ARTICULATION OF RESPONSE (CLARITY, ORGANIZATION, MECHANICS)The c.docx

ARTICULATION OF RESPONSE (CLARITY,
ORGANIZATION, MECHANICS)
The candidate provides adequate articulation of response.
A1. HEALTHCARE PROBLEM
Description is clear, logical, and reflects an authentic healthcare
problem.
A2. SIGNIFICANCE OF PROBLEM
Explanation reflects a clear and logical understanding of the
significance of the problem.
A3. CURRENT PRACTICE
Description is clear, accurate, and reflects the current practice
related to the problem.
A4. IMPACT ON BACKGROUND
Discussion is clear, logical, and reflects the impact on at least
one type of cultural background (patient and/or organizations).
B. PICO TABLE
PICO table is complete and correctly describes allthe elements
of the PICO.
B1. PICO QUESTION
Question is complete (includes all elements) and relevant to the
PICO.
C1. KEYWORDS
Keywords reflect a comprehensive approach to finding related
literature .
C2. NUMBER AND TYPES OF ARTICLES
Description is complete (includes number and types) and is
relevant to the literature search topic.
C2A. RESEARCH AND NON-RESEARCH EVIDENCE
Discussion is relevant and complete (includes 2 research and 2
non-research evidence sources)
D. EVIDENCE MATRIX
Attachment is complete (includes 5 sources), and the scholarly
journal sources are located in major medical databases.
E. RECOMMENDED PRACTICE CHANGE

Explanation is clear, informs a practice change that addresses
the PICO question, and is supported by the evidence.
F1. KEY STAKEHOLDERS
Explanation is congruent, logical, and addresses how to involve
the 3 stakeholders in the decision
F2. BARRIERS
Description is relevant, includes specific barriers, and clearly
applies the evidence.
F3. STRATEGIES FOR BARRIERS
identification is complete (includes 2 strategies) and is relevant
to the barriers
F4. INDICATOR TO MEASURE OUTCOME
Indicator is relevant and measures the outcome.
G. SOURCES
The submission includes in-text citations and references and
demonstrates a consistent application of APA style
Help document
A1. Healthcare Problem/ A2. Significance of Problem/ A3.
Current Practice / A4. Impact on Background
Write 1-2 paragraphs for each response. You may want to

include in text citations to support these sections. The A
section should only discuss the problem without any mention of
the intervention. The current practice should relate to the C in
the PICO. Discussion on how the problem impacts either the
workplace culture OR the patient's culture is required in A4.
B. PICO Table (Chapter 4) Download the table from
Taskstream or just include a 4 row by 2 column table in the
paper and use the PICO toolkit as a guide (Module Video 2 and
Toolkit, Appendix B).
B1. PICO Question (Chapter 4) Construct the question that
relates to the PICO elements in the table using the PICO
Question Development toolkit as a guide (Module Video 2.4 and
Toolkit, Appendix B).
Here is a question template:
Among (population), does (intervention) lower (problem) as
compared to the current practice?
C1. Keywords. This is just a list of the search terms used.
C2. Number and Types of Articles. Describe the number and
research types/subtypes of the articles you find in your search.
They may include research articles, quality improvement,
practice guidelines, pieces written to educate/inform, expert
opinion, and more. This is a bare-bones description of the
number and types of articles you find. For example, you might
begin by stating the number of articles that came up (10,000)
and the number you reviewed (20) then give a breakdown of the
types you reviewed (5 RCT, 5 Reviews, 5 Quality Improvement
projects, 5 Qualitative). (1 paragraph)
C2a. Research and Non-Research Evidence. (Chapters 6 & 7).
You can use 2 of your five research articles here to represent
the 2 research studies. Be sure to state if the publication is a
research or non-research evidence. Do not use an article with

the words systematic review or integrative review in the title as
an example of non-research evidence. This will be 4 paragraphs
describing the 2 research (Level I-III) and 2 non-research
(Level IV-V) studies found during the search. Each paragraph
should include the in-text citation for that article, for example:
The first research study found … (Smith & Jones, 2014).
Then write a few sentences about that research study and do the
same for the other 3 articles. Review the research evidence
appraisal toolkit and Chapter 6 for the research evidence
articles.
Review the non-research evidence appraisal toolkit and Chapter
7 for the non-research evidence articles.
D. Evidence Matrix. Download the matrix from Taskstream.
Enter the 5 research articles (Level I - III) in the table using
the level and quality guide toolkit to determine the correct
level. The research design/type should be quantitative,
qualitative, or mixed methods. Remember, level 1 are
randomized controlled trials only, level 2 are quasi-
experimental only, and everything else is level 3. For
systematic and integrative reviews, read the level 1-3
definitions in Appendix C. The sample size for review studies
are the number of articles reviewed. Please read chapter 6 & 7
and be sure not to include non research evidence such as quality
improvement projects in the table (Module Video 3 Toolkit,
Appendix C:Level and quality guide).
E. Recommended Practice Change. This section should be a
minimum of 6 sentences. The first sentence names the single
intervention that all the articles in the matrix support. Then
write one sentence each for the 5 studies in the matrix on how
each article used the intervention to decrease the stated
problem. Each of the five sentences must include an in text
citation for that research study. For example, Intentional

rounding was shown to decrease falls by 20% over 3 months
(Smith, Rogers, & Stein, 2014). (1-2 paragraphs)
References Video for help with in text citations (2 minutes)
F1. Key Stakeholders. (Chapter 3, p. 41 & Chapter 8, p. 155) A
stakeholder is anyone who has something to win or lose from
this change. Also, think about those whom you need to engage
in the process to make it a lasting change by involving them in
the decision to make the change. They can be floor nurses,
nurse educators, nurse managers, nurse administrators, etc. You
will need to mention at least 3 different key stakeholders. For
each stakeholder, name a strategy to involve them in the
decision to change the practice. (1-2 paragraphs)
F2. Barriers. (Chapter 9, p. 177) This section is a discussion of
the barriers to change. You may want to talk about change,
delegation of tasks, training/education, funding, and attitude
toward research as these are areas that cause barriers to
initiation and continuing compliance with a practice change.
Mention one barrier related to change and one barrier related to
translation of research into practice. (1 paragraph)
F3. Strategies for Barriers. (Chapter 9, p. 180) This section will
discuss 2 strategies to overcome the identified barriers in
section F2. (1 paragraph)
F4. Indicator to Measure Outcome. (Chapter 1, p. 9 & Chapter
8, p. 154 - 155) An indicator is something that can be measured.
For example, if the I in the PICO is “hourly rounding to
prevent falls” the outcome measure would be the number of
falls. Describe how you would measure an improvement after
the practice change. (1 paragraph)
G Sources. Remember to include 5 in-text citations in section E
from the 5 research studies in the Evidence Matrix. Also,
remember that the reference page should include all 7 studies

mentioned in the paper, assuming that the 2 research studies in
C2a are also included in the 5 research studies in the Evidence
Matrix.
For reference page and in text citations: References rules
A. Write a brief summary (suggested length of 2–3 pages)
of the significance and background of a healthcare problem by
doing the following:
1. Describe a healthcare problem.
Note: A healthcare problem can be broad in nature or focused.
2. Explain the significance of the problem.
3. Describe the current practice related to the problem.
4. Discuss how the problem impacts the organization and/or
patient’s cultural background (i.e., values, health behavior, and
preferences).
B. Complete the attached “PICO Table Template” by
identifying all the elements of the PICO.
1. Develop the PICO question.
C. Describe the search strategy (suggested length of 1–2 pages)
you used to conduct the literature review by doing the
following:
1. Identify the keywords used for the search.
2. Describe the number and types of articles that were available
for consideration.
a. Discuss two research evidence and two non-research
evidence sources that were considered (levels I–V).
Note: Be sure to upload a copy of the full text of the aritcles
with your submission

D. Complete the attached “Evidence Matrix” to
list five research evidence sources (levels I–III) from scholarly
journal sources you locate in major medical databases.
Note: Four different authors should be used for research
evidence. Research evidence must not be more than five years
old.
Note: You may submit your completed matrix as a separate
attachment to the task or you may include the matrix within
your paper, aligned to APA standards.
Note: Be sure to upload a copy of the full text of the articles
with your submission.
E. Explain a recommended practice change (suggested length of
1–3 pages) that addresses the PICO question within the
framework of the evidence collected and used in the attached
“Evidence Matrix.”
F. Describe a process for implementing the recommendation
from part E (suggested length of 2–3 pages) in which you do the
following:
1. Explain how you would involve three key stakeholders in the
decision to implement the recommendation.
2. Describe the specific barriers you may encounter in applying
evidence to practice changes in the nursing practice setting.
3. Identify two strategies that could be used to overcome the
barriers discussed in F2.
4. Identify one indicator to measure the outcome related to the
recommendation.
G. Acknowledge sources, using APA-formatted in-text citations
and references, for content that is quoted, paraphrased, or
summarized
PICO Table

Example:
P (patient/problem)
Hospital acquired infection
I (intervention/indicator)
Handwashing
C (comparison)
Personal protective equipment, thorough physical assessments,
proper room sanitization, private room
O (outcome)
Reduced infection
Evidence Matrix
Authors
Journal Name/ WGU Library
Year of Publication
Research Design
Sample Size
Outcome Variables Measured
Level (I–III)
Quality (A, B, C)
Results/Author’s Suggested Conclusions

Psychological Problems among aid workers
oPerating in darfur
Saif ali MuSa and abdalla a. R. M. HaMid
United Arab Emirates University, Al Ain, United Arab Emirates
Aid workers operating in war zones are susceptible to mental
health problems that could
develop into stress and acute traumatic stress. This study
examined the relationships between
burnout, job satisfaction (compassion satisfaction), secondary
traumatic stress (compassion
fatigue), and distress in 53 Sudanese and international aid
workers in Darfur (mean age =
31.6 years). Measures used were the Professional Quality of
Life Questionnaire (ProQOL;
Stamm, 2005), the Relief Worker Burnout Questionnaire
(Ehrenreich, 2001), and the General
Health Questionnaire (Goldberg & Williams, 1991). Results
showed that burnout was
positively related to general distress and secondary traumatic
stress, and negatively related
to compassion satisfaction. Sudanese aid workers reported
higher burnout and secondary
traumatic stress than did international workers. Results are
discussed in light of previous
findings. It was concluded that certain conditions might
increase aid workers’ psychological
suffering and relief organizations need to create positive work
climates through equipping aid
workers with adequate training, cultural orientation, and
psychological support services.
Keywords: Darfur, burnout, distress, compassion satisfaction,
secondary traumatic stress.

Sudan, the largest country in Africa, is located in northeast
Africa. It is
considered one of the least developed countries, and ranks 139
in the 2004 United
Nations’ Development Program’s Human Development Index
(UNDP, 2004).
The Darfur region of western Sudan is composed of three states;
north Darfur,
SOCIAL BEHAVIOR AND PERSONALITY, 2008, 36 (3), 407-
416
© Society for Personality Research (Inc.)
407
Saif Ali Musa, PhD, Assistant Professor, Program of Social
Work, United Arab Emirates University,
Al Ain, United Arab Emirates; Abdalla A. R. M. Hamid, PhD,
Associate Professor, Program of
Psychology, United Arab Emirates University, Al Ain, United
Arab Emirates.
Appreciation is due to reviewers including: Taha Amir, PhD,
and Hamzeh Dodeen, Department of
Psychology, United Emirates University, P.O. Box 17851, Al
Ain, United Arab Emirates, Email:
[email protected] or [email protected]
Please address correspondence and reprint requests to: Dr. Saif
Ali Musa, Assistant Professor of
the Social Work Program, United Arab Emirates University,
P.O. Box 17771, Al Ain, United Arab
Emirates. Phone: (971) 50 7134072; Fax: (971) 3 7671705;
Email [email protected]; Dr.
Abdalla Hamid, Associate Professor of the Psychology Program,
United Arab Emirates University,
P.O. Box 17771, Al Ain, United Arab Emirates. Phone: (971) 50

7131 823; Fax: (971) 3 7670 453;
Email: [email protected]
PSYCHOLOGICAL PROBLEMS AMONG AID WORKERS408
west Darfur and south Darfur. These three states are 250,000
square kilometers
in area with an estimated population of 6 million. The starting
point of the armed
conflict in Darfur region is typically said to be February 26,
2003 (Wikipedia,
2007). This conflict resulted in a complex humanitarian crisis
which necessitated
the intervention of both national and international aid agencies.
Research on stress and mental health problems suffered by aid
workers in
their efforts to help traumatized individuals in prolonged
complex emergency
situations is scarce and still a new field (Adams, Boscarion, &
Figley, 2006).
The bulk of research has focused on the wellbeing of
peacekeepers and armed
personnel and traumatic events facing them (Cardozo et al.,
2005). Aid workers
operating in war zones encounter situations that are likely to
generate more
distress than would normal, everyday situations (Salama, 2007).
They are
susceptible to stress and acute traumatic stress (McFarlane,
2004) as a result of
dealing with victims and being trapped in difficult situations.
Health problems suffered by aid workers include physical

illnesses, psychological
morbidity (such as distress, posttraumatic stress disorder,
alcohol abuse, anxiety
and depression), and even death (McFarlane, 2004). Other
problems include risk-
taking behavior, psychosomatic disorders (Salama, 2007),
nondirected anger,
intrusive thoughts, and fear of the future (Omidian, 2001).
International aid
workers may also be exposed to culture shock and lack of
support provided by
family or a partner, close friends, and their own culture
(Salama).
Aid workers who come to know the stories of fear, pain, and
suffering of
victims may experience similar feelings because they care. This
makes them
vulnerable to secondary traumatic stress (compassion fatigue) as
the emotional
residue of exposure to working with victims suffering from the
consequences of
a traumatic event (ACE-Network, 2007). According to Figley
(1995), secondary
traumatic stress is “natural consequent behaviors resulting from
knowledge
about a traumatizing event experienced by a significant other”
(Perry, 2003). The
symptoms may include avoiding things that are reminders of the
event, having
difficulty sleeping, or being afraid (Stamm, 2005). Researchers
and practitioners
have recently acknowledged that professionals who work with
or help traumatized
people are indirectly or secondarily at risk of developing the
same symptoms as

the individuals who are exposed directly to the trauma (Perry).
Perry (2003) advances several reasons that aid workers or
professionals
working with traumatized victims are at increased risk of
developing secondary
trauma: (a) empathizing with victims leads aid workers to
become vulnerable to
internalizing some of the victim’s trauma-related pain; (b) aid
workers would
have to listen to the same or similar stories over and over again
without sufficient
recovery time; (c) many aid workers have had some traumatic
experiences in
their own life and the pain of such experiences can be
reactivated when they
work with an individual who has suffered a similar trauma; and
(d) the current
PSYCHOLOGICAL PROBLEMS AMONG AID WORKERS 409
practices in the fields of relief and mental health are based on
individual service
delivery rather than on team-oriented practice and such
practices within a
fragmented system (i.e., camps for refugees or internally
displaced persons where
the turnover is high) are considered to set aid workers up for
increased stress.
There are some findings suggesting burnout is prevalent among
aid workers
in complex emergency situations (Cardozo et al., 2005).
Burnout is a state of

physical, mental and emotional exhaustion resulting from
prolonged demanding
and stressful situations (Pines, Aronson, & Kafry, 1981).
According to Stamm
(2005, p. 12), burnout is “associated with feelings of
hopelessness and difficulties
in dealing with work or in doing your job effectively. These
negative feelings
usually have a gradual onset. They can reflect the feeling that
your efforts make
no difference, or they can be associated with a very high work
load or a non-
supportive work environment”. Burnout symptoms can be
categorized into five
groups, namely, emotional, interpersonal, physical, behavioral,
and work-related
components (Salama, 2007). The emotional component includes
feelings such
as depression, anxiety, irritability, and helplessness. The
interpersonal category
comprises self-distancing, social withdrawal, and inefficient
communication.
The physical element involves sleep difficulties, fatigue,
exhaustion, headaches,
and stomachaches. The behavioral aspect includes alcohol
abuse, aggression,
pessimism, and cruelty. The work-related element includes poor
performance,
tardiness, and absenteeism.
In their study of stress and burnout amongst prehospital
emergency teams,
Louville, Jehel, Goujon, and Bisserbe (1997) found that women
and hospital-
based personnel scored significantly higher on depression and
distress measured

by the General Health Questionnaire (GHQ; Goldberg &
Williams, 1991),
and trait and state anxiety than did men and personnel who
intervene in the
emergency field.
Compassion satisfaction, according to Stamm (2005, p. 12), is
about “the
pleasure you derive from being able to do your work well. For
example, you
may feel like it is a pleasure to help others through your work”.
Conrad and
Kellar-Guenther (2006) studied compassion satisfaction,
compassion fatigue,
and burnout among Colorado child protection workers using the
Compassion
Satisfaction/Fatigue Self Test. They found that about 50% of
the protection
staff experienced high or very high levels of compassion fatigue
and low levels
of burnout. High compassion satisfaction was associated with
reduced fatigue
and lower levels of burnout. Most of participants (70%) had
high scores for
compassion satisfaction. The authors concluded that compassion
satisfaction
might alleviate the effects of burnout.
Cardozo et al. (2005) conducted a study with 285 expatriate aid
workers
and 325 Kosovar Albanian aid workers from 22 humanitarian
organizations
carrying out health projects in Kosovo. The study was
concerned with mental

health problems related to exposure to traumatic events. Their
results showed
that younger expatriates reported significantly more depressive
symptoms and
more nonspecific psychiatric morbidity as measured by the
GHQ. Kosovar and
expatriate aid workers with a history of psychiatric illnesses
also demonstrated
higher levels of depressive symptoms and nonspecific
psychiatric morbidity.
The present study attempted to identify psychological health
problems suffered
by aid workers assisting victims in Darfur, and aimed to explore
the relationship
between these problems and burnout and job satisfaction rates
among aid
workers. Because of the hostile and difficult work environment,
as witnessed by
the authors inside internally displaced persons camps, it was
expected that high
rates of burnout and psychological disturbances would be found.
method
ParticiPants
Participants were 53 humanitarian aid workers representing 11
relief
organizations operating in camps in Darfur, around the towns of
Nyala (40%)
and Fasher (60%). The sample was randomly selected from aid
workers who

had firsthand experience with victims inside the camps. The
ages of participants
ranged between 20 and 55 years (mean age = 31.6 years). Forty-
six percent of
them were married, 51% single, and 3% were divorced or
separated. Sixty percent
of the participants were Sudanese nationals while 39.6% were
international aid
workers. The percentages of male and female participants were
49.0% and 43.4%
respectively.
Materials and Procedures
Participants were requested to complete three questionnaires.
The goal of the
research and its importance were explained to them. The first
questionnaire was
the 30-item Professional Quality of Life Questionnaire
(ProQOL; Stamm, 2005).
It measures compassion satisfaction, compassion fatigue
(secondary traumatic
stress), and burnout. This questionnaire was subjected to factor
analysis using
equal variances (weights) as prior communality estimates. The
factor axis method
was used to extract the factors, and this was followed by a
varimax (orthogonal)
rotation. In our study only the first two factors displayed
eigenvalues greater
than 1, and the results of a scree test also suggested that only
the first two factors
were retained for rotation. Combined, factors 1 and 2 accounted
for 32.60% of
the total variance.

In interpreting the rotated factor pattern, an item was said to
load on a given
factor if the factor loading was 0.45 or greater for that factor,
and was less than
0.45 for the other. Using these criteria, 17 items were found to
load on the first
factor, which was subsequently labeled secondary traumatic
stress (STS) or
compassion fatigue. The average score on the STS scale was 45
(SD = 16.0,
alpha reliability = 0.87). Instead of having cutoff scores to
indicate relative risks
or protective factors, conservative quartile method was used
with high (top 25%),
middle 50%, and low (bottom 25%). This method has been
found to be useful for
screening (Stamm, 2005).
Six items loaded on the second factor, which was labeled
compassion
satisfaction (CS). The average score on CS was 23.8 (SD = 5.0,
alpha reliability
= 0.72). Higher scores on this scale indicate greater satisfaction
with one’s ability
to be an effective aid worker.
The second questionnaire was the 13-item Relief Worker
Burnout Questionnaire
(Ehrenreich, 2001) which is designed to help detecting burnout
among aid
workers. A score of 0-15 suggests the aid worker is probably

coping adequately
with the stress of work. A score of 16-25 suggests suffering
from work stress and
preventive action is recommended. A score of 26-35 suggests
possible burnout.
A score above 35 indicates probable burnout (Ehrenreich). In
our study the
average score was 15.0 (SD = 7.5, alpha reliability = .73).
The third questionnaire was the General Health Questionnaire
(GHQ-28
items; Goldberg & Williams, 1991). This questionnaire
comprises four subscales
gauging anxiety, depression, somatic symptoms, and social
dysfunction. The
GHQ is a well validated instrument for measuring nonpsychotic
psychiatric
disorders in both clinical and community settings. There are two
methods of
scoring the GHQ; the first is the GHQ scaling method (0,0,1,1)
and the second
is the Likert scaling method (0,1,2,3). The former is appropriate
for recognizing
psychiatric cases and the latter for survey research (Swallow,
Lindow, Masson,
& Hay, 2003). For differentiating psychiatric from
nonpsychiatric cases the GHQ
scoring system with a cutoff point of 4 or more is usually used.
This system
was used in the present study. When using the Likert system,
GHQ total score
measures general distress. In our study the mean score on this
scale was 5.2 (SD
= 5.2, alpha reliability = 0.85).
results

Factor analysis was performed on the ProQOL to find out
whether or not the
factors yielded were consistent with those of Stamm (2005).
Unlike Stamm’s
study which produced three factors, secondary traumatic stress,
burnout, and
compassion satisfaction, the present study produced two factors;
secondary
traumatic stress (STS) and compassion satisfaction (CS). This
difference may be
attributed to the overlap between secondary traumatic stress and
burnout. Stamm
(p. 5) stated that “we do not see high scores on burnout with
high satisfaction,
but there is a particularly distressing combination of burnout
with trauma”. About
25% of aid workers in our study scored below 35 on secondary
traumatic stress,
and about 25% of them scored above 60.
Concerning compassion satisfaction, the quartile method
revealed that 25% of
aid workers scored higher than 26 and about 25% of them
scored below 21. As
for burnout, 63% of participants scored 15 or less, 21% scored
16-25, and 16%
scored higher than 25. The GHQ results showed that 50% of the
aid workers in
our study scored less than 4, while 50% scored higher than 4.

Correlation analysis showed that burnout was positively related
to general
distress and secondary traumatic stress and negatively related to
compassion
satisfaction (see Table 1). Burnout was further positively
related to depression,
anxiety, somatic symptoms, and social dysfunction (see Table
1). Compassion
satisfaction was negatively associated with general distress,
anxiety, and social
dysfunction (see Table 1). Age was negatively related to
burnout and secondary
traumatic stress (see Table 1). The older participants
experienced less burnout
and secondary traumatic stress than did the younger workers.
TAble 1
the coefficients of correlation Between different VariaBles
Variable Burnout Compassion satisfaction Secondary traumatic
stress Depression
Somatic symptoms .57** - - -
Distress .71** -.33* - -
Anxiety .61** -.28* - -
Social dysfunction .52** -.28* -
Age -.37** - -.46**
Burnout - .35** .48** .30*
Note: * p < .05; ** p < .01
The t test results indicated significant difference between the
two sexes
in burnout, (t = 2.901, df = 47, p < 0.01). Female participants
scored higher
than males. No significant sex differences were found in

secondary traumatic
stress, compassion satisfaction, general distress, and GHQ
subscales. There
were significant differences between Sudanese and international
aid workers in
burnout and secondary traumatic stress (t = 2.608, df = 49, p <
0.05; t = 4.389,
df = 49, p < 0.001, respectively). Sudanese aid workers
suffered more burnout
and secondary traumatic stress.
Responses on burnout were divided into two groups: scores less
than average
(15) represents group A, and scores more than 15 represent
group B. There were
significant differences between the two groups in t test results
for anxiety, social
dysfunction, somatic symptoms, secondary traumatic stress, and
general distress
(t = 3.657, df = 50, p < 0.01; t = 2.862, df = 50, p < 0.01; t =
2.654, df = 50, p <
0.05; t = 4.422, df = 51, p < 0.001, t = 3.801, df = 50, p <
0.001, respectively).
Those participants with higher than average scores for burnout
reported more
symptoms of the above mentioned disturbances. Those who
were classified as
nonpsychotic psychiatric cases, according to the GHQ scoring
system, scored
higher on burnout than those who were not (t = 4.827, df = 50, p
< 0.001).

No significant differences were found between the two groups
in secondary
traumatic stress or compassion satisfaction.
discussion
In the present study, 25% of aid workers scored higher than 60
(the top quartile)
on secondary traumatic stress. According to Stamm (2005), if
the individual’s
score on the secondary traumatic stress scale is in the top
quartile, s/he may
want to take some time to think about what may be causing
distress to him/her
at work, or if there is some other reason for the elevated score.
While higher
scores do not mean that s/he has a problem, they are an
indication that s/he may
want to examine how s/he feels about his/her work and work
environment. The
person may wish to discuss this with his/her supervisor, a
colleague, or a health
care professional (Stamm). In our study the quartile method
suggested by Stamm
yielded the result that 25% of the participants had high levels of
secondary
traumatic stress. However, we believe that this percentage could
be far lower than
the actual incidence of secondary traumatic stress among aid
workers. The GHQ
results support this claim by showing that 50% of aid workers in
Darfur could
be classified as nonpsychotic psychiatric cases. This may be due
either to the
stressfulness of the working environment and the seriousness of
the adjustment

problems encountered by aid workers or to the tendency of
maladjusted
individuals to choose to become aid workers. This percentage of
potential clinical
cases (50%) is far higher than the percentages found by Cardozo
et al. (2005)
in Kosovar Albanian (11.5%) and expatriate aid workers
(16.9%). However, it
should be noted that the cutoff points used with the GHQ total
score were 8 and
9 for the Kosovar Albanian and expatriate aid worker,
respectively. These cutoff
points are higher than the usual one (4) which was used in the
present study. The
psychiatric morbidity rate in aid workers in the present study
was comparable
to Chung, Chung, and Easthope’s (2000) findings in people in a
community
exposed to traumatic stress related to an aircraft crash (56%),
and higher than
Chung, Farmer, Werrett, Easthope, and Chung’s (2001) findings
that 35% of
people exposed to a train disaster scored higher than 4 on the
GHQ total score.
Concerning compassion satisfaction, 25% of the aid workers
scored higher
than 26. This suggests that these workers derive a good deal of
professional
satisfaction from their work. Those (25%) who scored below 21
can be described
as very dissatisfied and may either have problems with their
job, or there may be
other reasons for deriving satisfaction from activities other than
their job (Stamm,
2005).

With regard to burnout, it seemed that the majority of the
participants (63%)
were able to cope, in one way or another, with their work stress
and therefore
there were low levels of burnout. The remaining 37%, who
scored 16 or above on
total burnout, could be described as suffering work stress and
possible burnout.
The latter group might need professional psychological help to
assist them with
their sufferings.
The positive relationship between burnout and secondary
traumatic stress is
consistent with the findings of Conrad and Kellar-Guenter
(2006) who found
high levels of compassion satisfaction in individuals with low
levels of burnout.
This is also consistent with the work of McFarlane (2004) who
found that aid
workers experienced chronic hassles that could develop into
stress and acute
traumatic stress. Hence, secondary traumatic stress might be
one of the factors
through which burnout manifests itself.
The positive associations between burnout and general distress
and three
of the subscales measured by the GHQ (anxiety, somatic
symptoms, and
social dysfunction) are consistent with the findings of Louville

et al. (1997)
who reported that in women and a hospital-based emergency
team burnout
was strongly related to depression and distress measured by the
GHQ. These
results suggest that the burnout process might involve
experiencing a variety
of psychological disturbances and distress that exceed the aid
worker’s ability
to cope and thus lead the person to total collapse. Conrad and
Kellar-Guenther
(2006) found that burnout could result from exposure to
prolonged and extreme
job stress that consequently leads the aid worker to stop doing
the job. If this
is the case, controlling for psychological stress can help
mitigating burnout.
Providing psychological support can help eliminating the
feelings of burnout
and aid work staff turnover. The higher levels of burnout
reported by female
participants compared to male participants could be explained
by women’s need
to prove themselves in more than one capacity: to be wife,
mother, woman and
employee (see for example, Brunt, 2007). In addition, women,
by nature, tend
to be more involved in relationships with others, such as
pleasing and serving
others (Brunt).
The negative association between compassion satisfaction (job
satisfaction)
and burnout supports previous research (i.e., Conrad & Kellar-
Guenther, 2006).
The negative associations of compassion satisfaction with

general distress,
anxiety, and social dysfunction, support the literature relating
job satisfaction to
reduced levels of psychological distress. It could be that lack of
job satisfaction
leads to these feelings or vice versa.
The negative associations of age with burnout and secondary
traumatic stress
suggest that age may moderate the influence of these problems
on aid workers.
Older aid workers may be more mature and rational in their
responses to the
demands and associated stress of their job.
The high rates of burnout and secondary traumatic stress in
Sudanese aid
workers compared to the international aid workers might be
attributed to the
fact that the vast majority of Sudanese aid workers are from
Darfur and they
themselves are displaced victims of the war. So, their symptoms
may be triggered
by the combined effect of primary and secondary traumatic
stress. Sudanese aid
workers are more likely to understand and relate to the tragic
stories told by
victims who were traumatized because they are familiar with the
local dialects.
conclusions and imPlications

This study provides vital information for humanitarian
organizations, for
aid workers operating in war zones and researchers interested in
this field. Aid
workers in Darfur encounter several serious adjustment
problems. The high
incidence of secondary traumatic stress, psychiatric illnesses,
and burnout among
aid workers can be attributed to experiencing difficult situations
such as being
in direct contact with highly traumatized victims and a hostile
environment. Aid
workers tend to be blamed by victims for shortages in services
(food, water,
shelter, security etc.). This condition may explain the high
percentage of aid
workers who took little satisfaction from their work. An
alternative explanation is
that there is a high incidence of people with psychological
problems choosing to
become aid workers. The implication of this study is that
managers and directors
of aid organizations should create a positive work climate for
their coworkers
through equipping aid workers with adequate training, cultural
orientation, and
psychological support services.
It is recommended that an academic discipline specializing in
the humanitarian
field should be established. This discipline needs to focus on
education, training,
and research and would help make use of the great source of
data that could be
scientifically analyzed to reach conclusions which contribute to
improving the

practice of humanitarian assistance in all relevant fields.
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The Effects of Vicarious Exposure to the Recent Massacre at
Virginia Tech
Carolyn R. Fallahi and Sally A. Lesik
Central Connecticut State University
The authors examined whether exposure to the April 2007
Virginia Tech school
shootings would increase symptoms of acute stress in students
at another university
who were not personally involved, but who followed the case
vicariously through news
media. The authors ran a series of regression analyses using
multinomial logit (MNL)
models, a methodology which can be used for dealing with
categorical outcome
variables. The authors found that as TV viewing of the Virginia
Tech case increased,
the probability that a student would respond with moderate or
acute stress symptoms
also increased. The authors were able to describe the magnitude
of the relationship
between vicarious exposure to the Virginia Tech case and acute
stress symptomatology.
Keywords: Virginia Tech tragedy, vicarious exposure,

multinomial logit models, acute stress
disorder, posttraumatic stress disorder
On April 16, 2007, Cho Seung-Hui, a 23-year-
old student, murdered 32 people and wounded 25
others before committing suicide at Virginia Poly-
technic Institute (Virginia Tech) in Blacksburg,
Virginia. It was the deadliest campus shooting in
the history of the United States. The media cov-
erage of this incident was extensive with daily
images of the shooter and victims available on
news stations throughout the country for weeks
following the incident. We wondered about the
effects on students at another university who were
not personally involved with the Virginia Tech
shootings, but who followed the case vicariously
through news media.
Responses to Other Tragedies
The destruction of the World Trade Center is
probably the most “imaged disaster in human
history” (Mason, 2004, p. 1). Symptoms were
not contained to New York, people living
throughout the United States experienced symp-
toms of stress as a direct result of 9/11 (Schuster
et al., 2001; Stein et al., 2004). A study of
children who experienced indirect exposure to
9/11 through the media showed increased levels
of worry and posttraumatic stress at levels com-
parable to those in children experiencing the
disasters directly (Lengua, Long, Smith, &
Meltzoff, 2005). This research confirms Pfeffer-
baum, Gurwitch, et al. (2000) and Pfefferbaum,
Seale, et al. (2000) who found that children

experienced symptoms of posttraumatic stress
disorder (PTSD) 2 years after the Oklahoma
City bombing if they lived within 100 miles of
the bombing and had a connection to someone
who either died or was hurt in the bombing.
Spang (1999) found few symptoms of distress
among adults living 900 miles away from Okla-
homa City as compared to the Oklahoma City
residents 6 months after the Oklahoma City
bombing. Stein et al. (2004) found that 2–3
months following the 9/11 terrorist attacks,
many adults continued to show stress-related
symptoms as a direct result of the attacks. Fol-
lowing the explosion of the Shuttle Challenger
in 1986, Terr, Block, Michel, Shi, Reinhardt,
and Metayer (1999) studied the responses of
children living in the east and west coast both
5–7 weeks and 14 months after the explosion.
They found that children who watched the Chal-
lenger explode and cared more about the teacher
on the Challenger, demonstrated more symp-
toms of PTSD initially.
Carolyn R. Fallahi and Sally A. Lesik, Department of
Psychology and Department of Mathematical Sciences,
Central Connecticut State University.
We thank Lisa Leishman, Melissa Cotter, and Sara R.
Fallahi, who coded much of the data for this study; Sally K.
Laden, who provided editorial assistance; and Dr. Bradley
Waite for his comments on earlier drafts of this article.
Correspondence concerning this article should be ad-
dressed to Carolyn R. Fallahi, Central Connecticut State
University, Department of Psychology, 208 Marcus White
Hall, 1615 Stanley Street, New Britain, CT 06050-4010.

E-mail: [email protected]
Psychological Trauma: Theory, Research, Practice, and Policy
© 2009 American Psychological Association
2009, Vol. 1, No. 3, 220 –230 1942-9681/09/$12.00 DOI:
10.1037/a0015052
220
On April 20, 1999, Littleton, Colorado expe-
rienced the deadliest act of school violence re-
corded in history prior to the Virginia Tech
case. Two students killed 12 students and one
teacher while wounding 21 others before com-
mitting suicide. The images of this tragedy were
aired continuously (Addington, 2003). Colum-
bine students throughout the country reported
fear of victimization at school (Brooks,
Schiraldi, & Ziedenberg, 2000). Addington
(2003) found students responded to Columbine
with an increase in fear at school, albeit a small
increase. This brings up an important question:
does exposure to a disaster vicariously increase
psychiatric symptomatology in samples of peo-
ple not directly exposed to the tragedy?
Media Violence Research
During the last half century, the negative
short-term and long-term effects of media vio-
lence have been well documented. Exposure to
media violence has been associated with the
formation of aggressive scripts in memory, hos-
tile attributional biases, and aggressive beliefs
(Huesmann, Moise-Titus, Podolski, & Eron,

2003). There is also a link between a heavy diet
of media violence and later aggression (Bush-
man & Anderson, 2001; Paik & Comstock,
1994). Further, the effects of media violence
include the augmentation of negative mood
states (Caprara, Renzi, Amolini, D’Imperio &
Travaglia, 1984), including aggressive emo-
tions (Anderson et al., 2003). Viewing indirect
aggression on TV early in life has been shown
to predict actual indirect aggression in real life
(Huesmann et al., 2003; Coyne & Archer,
2005).
Vicarious exposure in terms of the number of
hours of media viewing of 9/11 was studied by
Blanchard et al. (2004). They found that vicar-
ious exposure, as measured by the number of
hours of media viewing of 9/11 events, pre-
dicted acute stress disorder scores in two sam-
ples of college students who were not geograph-
ically positioned near New York City. Pfeffer-
baum, Gurwitch, et al. (2000) also found that
media exposure and indirect interpersonal ex-
posure, as defined by having a friend who knew
someone hurt or killed in the bombing, were
significant predictors of symptomatology. Gil-
Rivas, Holman, and Silver (2004) found that
adolescents who were indirectly exposed
through media coverage to the events of 9/11
experienced mild to moderate acute stress
symptoms, especially when there was signifi-
cant conflict with their parents. Propper, Stick-
gold, Keeley, and Christman (2007) found that
there was a strong relationship between media
exposure for 9/11 and changes in dream features

following the attack. Specifically, they found
that with every hour of TV viewing, this re-
sulted in a 5% to 6% increase in September 11th
dream references. Talking about the events with
friends and relatives was not significantly re-
lated to specific dream references. The authors
concluded that the media may have a “deterious
impact on the emotional well-being of U.S.
citizens in the aftermath of September 11” (p.
340). Pfefferbaum, Gurwitch, et al. (2000)
found that following the Oklahoma City bomb-
ing, children who lost a friend reported signif-
icantly more symptoms of PTSD than those
who lost an acquaintance. However, when look-
ing at children within the community, there was
not a significant difference between those per-
sonally involved in the bombing, for example,
knew someone that was hurt or killed, and those
children not personally involved. The authors
speculate that this may be due to the fact that
children were watching only bomb-related pro-
gramming that aired for months after the explo-
sion. They speculated that high levels of media
coverage contributed to the symptoms of the
children within the community.
Rosen, Quyen, Cavella, Finney, and Lee
(2005) found that exposure to 9/11 images did
not change the average symptoms of distress in
a group of chronic PTSD patients. However,
they reported an increase in their perceptions of
stress and the authors speculate that the 9/11
events may have caused the patients to misat-
tribute fluctuations in chronic symptoms to the
recent terrorist attacks.

The News Media
News coverage can be a vehicle through
which individuals can experience indirect vic-
timization (Warr, 1994). The news media can
play an important role in defining ‘what is a
public tragedy’ (Balk, 2004). The news media
can present a distorted image of the risks that
individuals face in response to tragedies like
Columbine’s school shootings (Brooks,
Schiraldi, & Zeidenberg, 2000). Public trage-
221EFFECTS OF VICARIOUS EXPOSURE TO VIRGINIA
TECH
dies affect our views on life and shatter our
basic assumptions about life (Balk, 2004).
Chiricos, Padgett, and Gertz (2006), as well as
Chiricos, Eschholz, and Gertz (2000) found a
relationship between watching TV news cover-
age of traumatic events and an increase in fear.
We were interested in studying the subjective
reactions of students at a large state university
in the northeast following the shooting at Vir-
ginia Tech. We hypothesized that there would
be a significant relationship between vicarious
exposure through the news media to the Vir-
ginia Tech case and acute stress symptoms.
Method
Student Participants

We recruited 145 female and 167 male par-
ticipants from undergraduate and graduate psy-
chology courses. Students who were enrolled in
the introductory psychology and life span de-
velopment courses fulfilled course research re-
quirements by participating in psychology stud-
ies. Students signed up for any of a number of
different studies, and all received extra credit
for participating.
Demographics
The participants primarily consisted of fresh-
man, sophomores, and juniors (96.4%), with a
mean age of 19.56 years (SD � 3.72), and a
median grade point average of 3.00, who iden-
tified themselves as single (96.7%). This was a
primarily Caucasian sample (82.1%), with
9.2% self-identifying as African American/
Black, 5.1% as Hispanic or Latino, 1.5% as
Asian, and 0.9% as Other.
Survey
Participants were asked to estimate the num-
ber of hours spent viewing news coverage of the
shootings at Virginia Tech that included both
TV and internet viewing of this case. They
participated approximately 3 weeks after the
incident occurred and provided an estimate of
hours spent viewing news coverage since the
event. In addition, they were asked to rate their
own symptoms of depression, anxiety, and
stress-related symptoms, (scale of 1 [not at all]
to 5 [very much so]). Symptoms were converted

to a categorical scale by rating 1 (no symptoms),
2–3 (moderate symptoms), and 4 –5 (acute
symptoms). This conversion was done for two
reasons. First, by combining outcome catego-
ries, we were able to obtain more efficient esti-
mates by combining indistinguishable catego-
ries. Second, these conversions aligned with the
severity of the given symptom.
The survey measures included self-ratings of
the following symptoms of acute stress disorder
as taken from the symptom list presented in the
Diagnostic and Statistical Manual IV-TR
(American Psychiatric Association, 2000):
(1) Intrusive Thoughts: Experiencing
thoughts associated with the Virginia
Tech case.
(2) Sleep Disturbance: Experiencing sleep
disturbance, for example, trouble falling
asleep, trouble staying awake at night,
sleeping longer than usual.
(3) Appetite Disturbance: Experiencing ei-
ther an increase or decrease in appetite.
(4) Nightmares: Experiencing nightmares
about the Virginia Tech case.
(5) Fear: Increasing feelings of fear that
something like the Virginia Tech case
could either happen again somewhere
else or at this university.
(6) Stomach Upset: Experiencing gastroin-

testinal distress, for example, upset
stomach, butterflies in your stomach,
and so forth
(7) Depressive Symptoms: Experiencing a
sad or down mood.
(8) Symptoms of Suicide: Experiencing an
increase in suicidal ideation as a direct
result of the Virginia Tech case.
(9) Disorganization: Feeling disorganized,
confused, and “in a daze.”
(10) Alcohol and Drugs: An increase in al-
cohol or drug use.
(11) Replaying the Event: Reliving the
trauma of the Virginia Tech Case Invol-
untarily.
222 FALLAHI AND LESIK
(12) Anger: Experiencing symptoms of an-
ger as a direct result of the Virgina Tech
case.
(13) Guilt: Experiencing symptoms of guilt
as a direct result of the Virginia Tech
case.
Predictor Variables
The predictor FEMALE represents the respon-

dents’ self-identified gender (FEMALE � 1 for
female respondents, and FEMALE � 0 for males).
The predictor AGE Is the respondent’s age in
years.
A respondent’s self-identified race/ethnicity
was coded as the dummy predictor MINORITY
(MINORITY � 1 if the respondent self-
identified as nonwhite; and MINORITY � 0
when the respondent self-identified as white).
The predictor HOURS represents the number
of hours of TV coverage of the VT shootings.
Response Variables
There were 14 response variables that repre-
sented different symptoms. The self-reported
severity for each of the symptoms was initially
represented on a 5-point Likert scale and were
combined as a categorical variable on a scale of
no symptoms, moderate symptomatology, and
acute symptomatology. The 14 symptoms stud-
ied represented symptoms of acute stress disor-
der and posttraumatic stress disorder (PTSD)
based on the DSM–IV–TR manual (American
Psychiatric Association, 2000). Table 1 gives
the distribution of the three combined catego-
ries based on each of the 14 symptoms.
Statistical Analysis
We ran a series of regression analyses us-
ing the multinomial logit (MNL) model. We
chose the MNL because it is the preferred

method for regression models in which the
response variable has more than two catego-
ries. Even though the initial Likert scale was
ordinal, we chose the MNL because it does
not rely on the parallel regression assumption
(Long & Freese, 2003). The categories were
collapsed to represent experiencing none,
moderate, or acute symptoms. The initial ba-
sic MNL model equation used for each symp-
tom is presented in Equation 1.
mlogit(Symptom) � �0 � �1FEMALE
� �2AGE � �3MINORITY � �4HOURS � ε.
(1)
More formally, the MNL fodel can be written
as a series of binary logits as in Equation 2.
ln(Symptom�m)/b�X � ln
P(Symptom � m�X
P(Symptom � b�X
� ��m�/bX � �0
�m�/b � �1
�m�/bFEMALE
� �2
�m�/bAGE � �3
�m�/bMINORITY
� �4

�m�/bHOURS. (2)
Parameter b represents the base category, and
m ranges from 1 to J, where J represents the
number of categorical outcomes, and X de-
scribes the vector of control variables. For this
study, J � 3 because there are three distinct
outcome categories representing the three cate-
gories of symptomatology.
Since there can be more than one solution for
the estimated coefficients in a MNL analysis,
one of the coefficients needs to be set to 0 in
order to identify the model. This is category is
referred to as the base category, and it does not
Table 1
Distribution of Student Response to the
Different Symptoms
Symptom None Moderate Acute Missing
Intrusive thoughts 204 95 12 28
Sleeping 263 40 10 26
Appetite 277 33 3 26
Distraction 241 59 13 26
Nightmares 270 38 5 26
Fear 200 88 25 26
Butterflies in stomach 268 35 10 26
Depression 261 38 14 26
Suicide 305 5 3 26
Disorganization 261 43 8 27
Alcohol/drugs 278 28 5 28
Replaying the event 262 42 7 28
Anger 245 55 11 28
Guilt 274 30 7 28

TECH
matter which category is chosen because the
predicted probabilities will be the same regard-
less of which parameterization (or base cate-
gory) is used (Long, 1997). For our study, we
set the base category to None, thus the MNL
model would fit model Equations 3 and 4 for
each of the 14 symptoms.
ln(Symptom � ModerateNone)
� �iX
�Moderate)/None (3)
ln(Symptom � AcuteNone) � �iX
�Acute)/None (4)
Where �iX
��/None is the ith coefficient for
category k using the category None as the base
category. The remaining coefficients are then
estimated with respect to this base category.
Tests for Dependent Categories
We used a Wald test to determine whether the
outcome categories could be combined for
the 14 regression models (Long & Freese,
2003). The advantage to combining outcome
categories is to combine categories which are

indistinguishable in order to obtain more effi-
cient estimates (Long & Freese, 2003). The null
hypothesis for such a test is that two categories
can be combined (Long, 1997), and thus there is
no difference in the separate odds ratios for the
combined categories. For all of the 14 regres-
sion models, we found that categories 2 and 3
could be collapsed into a single category ( p �
.10). We are calling this combined category
Moderate since this category represents a mod-
erate reaction to the given symptom. We also
found that categories 4 and 5 could be collapsed
( p � .30), and called this category Acute since
it represents a severe reaction to the given
symptom. By keeping the 5-point scale, the
pairwise comparisons between different catego-
ries would be numerous and difficult to under-
stand, while not giving us any new information.
Further, the Wald test showed that there was not
a significant difference between these two cat-
egories and combining them made conceptual
sense.
Testing the Effects of Independent
Variables
Since there are J � 3 outcome categories,
then there are J – 1 � 2 coefficients associated
with each independent variable. By using cate-
gory None as the base category, there are two
coefficients that would be associated with each
predictor namely �i
�Moderate)/None, and �i
�Acute)/None.

The null hypothesis that predictor i does not
affect the outcome can be written as:
H0:�i
(Moderate)/None � �i
(Acute)/None � 0
Table 2 shows the individual parameter esti-
mates and standard errors for all 14 regression
analyses. Table 3 gives the results using a like-
lihood ratio test that was used to determine
which groups of predictors are significant for
each of the 14 regression analyses (Long, 1997;
Long & Freese, 2003).
Similar to Mickey and Greenland’s (1989)
criteria for including variables in a logistic re-
gression analysis, we used p values that are less
than 0.10 as the initial criteria for testing the
effects of the independent variables. Using
more traditional significance levels such as p �
.05 may fail to identify variables that are sig-
nificant for some of the categories but not for
others.
Perfect Predictions
The MNL model cannot be used if there are
perfect predictors. In other words, if there is
no variability in a predictor variable within
one of the outcome categories, this produces
excessively large standard errors and unstable
parameter estimates. Perfect predictors can
occur if there are only a few observations in a
given outcome category and if one or more of

the predictor variables does not vary within
this outcome category. This scenario can be
seen in the Acute category for the predictor
variable MINORITY for the symptoms of Ap-
petite, Nightmares, Alcohol and Drugs, Re-
playing the Event, and Guilt, and for the pre-
dictor variable FEMALE for the category of
Suicide as is presented in Table 2. By remov-
ing these predictor variables and running the
regression analysis along with performing the
appropriate likelihood ratio test, the effect of
these variables on the other outcome catego-
ries gave results similar to those presented in
Table 3.
The IIA Assumption
The MNL relies on the assumption known as
the independence of irrelevant alternatives
(IIA). Essentially, the IIA assumption stipulates
that adding or deleting outcome categories will
not affect the odds ratio of the remaining out-
comes (Green, 2003; Maddala, 1983). We ran
the Hausman test (Hausmann & McFadden,
1984) for all 14 regression analyses with the
three combined categories and there was no
evidence to suggest that the IIA assumption has
been violated ( p � .80).
Interpretation of the Results

Although the MNL can be considered as a
simple extension of the logistic regression
Table 2
Estimated MNL Coefficients and Standard Errors [in Brackets]
for the MNL Model Comparing All
Possible Combinations for Each of the Three Response
Categories (N � 339)
Symptom Categories FEMALE AGE MINORITY HOURS CONS
Intrusive
thoughts None Moderate 0.171 [0.268] �0.085 [0.059] 0.420
[0.374] 0.024 [0.025] 0.691 [1.171]
None Acute �0.386 [0.670] �0.080 [0.162] �0.405 [1.054]
0.108�� [0.035] �1.630 [3.214]
Moderate Acute �0.558 [0.685] 0.005 [0.167] �0.825 [1.064]
0.084� [0.035] �2.322 [3.319]
Sleeping None Moderate 0.225 [0.357] �0.156 [0.104] �0.037
[0.540] 0.035 [0.029] 0.988 [2.032]
None Acute 0.117 [0.766] �0.035 [0.154] 0.642 [0.919]
0.097�� [0.035] �3.500 [3.110]
Moderate Acute �0.109 [0.818] 0.121 [0.182] 0.679 [1.021]
0.062 [0.039] �4.487 [3.635]
Appetite None Moderate �0.255 [0.400] �0.037 [0.078] �0.041
[0.581] 0.031 [0.032] �1.400 [1.556]
None Acute �0.078 [2.363] �0.178 [0.707] 21.815 [13.870]
0.143� [0.067] �23.533 [N/A]
Moderate Acute 0.176 [2.383] �0.141 [0.710] 21.856 [13.939]
0.111 [0.070] �22.133 [N/A]
Distraction None Moderate 0.271 [0.317] �0.090 [0.075] 0.090

[0.464] 0.007 [0.030] 0.170 [1.482]
None Acute �0.046 [0.604] 0.004 [0.084] 0.532 [0.738]
0.084�� [0.031] �3.454� [1.744]
Moderate Acute �0.318 [0.652] 0.094 [0.110] 0.442 [0.821]
0.077� [0.038] �3.624 [2.224]
Nightmares None Moderate 0.656† [0.375] �0.242� [0.124]
�0.161 [0.577] 0.042 [0.026] 2.322 [2.385]
None Acute 0.651 [0.933] �0.469 [0.434] �35.479 [N/A] 0.073
[0.071] 4.691 [8.205]
Moderate Acute �0.005 [0.983] �0.227 [0.447] �34.319 [N/A]
0.031 [0.074] 2.369 [8.455]
Fear None Moderate 1.200�� [0.291] �0.060 [0.049] �0.655
[0.477] 0.051† [0.028] �0.377 [0.995]
None Acute 1.838�� [0.582] �0.145 [0.138] �0.037 [0.717]
0.114�� [0.034] �0.988 [2.719]
Moderate Acute 0.639 [0.604] �0.085 [0.140] 0.618 [0.768]
0.063† [0.032] �0.610 [2.776]
Butterflies in
stomach None Moderate 0.286 [0.391] �0.085 [0.095] 0.181
[0.550] 0.045 [0.031] �0.735 [1.884]
None Acute �0.152 [0.739] 0.020 [0.094] 0.403 [0.915]
0.111�� [0.035] �4.413� [1.975]
Moderate Acute �0.438 [0.807] 0.105 [0.131] 0.221 [1.020]
0.066† [0.039] �3.677 [2.678]
Depression None Moderate 0.070 [0.366] �0.169 [0.110]
�0.604 [0.652] 0.021 [0.032] 1.399 [2.143]
None Acute �0.418 [0.604] 0.018 [0.075] 0.107 [0.785] 0.073�
[0.031] �3.474� [1.560]
Moderate Acute �0.487 [0.680] 0.187 [0.131] 0.710 [0.979]
0.052 [0.403] �4.873† [2.606]

Suicide None Moderate �0.913 [1.189] �0.411 [0.447] 2.104�
[1.050] �0.022 [0.097] 3.546 [8.445]
None Acute �43.266 [N/A] 0.035 [0.385] 2.300† [1.380] 0.108†
[0.058] �6.139 [7.894]
Moderate Acute �36.354 [N/A] 0.447 [0.584] 0.196 [1.709]
0.129 [0.109] �9.685 [11.479]
Disorganization None Moderate 0.051 [0.360] �0.084 [0.089]
�0.341 [0.575] 0.061� [0.025] �0.428 [1.753]
None Acute �0.973 [0.886] 0.062 [0.071] 0.760 [0.899] 0.051
[0.044] �4.780�� [1.564]
Moderate Acute �1.024 [0.934] 0.146 [0.112] 1.101 [1.028]
�0.011 [0.045] �4.367† [2.317]
Alcohol/drugs None Moderate �0.560 [0.433] �0.038 [0.084]
�0.399 [0.666] 0.026 [0.030] �1.303 [1.689]
None Acute 1.179 [1.166] �0.255 [0.365] �31.707 [N/A]
�0.061 [0.171] 0.468 [6.998]
Moderate Acute 1.739 [1.229] �0.217 [0.373] �31.308 [N/A]
�0.087 [0.173] 1.770 [7.167]
Replaying None Moderate 0.119 [0.363] �0.063 [0.078]
�1.852� [0.900] 0.074�� [0.027] �0.798 [1.569]
None Acute 0.859 [0.883] �0.203 [0.276] �32.704 [N/A] 0.070
[0.074] �0.270 [5.364]
Moderate Acute 0.741 [0.930] �0.139 [0.284] �35.858 [N/A]
�0.004 [0.076] 0.527 [5.525]
Anger None Moderate �0.208 [0.332] 0.006 [0.047] 0.173
[0.456] 0.090�� [0.028] �1.956 [0.962]
None Acute 0.281 [0.713] 0.019 [0.086] �0.137 [1.027]
0.130�� [0.040] �4.411� [1.825]
Moderate Acute 0.489 [0.747] 0.013 [0.093] �0.310 [1.059]
0.040 [0.036] �2.456 [1.977]
Guilt None Moderate �0.234 [0.437] �0.130 [0.123] 0.394

[0.568] 0.027 [0.029] 0.174 [2.405]
None Acute 0.233 [0.856] �0.086 [0.219] �35.771 [N/A]
0.117� [0.053] �2.570 [4.378]
Moderate Acute 0.467 [0.942] 0.045 [0.248] �40.166 [N/A]
0.090 [0.058] �2.745 [4.932]
† p � .10. � p � .05. �� p � .01.
TECH
model, the interpretations are much more diffi-
cult to describe because of the large number of
comparisons that are involved. Furthermore, by
combining some of the outcome categories, this
provides analyses that are more efficient (Long
& Freese, 2003). Predicted probabilities can be
used as a way to quickly and succinctly sum-
marize the probability of a given outcome cat-
egory given a specific set of values of the pre-
dictor variables (Long & Freese, 2003). For the
MNL, this corresponds to estimating the prob-
ability that is described in Equation 5.
P̂ �Symptom � m�X� �
exp�X�̂�m�/J�
�
i � 1
J
exp�X�̂�i�/J�

(5)
Where m is the desired outcome category, J is
the number of categories and X is the vector of
covariates.
Figure 1 presents the graphs of the predicted
probabilities for all of the regression models
that tested out as significant ( p � .10). For
example, as TV viewing increases, so too does
the probability that a participant will self-rate as
experiencing fear. Specifically, after viewing 10
hours of TV coverage of Virginia Tech, partic-
ipants had a 9.4% chance of experiencing acute
symptoms of fear. After 25 hours of coverage,
this percentage jumped to 30.7%. As expected,
after viewing only 5 hours of news coverage,
participants had a 66.2% chance of reporting no
symptoms. But as their viewing hours increased
to 25 hours of news coverage, the percentage
decreased to a 34.1% chance of reporting no
symptoms.
Odds Ratios
In order to describe the relationships among
the outcome categories, odds ratios (or factor
change coefficients) can be used (Long, 1997;
Long & Freese, 2003). To describe the factor
change in the odds of outcome m versus out-
come n as the predictor variable HOURS in-
creases by is described by equation (6).
m/n�x,HOURS � �

m/n�x,HOURS�
� e�HOURS, m/n
(6)
Table 4 provides the odds comparing each of
the outcome categories. To interpret the odds
ratio, for a standard deviation increase in the
amount of hours watched of the VT shootings,
the odds of experiencing an acute intrusive
thought are 1.8076 times greater as compared to
experiencing nothing (holding all other factors
fixed). The range of the odds for experiencing
an acute symptom as compared to experiencing
no symptoms what-so-ever ranged from 1.4769
for Guilt to 3.1983 for symptoms of Appetite.
Discussion
We assessed college students’ responses to
the shooting at Virginia Tech in the first few
weeks after the event occurred. The value of
using MNL allowed us to develop a predictive
model that describes more than simple correla-
tional relationships. This technique is a pre-
ferred method for looking at categorical depen-
dent variables, and allows us to make compar-
isons between categories resulting with an odds
ratio that allows us to describe the magnitude of
the differences in self-reported measures of
stress between the categories based on the num-
ber of hours watched. In addition, this modeling
strategy allows the researcher to include any

covariates that might be deemed important.
In this study, we were able to show that as
TV viewing of the Virginia Tech case in-
Table 3
Chi-Squared Statistics and p-Values for Testing the
Effects of Each of the Independent Variables for
Each of the Fourteen Symptoms (df � 2)
Symptom
Variable
FEMALE AGE MINORITY HOURS
Intrusive thoughts 0.878 3.022 1.565 9.024�
Sleeping 0.408 3.240 0.465 7.831�
Appetite 0.410 0.339 1.860 7.974�
Distraction 0.763 2.048 0.495 7.190�
Nightmares 3.482 7.474� 1.826 3.052
Fear 25.285�� 3.163 2.145 13.183��
Stomach 0.608 1.171 0.264 10.809��
Depression 0.553 3.500 1.022 5.019†
Suicide 5.459† 1.264 6.066� 4.709†
Disorganization 1.400 1.838 1.098 6.103�
Alcohol/drugs 3.043 1.031 1.304 0.815

Replaying the event 1.073 1.652 8.557� 7.203�
Anger 0.627 0.056 0.178 17.396��
Guilt 0.378 1.728 4.247 4.373
Note. † p � .10. � p � .05. �� p � .01.
creased, so too does the probability that a par-
ticipant will self-rate as experiencing acute
symptoms of intrusive thoughts, sleep distur-
bance, distraction, fear, stomach upset, depres-
sion, disorganization, replaying of the event,
and symptoms of anger. The probability of ex-
periencing acute symptoms for intrusive
thoughts, sleep and appetite disturbance, dis-
0
.2
.4
.6
.8
P
re
d
ic

te
d
P
ro
b
a
b
ili
ty
0 10 20 30 40 50
Hours
None Moderate
Acute
Fear
Fear
None Moderate Acute Hours
0.6621 0.2792 0.0587 5
0.5932 0.3129 0.0939 10
0.5148 0.3398 0.1453 15
0.4293 0.3545 0.2162 20
0.3411 0.3525 0.3065 25
0.2567 0.3319 0.4114 30
0.1826 0.2954 0.5221 35
0.1231 0.2491 0.6278 40
0.0791 0.2005 0.7204 45
0.0490 0.1553 0.7957 50
0

.2
.4
.6
.8
P
re
d
ic
te
d
P
ro
b
a
b
ili
ty
0 10 20 30 40 50
Hours
None Moderate
Acute
Stomach
Stomach

0.8637 0.112 0.0243 5
0.8261 0.1325 0.0414 10
0.7767 0.1540 0.0693 15
0.7122 0.1746 0.1131 20
0.6305 0.1911 0.1783 25
0.5324 0.1995 0.2681 30
0.4237 0.1963 0.3800 35
0.3154 0.1807 0.5038 40
0.2197 0.1556 0.6247 45
0.1441 0.1262 0.7297 50
0
.2
.4
.6
.8
P
re
d
ic
te
d
P
ro
b
a
b

ili
ty
0 10 20 30 40 50
Hours
None Moderate
Acute
Anger
Anger
0.7891 0.1832 0.0277 5
0.6877 0.2650 0.0473 10
0.5635 0.3605 0.0760 15
0.4298 0.4564 0.1137 20
0.3048 0.5372 0.1581 25
0.2023 0.5919 0.2058 30
0.1274 0.6185 0.2541 35
0.0771 0.6213 0.3016 40
0.0453 0.6067 0.3479 45
0.0261 0.5805 0.3933 50
Figure 1. Predicted probabilities for all significant regression
models based on number of
hours of viewing the VT shooting at the p � .01 level.
TECH
traction, fear, stomach disturbance, and anger
were less than 9% for TV viewing of 10 hours

and from 30% to 62% for 40 hours of exposure
to the Virginia Tech case. For suicide, disorga-
nization, and replaying, the probability of expe-
riencing acute symptoms was less than 3%
for 10 hours of TV exposure and from 3.55
to 10.73% for 40 hours of exposure. Further, we
were able to show that for each hour watched of
the Virginia Tech shootings, the odds of expe-
riencing acute symptoms increased from 1.48
to 3.20 times, depending on the symptom. This
study improves over past research in allowing
us to predict the probability of experiencing
acute symptomatology as the result of exposure
to real life violence in the media by going
beyond showing a relationship and actually
quantifying the magnitude of that relationship.
Table 4
Factor Change in the Odds of Symptoms Based on Number of
Hours Watched
Symptom Outcome comparison Parameter estimate (b) Factor
change in the odds
Intrusive thoughts Acute-None 0.10440 1.8076��
Acute-Moderate 0.07536 1.5331�
Moderate-None 0.02904 1.1790
Sleeping Acute-None 0.10172 1.8017��
Acute-Moderate 0.06801 1.4824†
Appetite Acute-None 0.20086 3.1983��

Distraction Acute-None 0.09621 1.7452��
Nightmares Acute-None 0.03491 1.2261
Acute-Moderate �0.00487 0.9720
Moderate-None 0.03978 1.2615†
Fear Acute-None 0.11578 1.9692��
Butterflies in stomach Acute-None 0.11541 1.9503��
Depression Acute-None 0.07515 1.5449��
Acute-Moderate 0.06220 1.4334†
Suicide Acute-None 0.10801 1.8886†
Acute-Moderate 0.12714 2.1136
Moderate-None �0.01913 0.8935
Disorganization Acute-None 0.05780 1.3979

Moderate-None 0.05235 1.3544�
Alcohol/drugs Acute-None �0.06305 0.6936
Replaying the event Acute-None 0.02901 1.1833
Moderate-None 0.04586 1.3049�
Anger Acute-None 0.13470 2.1848��
Guilt Acute-None 0.06720 1.4769†
† p � .10. � p � .05. �� p � .01.
As psychologists working in prevention, it is
helpful to know how many hours of TV viewing
are associated with the development of symp-
tomatology. Further, it is now apparent that
clinicians working with clients following a trau-
matic event need to incorporate questions about
vicarious exposure to the event in their assess-
ments. Psychologists and Educators need to be
better informed about how to help students cope
with viewing high-profile media events. Finally,

this research gives more evidence that Psychol-
ogists should work harder to advise newscasters
on the potential negative responses to violent
media.
Research exploring gender differences in fear
reactions to mass media has consistently shown
that females respond with greater frequency and
magnitude to audiovisual images in studies pro-
duced from 1987 to 1996 and more recently
(Peck, 1999; Valkenburg, Cantor, & Peeters,
2007). Similar results were found in the exam-
ination of females responses to the Virginia
Tech case as females experienced significantly
more symptoms of fear via their self-ratings as
compared to males. No gender differences were
seen in the other 13 acute stress symptomatol-
ogy. This makes more of a case for the detri-
mental effects of vicarious exposure to TV. The
effects seem to be due to TV watching as op-
posed to differences between the male and fe-
male participants in our study.
Additionally, past research has shown that
age and race are factors that make one vulner-
able to the effects of violent media exposure
(Tucker et al., 2000; Pulcino et al., 2003). In
this sample, we did not have a large distribution
of participants from varying age or minority
status. Our sample was too biased in that par-
ticipants were primarily in late adolescence or
emerging adulthood and were primarily Cauca-
sian. Future research will need to document the
probabilities of experiencing increased preva-
lence and magnitude of symptomatology based
on vicarious exposure to violent media for other

samples.
The generalizability of this study is limited
because of the lack of standard measures and
the reliance on self-report of the participants. In
addition, exposure, as measured by the number
of hours watching news reports about this inci-
dent, was based on self-report without objective
corroboration. Finally, without a pretest mea-
sure of symptoms of PTSD, we are limited in
drawing a causal inference that the media ex-
posure was the cause of the acute stress symp-
toms in our sample. Other factors such as stress
surrounding upcoming examinations or other
stressful events in the life of a student may in
fact have influenced their self-reported mea-
sures of stress.
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Received September 10, 2008
Revision received December 29, 2008
Accepted December 29, 2008 �

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