
&
Maercker,
2006),
although
it
is
unclear

as
to
what
time-‐point
one
“face”
shows
versus
the
other
when
individuals
respond

to
survey
items
regarding
growth
from
their
stressful/traumatic
experiences.
The

key
aspect
that
distinguishes
the
constructive
from
the
illusory
component
of
PTG
is

the
temporal
nature
of
these
responses
to
trauma.

For
future
study,
there
is
a
need

74

for
a
process-‐oriented
investigation
in
order
to
better
understand
the
two

components
in
PTG
scores.
In
order
to
do
this,
future
examinations
could
include

repeated
assessments
of
PTG
over
time,
including
immediately
after
an
SLE
and
at

multiple
follow-‐up
time
points.
They
might
also
include
assessments
of
the

relationship
between
PTG
and
the
following:
active
coping
and
situational
coping

(vs.
dispositional
coping),
indicators
of
well-‐being
and
quality
of
life
cross-‐
sectionally
and
at
follow-‐up
time
points,
changes
in
self-‐perceptions
on
self-‐esteem,

coherence,
and
degree
of
personal
control.
Such
analyses
may
help
to
discern
which

component
of
PTG
is
being
captured
by
the
measure,
and
whether
there
is
a

temporal
difference
detectable
for
when
PTG
reflects
either
the
constructive
or

illusory
component.

With
regard
to
examining
predictors
of
PTG,
the
estimated
proportion
of

variance
explained
by
predictors
was
relatively
low
(13%).

Influences
of
earlier
life

SLEs
and
susceptibility
have
not
been
assessed
for
their
influence
on
the

development
of
PTG,
despite
burgeoning
evidence
that
demonstrates
SLEs
in

childhood
can
impact
subsequent
stress
processing
depending
on
genetic

polymorphisms
or
also
via
epigenetic
mechanisms.
For
example,
childhood

maltreatment
early
in
life
may
interact
with
genetic
susceptibility
(i.e.,
genetic

polymorphisms
in
the
promoter
region
(5-‐HTTLPR)
of
the
serotonin-‐transporter

gene
or
a
monoamine
oxidase
A
(MAOA)
gene;
see
Caspi
et
al.,
2010;
Rutter,
Kim-‐
Cohen,
&
Maughan,
2006)
to
influence
stress-‐related
mental
health
later
in
life.

Similarly,
childhood
trauma
has
been
shown
to
impact
immediate
genetic

expression
such
that
the
stress-‐response
system
may
be
altered
throughout
the

75

remainder
of
life
(Klengel
et
al.,
2012).

Thus,
it
may
be
important
to
examine
how

the
development
of
PTG
is
influenced
by
stress
susceptibility
(genetic
variants)
or
if

earlier
SLEs
during
childhood
interact
with
one’s
predisposed
susceptibility
to

impact
the
development
of
PTG
and
other
indicators
of
psychosocial
adaptation
to

SLEs
in
later
adolescence
and
into
adulthood.

With
regard
to
higher
levels
of
PTG
reported
among
Hispanics
with
higher

future
time
perspective,
there
are
strong
empirical
and
conceptual
reasons
to

further
examine
the
relationships
between
predictors
of
positive
psychosocial

change
and
PTG
among
Hispanics.

Several
studies
have
demonstrated
that

Hispanics
report
higher
levels
of
PTG
than
non-‐Hispanics
across
various
types
of

SLEs
(Milam,
2006;
Milam,
et
al.,
2005;
Powell,
et
al.,
2003;
Smith
et
al.,
2008;

Tedeschi
&
Calhoun,
2004;
Urcuyo,
et
al.,
2005),
yet
this
finding
has
not
been

consistent
across
all
studies
(Milam,
2004;
Milam,
et
al.,
2004).

A
recent
study
found

that
PTG
among
older
youth
was
lowest
among
Hispanics
who
primarily
spoke

English
as
their
primary
language
at
home
whereas
PTG
was
highest
among

Hispanics
who
spoke
another
language
at
home,
compared
to
White,
non-‐Hispanic

and
other
ethnicities
(Arpawong,
et
al.,
In
Press).

Such
findings
suggest
that
aside

from
cultural
norms
and
values
(e.g.,
spirituality/religiosity,
responsibilities

towards
family)
shared
among
the
diverse
grouping
of
Hispanics,
there
may
be

other
factors,
such
as
acculturation
levels,
that
influence
the
development
of
PTG.

Further
investigation
of
concepts
related
to
cultural
context
may
provide
more

insight
into
positive
psychosocial
adaptation
to
SLEs
among
Hispanics.

76

Summary

Novel
contributions
of
these
studies
include
the
examination
of
predictors
of

PTG
and
analysis
of
PTG-‐related
changes
in
substance
use
behaviors
among
older,

vulnerable
youth
through
a
longitudinal
study
design.
Among
this
sample,
findings

demonstrate
that
PTG
is
less
reflective
of
mood
states
(depression
and
positive

affect),
rather
individuals
who
identify
more
with
being
in
the
stage
of
Emerging

Adulthood
engage
in
the
processes
of
cognitive
restructuring
and
re-‐building
of
the

life
perspective
such
that
they
are
able
to
function
at
a
higher
level
than
the
pre-‐
trauma
self.
Further,
reports
of
PTG
tend
to
be
higher
among
those
who
orient
to

their
daily
roles
and
responsibilities
with
a
future
(vs.
past
or
present)
time

perspective.
Findings
from
this
study
indicate
that
those
who
identify
as
being

Hispanic
report
higher
levels
of
both
future
time
perspective
and
overall
PTG.
Lastly,

findings
of
these
studies
demonstrate
that
levels
of
PTG
do
not
reflect
illusory
or

transitory
exaggerations
of
post-‐trauma
improvement,
rather
levels
of
PTG
reported

are
consistent
with
post-‐trauma
improvement
in
behaviors.
Older
youth
in
these

studies
demonstrated
both
improvements
in
perceptions
of
themselves,
their

relationships,
their
possibilities
in
life,
and
substance
use
behaviors

contemporaneously.
Lastly,
while
less
cumulative
stress
from
SLEs
was
predictive
of

higher
PTG,
findings
of
these
studies
support
the
notion
that
positive
psychosocial

adjustment
to
a
life-‐altering
experience
may
be
protective
against
consequences
of

stress
from
SLEs
on
substance
use
behaviors
among
high-‐risk
youth.
Given
these

findings,
there
are
important
implications
for
both
substance
use
prevention

interventions
and
many
avenues
for
future
research
on
PTG.

77

REFERENCES

Aiken,
L.
S.,
West,
S.
G.,
&
Reno,
R.
R.
(1991).
Multiple
regression:
Testing
and

interpreting
interactions:
Sage
Publications,
Inc.

Aldwin,
C.
M.,
Levenson,
M.
R.,
&
Spiro,
A.,
3rd.
(1994).
Vulnerability
and
resilience
to

combat
exposure:
can
stress
have
lifelong
effects?
Psychology
and
aging,
9(1),

34-‐44.

Alisic,
E.,
van
der
Schoot,
T.
A.,
van
Ginkel,
J.
R.,
&
Kleber,
R.
J.
(2008).
Looking

beyond
posttraumatic
stress
disorder
in
children:
posttraumatic
stress

reactions,
posttraumatic
growth,
and
quality
of
life
in
a
general
population

sample.
The
Journal
of
clinical
psychiatry,
69(9),
1455-‐1461.

Anda,
R.
F.,
Felitti,
V.
J.,
Bremner,
J.
D.,
Walker,
J.
D.,
Whitfield,
C.,
Perry,
B.
D.,
Dube,
S.

R.,
&
Giles,
W.
H.
(2006).
The
enduring
effects
of
abuse
and
related
adverse

experiences
in
childhood.
A
convergence
of
evidence
from
neurobiology
and

epidemiology.
European
archives
of
psychiatry
and
clinical
neuroscience,

256(3),
174-‐186.

Antoni,
M.
H.,
Lehman,
J.
M.,
Kilbourn,
K.
M.,
Boyers,
A.
E.,
Culver,
J.
L.,
Alferi,
S.
M.,

Yount,
S.
E.,
McGregor,
B.
A.,
Arena,
P.
L.,
Harris,
S.
D.,
Price,
A.
A.,
&
Carver,
C.

S.
(2001).
Cognitive-‐behavioral
stress
management
intervention
decreases

the
prevalence
of
depression
and
enhances
benefit
finding
among
women

under
treatment
for
early-‐stage
breast
cancer.
Health
psychology
:
official

journal
of
the
Division
of
Health
Psychology,
American
Psychological

Association,
20(1),
20-‐32.

Armeli,
S.,
Gunthert,
K.
C.,
&
Cohen,
L.
H.
(2001).
Stressor
appraisals,
coping,
and

post-‐event
outcomes:
The
dimensionality
and
antecedents
of
stress-‐related

growth.
Journal
of
Social
and
Clinical
Psychology,
20(3),
366-‐395.

Arnett,
J.
J.
(2000).
Emerging
adulthood.
A
theory
of
development
from
the
late
teens

through
the
twenties.
The
American
psychologist,
55(5),
469-‐480.

Arnett,
J.
J.
(2004).
Adolescence
and
emerging
adulthood:
Pearson
Prentice
Hall.

Arpawong,
T.
E.,
Oland,
A.,
Milam,
J.
E.,
Ruccione,
K.
S.,
&
Meeske,
K.
A.
(In
Press).

Posttraumatic
Growth
Among
an
Ethnically
Diverse
Sample
of
Adolescent

and
Young
Adult
Cancer
Survivors.
Psycho-‐Oncology.

78

Arpawong,
T.
E.,
Richeimer,
S.
H.,
Weinstein,
F.,
Elghamrawy,
A.,
&
Milam,
J.
E.
(2012).

Posttraumatic
Growth,
Quality
of
Life,
and
Treatment
Symptoms
Among

Cancer
Chemotherapy
Outpatients.
Health
psychology(Epub
ahead
of
print).

Association,
A.
P.,
&
DSM-‐IV.,
A.
P.
A.
T.
F.
o.
(2000).
Diagnostic
and
statistical
manual

of
mental
disorders:
DSM-‐IV-‐TR:
American
Psychiatric
Publishing,
Inc.

Austin,
A.
A.
(2004).
Alcohol,
tobacco,
other
drug
use,
and
violent
behavior
among

Native
Hawaiians:
ethnic
pride
and
resilience.
Substance
use
&
misuse,
39(5),

721-‐746.

Barnett,
E.,
Spruijt-‐Metz,
D.,
Unger,
J.
B.,
Rohrbach,
L.
A.,
Sun,
P.,
&
Sussman,
S.
(In

Press).
Bi-‐directional
Study
of
Future
Time
Perspective
and
Substance
Use

Among
Continuation
High
School
Students.
Substance
Use
&
Misuse.

Berger,
R.,
&
Weiss,
T.
(2010).
Posttraumatic
Growth
in
U.S.
Latinos.
Posttraumatic

growth
and
culturally
competent
practice:
Lessons
learned
from
around
the

globe,
113-‐127.

Berger,
V.
W.
(2005).
The
reverse
propensity
score
to
detect
selection
bias
and

correct
for
baseline
imbalances.
Statistics
in
medicine,
24(18),
2777-‐2787.

Bland,
J.
M.,
&
Altman,
D.
G.
(1986).
Statistical
methods
for
assessing
agreement

between
two
methods
of
clinical
measurement.
Lancet,
1(8476),
307-‐310.

Bloom,
B.
L.
(1985).
A
factor
analysis
of
self-‐report
measures
of
family
functioning.

Family
Process,
24(2),
225-‐239.

Booker,
C.
L.,
Gallaher,
P.,
Unger,
J.
B.,
Ritt-‐Olson,
A.,
&
Johnson,
C.
A.
(2004).
Stressful

life
events,
smoking
behavior,
and
intentions
to
smoke
among
and

multiethnic
sample
of
sixth
graders.
Ethnicity
&
Health,
9(4),
369-‐397.

Buckner,
J.
C.,
Beardslee,
W.
R.,
&
Bassuk,
E.
L.
(2004).
Exposure
to
violence
and
low-‐
income
children's
mental
health:
direct,
moderated,
and
mediated
relations.

The
American
journal
of
orthopsychiatry,
74(4),
413-‐423.

Calhoun,
L.
G.,
Cann,
A.,
Tedeschi,
R.
G.,
&
McMillan,
J.
(2000).
A
correlational
test
of

the
relationship
between
posttraumatic
growth,
religion,
and
cognitive

processing.
Journal
of
traumatic
stress,
13(3),
521-‐527.

Calhoun,
L.
G.,
&
Tedeschi,
R.
G.
(2001).
Posttraumatic
growth:
The
positive
lessons

of
loss.

79

Cameron,
L.
D.,
Booth,
R.
J.,
Schlatter,
M.,
Ziginskas,
D.,
&
Harman,
J.
E.
(2007).

Changes
in
emotion
regulation
and
psychological
adjustment
following
use
of

a
group
psychosocial
support
program
for
women
recently
diagnosed
with

breast
cancer.
Psycho-‐Oncology,
16(3),
171-‐180.

Caplan,
G.
(1974).
Support
systems
and
community
mental
health:
Lectures
on

concept
development:
Behavioral
Publications.

Carver,
C.
S.
(1998).
Resilience
and
thriving:
Issues,
models,
and
linkages.
Journal
of

Social
Issues,
54(2),
245-‐266.

Carver,
C.
S.,
&
Antoni,
M.
H.
(2004).
Finding
Benefit
in
Breast
Cancer
During
the

Year
After
Diagnosis
Predicts
Better
Adjustment
5
to
8
Years
After
Diagnosis.

Health
Psychology,
23(6),
595-‐598.

Caspi,
A.,
Hariri,
A.
R.,
Holmes,
A.,
Uher,
R.,
&
Moffitt,
T.
E.
(2010).
Genetic
sensitivity

to
the
environment:
the
case
of
the
serotonin
transporter
gene
and
its

implications
for
studying
complex
diseases
and
traits.
Focus,
8(3),
398.

Chassin,
L.,
Presson,
C.
C.,
Rose,
J.
S.,
&
Sherman,
S.
J.
(1996).
The
natural
history
of

cigarette
smoking
from
adolescence
to
adulthood:
demographic
predictors
of

continuity
and
change.
Health
psychology
:
official
journal
of
the
Division
of

Health
Psychology,
American
Psychological
Association,
15(6),
478-‐484.

Cieslak,
R.,
Benight,
C.,
Schmidt,
N.,
Luszczynska,
A.,
Curtin,
E.,
Clark,
R.
A.,
&

Kissinger,
P.
(2009).
Predicting
posttraumatic
growth
among
Hurricane

Katrina
survivors
living
with
HIV:
The
role
of
self-‐efficacy,
social
support,
and

PTSD
symptoms.
Anxiety,
Stress
&
Coping:
An
International
Journal,
22(4),

449-‐463.

Cohen,
J.
(1978).
Partialed
products
are
interactions;
partialed
powers
are
curve

components.
Psychological
Bulletin,
85(4),
858.

Cohen,
S.,
Kamarck,
T.,
&
Mermelstein,
R.
(1983).
A
Global
Measure
of
Perceived

Stress.
Journal
of
health
and
social
behavior,
24(4),
385-‐396.

Cohen,
S.,
&
Williamson,
G.
M.
(1988).
Perceived
Stress
in
a
Probability
Sample
of
the

United-‐States.
Social
Psychology
of
Health,
31-‐67.

Collins,
L.
M.,
Sussman,
S.,
Rauch,
J.
M.,
Dent,
C.
W.,
Johnson,
C.
A.,
Hansen,
W.
B.,
&

Flay,
B.
R.
(1987).
Psychosocial
Predictors
of
Young
Adolescent
Cigarette

Smoking:
A
Sixteen‚ÄêMonth,
Three‚ÄêWave
Longitudinal
Study1.
Journal
of

Applied
Social
Psychology,
17(6),
554-‐573.

80

Copeland,
W.
E.,
Keeler,
G.,
Angold,
A.,
&
Costello,
E.
J.
(2007).
Traumatic
events
and

posttraumatic
stress
in
childhood.
Archives
of
general
psychiatry,
64(5),
577-‐
584.

Cordova,
M.
J.,
Cunningham,
L.
L.
C.,
Carlson,
C.
R.,
&
Andrykowski,
M.
A.
(2001).

Posttraumatic
growth
following
breast
cancer:
A
controlled
comparison

study.
Health
Psychology,
20(3),
176-‐185.

Cruess,
D.
G.,
Antoni,
M.
H.,
Kumar,
M.,
McGregor,
B.,
Alferi,
S.,
Boyers,
A.
E.,
Carver,
C.

S.,
&
Kilbourn,
K.
(2001).
Effects
of
stress
management
on
testosterone
levels

in
women
with
early-‐stage
breast
cancer.
International
Journal
of
Behavioral

Medicine,
8(3),
194-‐207.

Cryder,
C.
H.,
Kilmer,
R.
P.,
Tedeschi,
R.
G.,
&
Calhoun,
L.
G.
(2006).
An
exploratory

study
of
posttraumatic
growth
in
children
following
a
natural
disaster.
The

American
journal
of
orthopsychiatry,
76(1),
65-‐69.

Currier,
J.
M.,
Hermes,
S.,
&
Phipps,
S.
(2009).
Brief
report:
Children's
response
to

serious
illness:
perceptions
of
benefit
and
burden
in
a
pediatric
cancer

population.
Journal
of
Pediatric
Psychology,
34(10),
1129-‐1134.

Dawson,
D.
A.,
Grant,
B.
F.,
&
Li,
T.
K.
(2007).
Impact
of
age
at
first
drink
on
stress-‐
reactive
drinking.
Alcoholism,
clinical
and
experimental
research,
31(1),
69-‐77.

Dohrenwend,
B.
P.
(2006).
Inventorying
stressful
life
events
as
risk
factors
for

psychopathology:
Toward
resolution
of
the
problem
of
intracategory

variability.
Psychological
Bulletin,
132(3),
477.

Dong,
M.,
Anda,
R.
F.,
Dube,
S.
R.,
Giles,
W.
H.,
&
Felitti,
V.
J.
(2003).
The
relationship

of
exposure
to
childhood
sexual
abuse
to
other
forms
of
abuse,
neglect,
and

household
dysfunction
during
childhood.
Child
abuse
&
neglect,
27(6),
625-‐
639.

Dube,
S.
R.,
Miller,
J.
W.,
Brown,
D.
W.,
Giles,
W.
H.,
Felitti,
V.
J.,
Dong,
M.,
&
Anda,
R.
F.

(2006).
Adverse
childhood
experiences
and
the
association
with
ever
using

alcohol
and
initiating
alcohol
use
during
adolescence.
The
Journal
of

adolescent
health
:
official
publication
of
the
Society
for
Adolescent
Medicine,

38(4),
444
e441-‐410.

Fergusson,
D.
M.,
Boden,
J.
M.,
&
Horwood,
L.
J.
(2008).
The
developmental

antecedents
of
illicit
drug
use:
Evidence
from
a
25-‐year
longitudinal
study.

Drug
and
Alcohol
Dependence,
96(1-‐2),
165-‐177.

81

Frazier,
P.
A.,
Tix,
A.
P.,
&
Barron,
K.
E.
(2004a).
Testing
moderator
and
mediator

effects
in
counseling
psychology
research.
Journal
of
Counseling
Psychology,

51(1),
115-‐134.

Frazier,
P.
A.,
Tix,
A.
P.,
&
Barron,
K.
E.
(2004b).
Testing
moderator
and
mediator

effects
in
counseling
psychology
research
(vol
51,
pg
115,
2004).
Journal
of

Counseling
Psychology,
51(2),
157-‐157.

Garland,
S.
N.,
Carlson,
L.
E.,
Cook,
S.,
Lansdell,
L.,
&
Speca,
M.
(2007).
A
non-‐
randomized
comparison
of
mindfulness-‐based
stress
reduction
and
healing

arts
programs
for
facilitating
post-‐traumatic
growth
and
spirituality
in

cancer
outpatients.
Supportive
care
in
cancer
:
official
journal
of
the

Multinational
Association
of
Supportive
Care
in
Cancer,
15(8),
949-‐961.

Graham,
J.
W.,
Flay,
B.
R.,
Johnson,
C.
A.,
Hansen,
W.
B.,
Grossman,
L.,
&
Sobel,
J.
L.

(1984).
Reliability
of
Self-‐Report
Measures
of
Drug-‐Use
in
Prevention

Research
-‐
Evaluation
of
the
Project
Smart
Questionnaire
Via
the
Test-‐Retest

Reliability
Matrix.
Journal
of
drug
education,
14(2),
175-‐193.

Grotevant,
H.
D.,
&
Cooper,
C.
R.
(1986).
Individuation
in
Family
Relationships
-‐
a

Perspective
on
Individual-‐Differences
in
the
Development
of
Identity
and

Role-‐Taking
Skill
in
Adolescence.
Human
Development,
29(2),
82-‐100.

Grunkemeier,
G.
L.,
Payne,
N.,
Jin,
R.,
&
Handy,
J.
R.,
Jr.
(2002).
Propensity
score

analysis
of
stroke
after
off-‐pump
coronary
artery
bypass
grafting.
The
Annals

of
thoracic
surgery,
74(2),
301-‐305.

Hahn,
R.
A.,
&
Se,
T.
F.
C.
P.
(2008).
Recommendations
to
reduce
psychological
harm

from
traumatic
events
among
children
and
adolescents.
American
Journal
of

Preventive
Medicine,
35(3),
314-‐316.

Helgeson,
V.
S.,
Reynolds,
K.
A.,
&
Tomich,
P.
L.
(2006).
A
meta-‐analytic
review
of

benefit
finding
and
growth.
Journal
of
Consulting
and
Clinical
Psychology,

74(5),
797-‐816.

Henson,
J.
M.,
Carey,
M.
P.,
Carey,
K.
B.,
&
Maisto,
S.
A.
(2006).
Associations
among

health
behaviors
and
time
perspective
in
young
adults:
model
testing
with

boot-‐strapping
replication.
Journal
of
Behavioral
Medicine,
29(2),
127-‐137.

Ho,
S.
M.
Y.,
Chu,
K.
W.,
&
Yiu,
J.
(2008).
The
relationship
between
explanatory
style

and
posttraumatic
growth
after
bereavement
in
a
non-‐clinical
sample.
Death

Studies,
32(5),
461-‐478.

82

Holahan,
C.
J.,
Moos,
R.
H.,
Holahan,
C.
K.,
Cronkite,
R.
C.,
&
Randall,
P.
K.
(2001).

Drinking
to
cope,
emotional
distress
and
alcohol
use
and
abuse:
A
ten-‐year

model.
Journal
of
studies
on
alcohol
and
drugs,
62(2),
190.

Holmes,
T.
H.,
&
Rahe,
R.
H.
(1967).
The
social
readjustment
rating
scale.
Journal
of

Psychosomatic
Research.

Hornblower,
M.
(1997).
Great
xpectations.
Time,
149,
58-‐69.

Ickovics,
J.
R.,
Meade,
C.
S.,
Kershaw,
T.
S.,
Milan,
S.,
Lewis,
J.
B.,
&
Ethier,
K.
A.
(2006).

Urban
teens:
Trauma,
posttraumatic
growth,
and
emotional
distress
among

female
adolescents.
Journal
of
Consulting
and
Clinical
Psychology,
74(5),
841-‐
850.

Janoff-‐Bulman,
R.
(2002).
Shattered
assumptions:
Free
Pr.

Johnston,
L.
D.,
O'Malley,
P.
M.,
Bachman,
J.
G.,
&
Schulenberg,
J.
E.
(2013).

Monitoring
the
Future
national
results
on
adolescent
drug
use:
Overview
of

key
findings,
2012.
Ann
Arbor:
Institute
for
Social
Research,
The
University
of

Michigan.

Joseph,
S.
(2003).
Person
centred
approach
to
understanding
posttraumatic
stress.

Person
Centred
Practice,
11,
70-‐75.

Joseph,
S.,
&
Linley,
P.
A.
(2006).
Growth
following
adversity:
theoretical

perspectives
and
implications
for
clinical
practice.
Clinical
Psychology
Review,

26(8),
1041-‐1053.

Kessler,
R.
C.
(2002).
National
Comorbidity
Survey,
1990-‐1992.
Inter-‐university

Consortium
for
Political
and
Social
Research,
Ann
Arbor.

Kleim,
B.,
&
Ehlers,
A.
(2009).
Evidence
for
a
curvilinear
relationship
between

posttraumatic
growth
and
posttrauma
depression
and
PTSD
in
assault

survivors.
Journal
of
traumatic
stress,
22(1),
45-‐52.

Klengel,
T.,
Mehta,
D.,
Anacker,
C.,
Rex-‐Haffner,
M.,
Pruessner,
J.
C.,
Pariante,
C.
M.,

Pace,
T.
W.
W.,
Mercer,
K.
B.,
Mayberg,
H.
S.,
&
Bradley,
B.
(2012).
Allele-‐
specific
FKBP5
DNA
demethylation
mediates
gene-‐childhood
trauma

interactions.
Nature
Neuroscience,
16(1),
33-‐41.

Knaevelsrud,
C.,
Liedl,
A.,
&
Maercker,
A.
(2010).
Posttraumatic
growth,
optimism

and
openness
as
outcomes
of
a
cognitive-‐behavioural
intervention
for

83

posttraumatic
stress
reactions.
Journal
of
Health
Psychology,
15(7),
1030-‐
1038.

Kubany,
E.
S.,
Leisen,
M.
B.,
Kaplan,
A.
S.,
Watson,
S.
B.,
Haynes,
S.
N.,
Owens,
J.
A.,
&

Burns,
K.
(2000).
Development
and
preliminary
validation
of
a
brief
broad-‐
spectrum
measure
of
trauma
exposure:
The
Traumatic
Life
Events

Questionnaire.
Psychological
Assessment,
12(2),
210.

Larkin,
H.,
Shields,
J.
J.,
&
Anda,
R.
F.
(2012).
The
health
and
social
consequences
of

adverse
childhood
experiences
(ACE)
across
the
lifespan:
an
introduction
to

prevention
and
intervention
in
the
community.
Journal
of
prevention
&

intervention
in
the
community,
40(4),
263-‐270.

Laufer,
A.,
&
Solomon,
Z.
(2006).
Posttraumatic
symptoms
and
posttraumatic

growth
among
Israeli
youth
exposed
to
terror
incidents.
Journal
of
Social
and

Clinical
Psychology,
25(4),
429-‐447.

Lechner,
S.
C.,
&
Antoni,
M.
H.
(2004).
Posttraumatic
growth
and
group-‐based

interventions
for
persons
dealing
with
cancer:
What
have
we
learned
so
far?

Psychological
Inquiry,
15(1),
35-‐41.

Lee,
L.
O.,
Young-‐Wolff,
K.
C.,
Kendler,
K.
S.,
&
Prescott,
C.
A.
(2012).
The
effects
of
age

at
drinking
onset
and
stressful
life
events
on
alcohol
use
in
adulthood:
a

replication
and
extension
using
a
population-‐based
twin
sample.
Alcoholism,

clinical
and
experimental
research,
36(4),
693-‐704.

Lelorain,
S.,
Bonnaud-‐Antignac,
A.,
&
Florin,
A.
(2010).
Long
term
posttraumatic

growth
after
breast
cancer:
Prevalence,
predictors
and
relationships
with

psychological
health.
Journal
of
Clinical
Psychology
in
Medical
Settings,
17(1),

14-‐22.

Linden,
W.
(1984).
Development
and
initial
validation
of
a
life
event
scale
for

students.
Canadian
Journal
of
Counselling
and
Psychotherapy/Revue

canadienne
de
counseling
et
de
psychoth √©rapie,
18(3).

Linley,
P.
A.,
&
Joseph,
S.
(2004).
Positive
Change
Following
Trauma
and
Adversity:
A

Review.
Journal
of
traumatic
stress,
17(1),
11-‐21.

Lisha,
N.
E.,
Grana,
R.,
Sun,
P.,
Rohrbach,
L.,
Spruijt-‐Metz,
D.,
Reifman,
A.,
&
Sussman,

S.
(2012).
Evaluation
of
the
Psychometric
Properties
of
the
Revised
Inventory

of
the
Dimensions
of
Emerging
Adulthood
(IDEA-‐R)
in
a
Sample
of

Continuation
High
School
Students.
Evaluation
&
the
health
professions.

84

Low,
N.
C.,
Dugas,
E.,
O'Loughlin,
E.,
Rodriguez,
D.,
Contreras,
G.,
Chaiton,
M.,
&

O'Loughlin,
J.
(2012).
Common
stressful
life
events
and
difficulties
are

associated
with
mental
health
symptoms
and
substance
use
in
young

adolescents.
BMC
Psychiatry,
12,
116.

MacKinnon,
D.
P.,
Johnson,
C.
A.,
Pentz,
M.
A.,
Dwyer,
J.
H.,
Hansen,
W.
B.,
Flay,
B.
R.,
&

Wang,
E.
Y.
I.
(1991).
Mediating
mechanisms
in
a
school-‐based
drug

prevention
program:
First-‐year
effects
of
the
Midwestern
Prevention
Project.

Health
Psychology,
10(3),
164.

Maercker,
A.,
&
Zoellner,
T.
(2004).
The
Janus
face
of
self-‐perceived
growth:
Toward

a
two-‐component
model
of
posttraumatic
growth.
Psychological
Inquiry,

15(1),
41-‐48.

Masten,
A.
S.
(2004).
Regulatory
processes,
risk,
and
resilience
in
adolescent

development.
Annals
of
the
New
York
Academy
of
Sciences,
1021,
310-‐319.

McCaslin,
S.
E.,
de
Zoysa,
P.,
Butler,
L.
D.,
Hart,
S.,
Marmar,
C.
R.,
Metzler,
T.
J.,
&

Koopman,
C.
(2009).
The
relationship
of
posttraumatic
growth
to

peritraumatic
reactions
and
posttraumatic
stress
symptoms
among
Sri

Lankan
university
students.
Journal
of
traumatic
stress,
22(4),
334-‐339.

McCuller,
W.
J.,
Sussman,
S.,
Wapner,
M.,
Dent,
C.,
&
Weiss,
D.
J.
(2006).
Motivation
to

quit
as
a
mediator
of
tobacco
cessation
among
at-‐risk
youth.
Addictive

behaviors,
31(5),
880-‐888.

McGregor,
B.
A.,
Antoni,
M.
H.,
Boyers,
A.,
Alferi,
S.
M.,
Blomberg,
B.
B.,
&
Carver,
C.
S.

(2004).
Cognitive-‐behavioral
stress
management
increases
benefit
finding

and
immune
function
among
women
with
early-‐stage
breast
cancer.
Journal

of
Psychosomatic
Research,
56(1),
1-‐8.

Meyerson,
D.
A.,
Grant,
K.
E.,
Carter,
J.
S.,
&
Kilmer,
R.
P.
(2011).
Posttraumatic

growth
among
children
and
adolescents:
A
systematic
review.
Clinical

Psychology
Review,
31(6),
949-‐964.

Milam,
J.
(2006).
Posttraumatic
growth
and
HIV
disease
progression.
Journal
of

Consulting
and
Clinical
Psychology,
74(5),
817-‐827.

Milam,
J.,
Ritt-‐Olson,
A.,
Tan,
S.,
Unger,
J.,
&
Nezami,
E.
(2005).
The
September
11th

2001
Terrorist
Attacks
and
Reports
of
Posttraumatic
Growth
among
a
Multi-‐
Ethnic
Sample
of
Adolescents.
Traumatology,
11(4),
233-‐246.

85

Milam,
J.
E.
(2004).
Posttraumatic
Growth
Among
HIV/AIDS
Patients.
Journal
of

Applied
Social
Psychology,
34(11),
2353-‐2376.

Milam,
J.
E.,
Ritt-‐Olson,
A.,
&
Unger,
J.
B.
(2004).
Posttraumatic
Growth
Among

Adolescents.
Journal
of
Adolescent
Research,
19(2),
192-‐204.

Needle,
R.,
McCubbin,
H.,
Lorence,
J.,
&
Hochhauser,
M.
(1983).
Reliability
and

validity
of
adolescent
self-‐reported
drug
use
in
a
family-‐based
study:
a

methodological
report.
The
International
journal
of
the
addictions,
18(7),
901-‐
912.

Newcomb,
M.
D.,
&
Harlow,
L.
(1986).
Life
events
and
substance
use
among

adolescents:
Mediating
effects
of
perceived
loss
of
control
and

meaninglessness
in
life.
Journal
of
Personality
and
Social
Psychology,
51(3),

564.

O'Leary,
V.
E.,
&
Ickovics,
J.
R.
(1995).
Resilience
and
thriving
in
response
to

challenge:
an
opportunity
for
a
paradigm
shift
in
women's
health.
Women's

health,
1(2),
121-‐142.

Palmer,
R.
H.
C.,
Young,
S.
E.,
Hopfer,
C.
J.,
Corley,
R.
P.,
Stallings,
M.
C.,
Crowley,
T.
J.,
&

Hewitt,
J.
K.
(2009).
Developmental
epidemiology
of
drug
use
and
abuse
in

adolescence
and
young
adulthood:
Evidence
of
generalized
risk.
Drug
and

Alcohol
Dependence,
102(1-‐3),
78-‐87.

Park,
C.
L.
(2004).
The
notion
of
growth
following
stressful
life
experiences:

Problems
and
prospects.
Psychological
Inquiry,
15(1),
69-‐76.

Park,
C.
L.,
Edmondson,
D.,
Fenster,
J.
R.,
&
Blank,
T.
O.
(2008).
Meaning
making
and

psychological
adjustment
following
cancer:
The
mediating
roles
of
growth,

life
meaning,
and
restored
just-‐world
beliefs.
Journal
of
Consulting
and

Clinical
Psychology,
76(5),
863-‐875.

Penedo,
F.
J.,
Molton,
I.,
Dahn,
J.
R.,
Shen,
B.-‐J.,
Kinsinger,
D.,
Traeger,
L.,
Siegel,
S.,

Schneiderman,
N.,
&
Antoni,
M.
(2006).
A
Randomized
Clinical
Trial
of
Group-‐
Based
Cognitive-‐Behavioral
Stress
Management
in
Localized
Prostate
Cancer:

Development
of
Stress
Management
Skills
Improves
Quality
of
Life
and

Benefit
Finding.
Annals
of
Behavioral
Medicine,
31(3),
261-‐270.

Peterson,
C.,
Park,
N.,
Pole,
N.,
D'Andrea,
W.,
&
Seligman,
M.
E.
P.
(2008).
Strengths
of

character
and
posttraumatic
growth.
Journal
of
traumatic
stress,
21(2),
214-‐
217.

86

Phillips,
K.
M.,
Antoni,
M.
H.,
Lechner,
S.
C.,
Blomberg,
B.
B.,
Llabre,
M.
M.,
Avisar,
E.,

Gl √ºck,
S.,
DerHagopian,
R.,
&
Carver,
C.
S.
(2008).
Stress
management

intervention
reduces
serum
cortisol
and
increases
relaxation
during

treatment
for
nonmetastatic
breast
cancer.
Psychosomatic
Medicine,
70(9),

1044-‐1049.

Powell,
S.,
Rosner,
R.,
Butollo,
W.,
Tedeschi,
R.
G.,
&
Calhoun,
L.
G.
(2003).

Posttraumatic
growth
after
war:
a
study
with
former
refugees
and
displaced

people
in
Sarajevo.
Journal
of
clinical
psychology,
59(1),
71-‐83.

Radloff,
L.
S.
(1977).
the
Ces-‐D
scale.
Applied
psychological
measurement,
1(3),
385-‐
401.

Radloff,
L.
S.
(1991).
The
Use
of
the
Center
for
Epidemiologic
Studies
Depression

Scale
in
Adolescents
and
Young-‐Adults.
Journal
of
youth
and
adolescence,

20(2),
149-‐166.

Reifman,
A.,
Arnett,
J.,
&
Colwell,
M.
(2003).
The
IDEA:
Inventory
of
the
Dimensions

of
Emerging
Adulthood.
Retrieved
November
25,
2005.

Rohrbach,
L.
A.,
Grana,
R.,
Vernberg,
E.,
Sussman,
S.,
&
Sun,
P.
(2009).
Impact
of

hurricane
Rita
on
adolescent
substance
use.
Psychiatry,
72(3),
222-‐237.

Rohrbach,
L.
A.,
Sussman,
S.,
Dent,
C.
W.,
&
Sun,
P.
(2005).
Tobacco,
alcohol,
and

other
drug
use
among
high-‐risk
young
people:
A
five-‐year
longitudinal
study

from
adolescence
to
emerging
adulthood.
Journal
of
Drug
Issues,
35(2),
333-‐
355.

Rutter,
M.,
Kim-‐Cohen,
J.,
&
Maughan,
B.
(2006).
Continuities
and
discontinuities
in

psychopathology
between
childhood
and
adult
life.
Journal
of
Child

Psychology
and
Psychiatry,
47(3‚Äê4),
276-‐295.

Sabiston,
C.
M.,
McDonough,
M.
H.,
&
Crocker,
P.
R.
E.
(2007).
Psychosocial

experiences
of
breast
cancer
survivors
involved
in
a
dragon
boat
program:

Exploring
links
to
positive
psychological
growth.
Journal
of
Sport
&
Exercise

Psychology,
29(4),
419-‐438.

SAHMSA.
(2011).
Results
From
the
2010
National
Survey
on
Drug
Use
and
Health:

Summary
of
National
Findings
HHS
Publication
No.:(SMA)
11-‐4658
(pp.
156).

Rockville,
MD.

SAMHSA.
(2010).
Substance
Abuse
Mental
Health
Services
Administration
(2010)

Results
from
the
2009
National
Survey
on
Drug
Use
and
Health:
Volume
I.

87

Summary
of
National
Findings
(Substance
Abuse
and
Mental
Health
Services

Administration,
Rockville,
MD)
Series
H-‐38A,
HHS
Publ
No
SMA,
48.

Schaefer,
J.
A.,
&
Moos,
R.
H.
(1992).
Life
crises
and
personal
growth.
In
B.
N.

Carpenter
(Ed.),
Personal
coping:
Theory
research
and
application
(pp.
149-‐
170).
Westport,
CT:
Praeger.

Schaefer,
J.
A.,
&
Moos,
R.
H.
(1998).
The
context
for
posttraumatic
growth:
Life
crises,

individual
and
social
resources,
and
coping.
Mahwah,
NJ,
US:
Lawrence

Erlbaum
Associates
Publishers.

Schuettler,
D.,
&
Boals,
A.
(2011).
The
path
to
posttraumatic
growth
versus

posttraumatic
stress
disorder:
Contributions
of
event
centrality
and
coping.

Journal
of
Loss
and
Trauma,
16(2),
180-‐194.

Seligman,
M.
E.,
&
Csikszentmihalyi,
M.
(2000).
Positive
psychology.
An
introduction.

The
American
psychologist,
55(1),
5-‐14.

Sheehan,
T.
J.,
Fifield,
J.,
Reisine,
S.,
&
Tennen,
H.
(1995).
The
Measurement
Structure

of
the
Center
for
Epidemiologic
Studies
Depression
Scale.
Journal
of

Personality
Assessment,
64(3),
507-‐521.

Siegel,
K.,
&
Schrimshaw,
E.
W.
(2000).
Perceiving
benefits
in
adversity:
Stress-‐
related
growth
in
women
living
with
HIV/AIDS.
Social
Science
&
Medicine,

51(10),
1543-‐1554.

Skogrand,
L.,
Hatch,
D.,
&
Singh,
A.
(2005).
Understanding
Latino
families,

implications
for
family
education.
Retrieved
August,
2,
2010.

Smith,
B.
W.,
Dalen,
J.,
Bernard,
J.
F.,
&
Baumgartner,
K.
B.
(2008).
Posttraumatic

growth
in
Non-‐Hispanic
White
and
Hispanic
Women
with
cervical
cancer.

Journal
of
Psychosocial
Oncology,
26(4),
91-‐109.

Smyth,
J.
M.,
Hockemeyer,
J.
R.,
&
Tulloch,
H.
(2008).
Expressive
writing
and
post-‐
traumatic
stress
disorder:
Effects
on
trauma
symptoms,
mood
states,
and

cortisol
reactivity.
British
Journal
of
Health
Psychology,
13(1),
85-‐93.

Snaith,
R.
P.,
Hamilton,
M.,
Morley,
S.,
Humayan,
A.,
Hargreaves,
D.,
&
Trigwell,
P.

(1995).
A
scale
for
the
assessment
of
hedonic
tone
the
Snaith-‐Hamilton

Pleasure
Scale.
The
British
journal
of
psychiatry
:
the
journal
of
mental
science,

167(1),
99-‐103.

88

Stacy,
A.
W.,
Flay,
B.
R.,
Sussman,
S.,
Brown,
K.
S.,
Santi,
S.,
&
Best,
J.
A.
(1990).

Validity
of
alternative
self-‐report
indices
of
smoking
among
adolescents.

Psychological
Assessment:
A
Journal
of
Consulting
and
Clinical
Psychology,
2(4),

442.

Steinberg,
L.
D.
(1981).
Transformations
in
Family-‐Relations
at
Puberty.

Developmental
Psychology,
17(6),
833-‐840.

Stimson,
J.
A.,
Carmines,
E.
G.,
&
Zeller,
R.
A.
(1978).
Interpreting
polynomial

regression.
Sociological
Methods
&
Research,
6(4),
515-‐524.

Stump,
M.
J.,
&
Smith,
J.
E.
(2008).
The
relationship
between
posttraumatic
growth

and
substance
use
in
homeless
women
with
histories
of
traumatic
experience.

The
American
journal
on
addictions
/
American
Academy
of
Psychiatrists
in

Alcoholism
and
Addictions,
17(6),
478-‐487.

Sun,
G.
W.,
Shook,
T.
L.,
&
Kay,
G.
L.
(1996).
Inappropriate
use
of
bivariable
analysis

to
screen
risk
factors
for
use
in
multivariable
analysis.
Journal
of
clinical

epidemiology,
49(8),
907-‐916.

Sun,
P.,
Unger,
J.
B.,
Guo,
Q.,
Gong,
J.,
Ma,
H.,
Palmer,
P.
H.,
Chou,
C.
P.,
Li,
Y.,
Sussman,

S.,
Ritt-‐Olson,
A.,
Xiao,
L.,
&
Johnson,
C.
A.
(2007).
Comorbidity
between

depression
and
smoking
moderates
the
effect
of
a
smoking
prevention

program
among
boys
in
China.
Nicotine
&
tobacco
research
:
official
journal
of

the
Society
for
Research
on
Nicotine
and
Tobacco,
9
Suppl
4,
S599-‐609.

Sussman,
S.,
Dent,
C.
W.,
&
Galaif,
E.
R.
(1997).
The
correlates
of
substance
abuse
and

dependence
among
adolescents
at
high
risk
for
drug
abuse.
Journal
of

substance
abuse,
9,
241-‐255.

Sussman,
S.,
Dent,
C.
W.,
&
Leu,
L.
(2000).
The
one-‐year
prospective
prediction
of

substance
abuse
and
dependence
among
high-‐risk
adolescents.
Journal
of

substance
abuse,
12(4),
373-‐386.

Sussman,
S.,
Dent,
C.
W.,
&
McCullar,
W.
J.
(2000).
Group
self-‐identification
as
a

prospective
predictor
of
drug
use
and
violence
in
high-‐risk
youth.
Psychology

of
Addictive
Behaviors,
14(2),
192.

Sussman,
S.,
Earleywine,
M.,
Wills,
T.,
Cody,
C.,
Biglan,
T.,
Dent,
C.
W.,
&
Newcomb,
M.

D.
(2004).
The
motivation,
skills,
and
decision-‐making
model
of
"drug
abuse"

prevention.
Substance
use
&
misuse,
39(10-‐12),
1971-‐2016.

89

Sussman,
S.,
Milam,
J.,
Arpawong,
T.
E.,
Tsai,
J.,
&
Black,
D.
S.
(Provisionally
accepted).

Spirituality
in
Addictions
Treatment:
Wisdom
to
Know…What
It
Is.
Substance

use
&
misuse.

Sussman,
S.,
Stacy,
A.
W.,
Dent,
C.
W.,
Simon,
T.
R.,
Galaif,
E.
R.,
Moss,
M.
A.,
Craig,
S.,
&

Johnson,
C.
A.
(1995).
Continuation
high
schools:
youth
at
risk
for
drug
abuse.

Journal
of
drug
education,
25(3),
191-‐209.

Sussman,
S.,
&
Sun,
P.
(2009).
Youth
tobacco
use
cessation:
2008
update.
Tobacco

induced
diseases,
5,
3.

Sussman,
S.,
Sun,
P.,
Rohrbach,
L.
A.,
&
Spruijt-‐Metz,
D.
(2011).
One-‐year
outcomes
of

a
drug
abuse
prevention
program
for
older
teens
and
emerging
adults:

Evaluating
a
motivational
interviewing
booster
component.
Health

psychology
:
official
journal
of
the
Division
of
Health
Psychology,
American

Psychological
Association.

Sussman,
S.,
Sun,
P.,
Rohrbach,
L.
A.,
&
Spruijt-‐Metz,
D.
(2012).
One-‐year
outcomes
of

a
drug
abuse
prevention
program
for
older
teens
and
emerging
adults:

Evaluating
a
motivational
interviewing
booster
component.
Health

Psychology,
31(4),
476.

Sussman,
S.
Y.,
&
Ames,
S.
L.
(2008).
Drug
abuse:
concepts,
prevention,
and
cessation:

Cambridge
Univ
Pr.

Taylor,
S.
E.
(1983).
Adjustment
to
Threatening
Events
-‐
a
Theory
of
Cognitive

Adaptation.
American
Psychologist,
38(11),
1161-‐1173.

Taylor,
S.
E.,
&
Armor,
D.
A.
(1996).
Positive
illusions
and
coping
with
adversity.

Journal
of
Personality,
64(4),
873-‐898.

Taylor,
S.
E.,
Kemeny,
M.
E.,
Reed,
G.
M.,
Bower,
J.
E.,
&
Gruenewald,
T.
L.
(2000).

Psychological
resources,
positive
illusions,
and
health.
The
American

psychologist,
55(1),
99-‐109.

Tebes,
J.
K.,
Irish,
J.
T.,
Puglisi
Vasquez,
M.
J.,
&
Perkins,
D.
V.
(2004).
Cognitive

transformation
as
a
marker
of
resilience.
Substance
use
&
misuse,
39(5),
769-‐
788.

Tedeschi,
R.
G.
(1995).
Trauma
&
transformation
:
growing
in
the
aftermath
of

suffering.
Thousand
Oaks,
Calif.:
Sage
Publications.

90

Tedeschi,
R.
G.,
&
Calhoun,
L.
G.
(1995).
Trauma
&
transformation:
Growing
in
the

aftermath
of
suffering:
Sage
Publications,
Inc.

Tedeschi,
R.
G.,
&
Calhoun,
L.
G.
(1996).
The
Posttraumatic
Growth
Inventory:

measuring
the
positive
legacy
of
trauma.
Journal
of
traumatic
stress,
9(3),

455-‐471.

Tedeschi,
R.
G.,
&
Calhoun,
L.
G.
(2004).
Target
Article:
"Posttraumatic
Growth:

Conceptual
Foundations
and
Empirical
Evidence".
Psychological
Inquiry,

15(1),
1-‐18.

Tomich,
P.
L.,
&
Helgeson,
V.
S.
(2004).
Is
Finding
Something
Good
in
the
Bad
Always

Good?
Benefit
Finding
Among
Women
With
Breast
Cancer.
Health
Psychology,

23(1),
16-‐23.

Tsourtos,
G.,
Ward,
P.
R.,
Muller,
R.,
Lawn,
S.,
Winefield,
A.
H.,
Hersh,
D.,
&
Coveney,
J.

(2011).
The
importance
of
resilience
and
stress
to
maintaining
smoking

abstinence
and
cessation:
a
qualitative
study
in
Australia
with
people

diagnosed
with
depression.
Health
&
social
care
in
the
community,
19(3),
299-‐
306.

Unger,
J.
B.,
Kipke,
M.
D.,
Simon,
T.
R.,
Johnson,
C.
J.,
Montgomery,
S.
B.,
&
Iverson,
E.

(1998).
Stress,
coping,
and
social
support
among
homeless
youth.
Journal
of

Adolescent
Research,
13(2),
134-‐157.

Updegraff,
J.
A.,
Taylor,
S.
E.,
Kemeny,
M.
E.,
&
Wyatt,
G.
E.
(2002).
Positive
and

negative
effects
of
HIV
infection
in
women
with
low
socioeconomic
resources.

Personality
and
Social
Psychology
Bulletin,
28(3),
382-‐394.

Urcuyo,
K.
R.,
Boyers,
A.
E.,
Carver,
C.
S.,
&
Antoni,
M.
H.
(2005).
Finding
benefit
in

breast
cancer:
Relations
with
personality,
coping,
and
concurrent
well-‐being.

Psychology
&
Health,
20(2),
175-‐192.

Ursano,
R.
J.,
Bell,
C.,
Eth,
S.,
Friedman,
M.,
Norwood,
A.,
Pfefferbaum,
B.,
Pynoos,
J.,

Zatzick,
D.,
Benedek,
D.,
&
McIntyre,
J.
(2004).
Practice
guideline
for
the

treatment
of
patients
with
acute
stress
disorder
and
posttraumatic
stress

disorder.
The
American
journal
of
psychiatry,
161(11
Suppl),
3-‐31.

Wagner,
K.
D.,
Brief,
D.
J.,
Vielhauer,
M.
J.,
Sussman,
S.,
Keane,
T.
M.,
&
Malow,
R.

(2009).
The
potential
for
PTSD,
substance
use,
and
HIV
risk
behavior
among

adolescents
exposed
to
Hurricane
Katrina.
Substance
use
&
misuse,
44(12),

1749-‐1767.

91

Watson,
D.,
Clark,
L.
A.,
&
Tellegen,
A.
(1988).
Development
and
validation
of
brief

measures
of
positive
and
negative
affect:
the
PANAS
scales.
Journal
of

Personality
and
Social
Psychology,
54(6),
1063-‐1070.

Wiechelt,
S.
A.
(2007).
Trauma
and
substance
misuse:
critical
considerations
in

understanding
the
maelstrom.
Substance
use
&
misuse,
42(2-‐3),
527-‐533.

Wills,
T.
A.
(1986).
Stress
and
coping
in
early
adolescence:
relationships
to

substance
use
in
urban
school
samples.
Health
psychology
:
official
journal
of

the
Division
of
Health
Psychology,
American
Psychological
Association,
5(6),

503-‐529.

Wills,
T.
A.,
&
Cleary,
S.
D.
(1996).
How
are
social
support
effects
mediated?
A
test

with
parental
support
and
adolescent
substance
use.
Journal
of
Personality

and
Social
Psychology,
71(5),
937-‐952.

Wills,
T.
A.,
&
Shiffman,
S.
(1985).
Coping
and
substance
use:
A
conceptual

framework.
Coping
and
substance
use,
3-‐24.

Wills,
T.
A.,
Vaccaro,
D.,
&
McNamara,
G.
(1992).
The
role
of
life
events,
family

support,
and
competence
in
adolescent
substance
use:
a
test
of
vulnerability

and
protective
factors.
American
journal
of
community
psychology,
20(3),

349-‐374.

Young-‐Wolff,
K.
C.,
Kendler,
K.
S.,
&
Prescott,
C.
A.
(2012).
Interactive
effects
of

childhood
maltreatment
and
recent
stressful
life
events
on
alcohol

consumption
in
adulthood.
Journal
of
studies
on
alcohol
and
drugs,
73(4),

559-‐569.

Zimbardo,
P.
G.,
&
Boyd,
J.
N.
(1999).
Putting
time
in
perspective:
A
valid,
reliable

individual-‐differences
metric.
Journal
of
Personality
and
Social
Psychology,

77(6),
1271-‐1288.

Zimbardo,
P.
G.,
&
Boyd,
J.
N.
(1999).
Putting
time
in
perspective:
A
valid,
reliable

individual-‐differences
metric.
Journal
of
Personality
and
Social
Psychology,

77(6),
1271.

Zoellner,
T.,
&
Maercker,
A.
(2006).
Posttraumatic
growth
in
clinical
psychology-‐-‐A

critical
review
and
introduction
of
a
two
component
model.
Clinical

Psychology
Review,
26(5),
626-‐653.

Zoellner,
T.,
Rabe,
S.,
Karl,
A.,
&
Maercker,
A.
(2011).
Post-‐traumatic
growth
as

outcome
of
a
cognitive-‐behavioural
therapy
trial
for
motor
vehicle
accident

92

survivors
with
PTSD.
Psychology
and
Psychotherapy:
Theory,
Research
and

Practice,
84(2),
201-‐213.

Zweig,
J.
M.,
&
Institute,
U.
(2003).
Vulnerable
youth:
Identifying
their
need
for

alternative
educational
settings:
Urban
Institute
Washington
DC.

Asset Metadata

Creator Arpawong, Thalida Em (author)

Core Title Post-traumatic growth among high-risk youth: predictors, impact of stressful life events, and relationship with changes in substance use behaviors

Contributor Electronically uploaded by the author (provenance)

School Keck School of Medicine

Degree Doctor of Philosophy

Degree Program Preventive Medicine (Health Behavior)

Publication Date 07/12/2013

Defense Date 03/15/2013

Publisher University of Southern California (original), University of Southern California. Libraries (digital)

Tag Adolescents,alternative high school,emerging adults,Ethnicity,OAI-PMH Harvest,post-traumatic growth,stressful life events,substance use

Format application/pdf (imt)

Language English

Advisor Rohrbach, Louise Ann (committee chair), Sussman, Steven (committee chair), Land, Helen (committee member), Milam, Joel E. (committee member), Unger, Jennifer B. (committee member)

Creator Email arpawong@usc.edu

Permanent Link (DOI) https://doi.org/10.25549/usctheses-c3-287431

Unique identifier UC11292697

Identifier etd-ArpawongTh-1763.pdf (filename),usctheses-c3-287431 (legacy record id)

Legacy Identifier etd-ArpawongTh-1763.pdf

Dmrecord 287431

Document Type Dissertation

Format application/pdf (imt)

Rights Arpawong, Thalida Em

Type texts

Source University of Southern California (contributing entity), University of Southern California Dissertations and Theses (collection)

Access Conditions The author retains rights to his/her dissertation, thesis or other graduate work according to U.S. copyright law. Electronic access is being provided by the USC Libraries in agreement with the a...

Repository Name University of Southern California Digital Library

Repository Location USC Digital Library, University of Southern California, University Park Campus MC 2810, 3434 South Grand Avenue, 2nd Floor, Los Angeles, California 90089-2810, USA

Abstract (if available)

Abstract Background: The experience of a highly stressful life event (SLE) may elicit positive psychosocial change in some individuals, referred to as Post-traumatic Growth (PTG). This dissertation represents novel research in which two studies were designed to answer the following questions: (1) what predicts PTG, including personal and environmental characteristics as well as the number and severity of stressfulness of SLEs experienced?