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AJSW, Volume 4, Number 1, 2014                                                      Nkhoma, P. 

DRINKING AND DEPRESSION AS PREDICTORS 
OF SOCIAL SUPPORT AND QUALITY OF LIFE 
AMONGST CIVILIANS AND EX-COMBATANTS 

IN JUBA, SOUTH SUDAN 
Nkhoma, Potiphar. 

ABSTRACT 

This paper examined drinking and depression as predictors of social 
support and quality of life among civilians and ex-combatants in 
South Sudan. High levels of drinking and depression and rising rates 
of suicide have been reported as growing matters of public health 
concern. Some ex-combatants will suffer severe psychological 
conditions, including Post Traumatic Stress Disorder (PTSD) after 
the war. Mental health conditions that co-exist with alcohol abuse 
have a more debilitating effect.  Designing effective intervention 
programs to prevent complications and or to treat those at risk is 
critical. Several scales were used to measure psychological well-
being. Regression, independent samples t-test techniques and 
standard equation modelling were used to evaluate the hypotheses. 
Gender and affiliation were found to be significant predictors of 
social support while education and drinking were significant 
predictors of quality of life. However, depression was not a 
significant predictor of either. The research was conducted from 
April to September 2011. Data was collected from civilians and 
verified ex-combatants and Women Associated Armed Forces in 
Western and Northern Bahr El Ghazal. Four trained caseworkers of 
the South Sudan DDR Commission assisted with data collection. 
 
KEY TERMS:  Drinking, depression, ex-combatants, and Sudan. 
 
 
 
 
 
 
The writer of the paper is a Social Work Clinician with experience 
working in South Africa, Guinea, Liberia, Sierra Leone and USA. 
Email: pnkhoma@ci.davenport.ia.us 

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INTRODUCTION 

The signing of the Comprehensive Peace Agreement (CAP) in 

January of 2005 between the Sudan’s People’s Liberation Movement 

(SPLM) and the Government of Sudan ended Sudan’s second civil 

war which had lasted from 1983 to 2005. This was preceded by the 

first civil war that had lasted from 1955 to 1972. This agreement 

marked the end of one of Africa’s protracted and bitter civil wars, 

and raised hopes for long term peace after nearly forty years of war. 

The prolonged exposure to war renders many in the population 

susceptible to possible cumulative emotional trauma and related 

mental health issues. The reported high rates of trauma and 

depression may give just a glimpse into the depth and breadth of 

challenges likely to face service providers. Exposure to trauma often 

affects every aspect of everyday living and functioning, including 

but not limited to, how one deals with and or manages change, 

learns, thinks, works and or relates to others. Trauma survivors are 

generally more prone to experience mental health and physical 

health problems.  

Intervention strategies focused only on ex-combatants and women 

associated with armed forces (WAAF) alone, will neglect the 

majority of civilians exposed to trauma untreated, and vulnerable to 

developing complications. The adjustment and coping of ex-

combatants may be further enhanced by the re-integration socio-

economic support packages and skills training they receive. It is well 

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established that individuals with good emotional social support 

systems cope better than those without. Ironically, to some degree, 

war also gave combatants more ‘control’ and ‘self-efficacy’ than 

civilians over stressful and stress inducing situations.  

By understanding the effects of trauma, its sources and impacts, 

policy and decision makers, as well as other stakeholders can help 

design and develop appropriate and responsive programs. Along 

with other developmental priorities, such as reconstruction and 

development, restoration of safety and security, provision of 

competent basic mental health support must be a key priority for 

long term peace and stability in South Sudan.  

BACKGROUND 

Few studies in South Sudan found depression, drinking and suicide 

to be growing problems, and possible key issues of public health 

concern for South Sudan (Nkhoma, 2011; Winkler, 2010; Roberts et 

al., 2009). The possible co-occurrence of drinking, post-traumatic 

stress disorder and depression amongst ex-combatants has a 

potential to amplify the harms associated with each one separately. 

Roberts reported the existence of disconcertingly high levels of 

PTSD amongst Southern Sudanese, where 36% of the sampled 

population (n= 1242) met the criteria for PTSD, existing along with 

high levels of depressive symptoms at 50% for the sample (Roberts 

et al., 2009). In their study conducted in northern Uganda and South 

Sudan, Karunakara et al., (2004) found prevalence of PTSD amongst 

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South Sudanese in South Sudan was 48% compared to 46% among 

South Sudanese refugees living in Uganda. When considered 

together with reports of high levels of drinking, aggression, domestic 

violence and high rates suicide, these factors will place enormous 

additional stressors on the mental health of those affected and the 

public health system. Mental health is recognized as a key public 

health issue in conflict affected populations (IASC, 2007, Roberts et 

al., 2009), we believe it must considered as such too in post-conflict 

societies, especially in the interests of building peace and stability.  

There are currently no studies conducted in South Sudan to compare 

the mental health effects of war between the civilian, and the ex-

combatant populations. In recent focus group discussion with ex-

combatants and WAAF, my findings indicated that they experience 

serious mental health concerns including high levels of depression, 

post-traumatic stress, increased alcohol use, domestic violence and 

suicidal ideation.  

Depression is one of the most common affective disorders with a life 

time prevalence of between 10 to 16%, and an estimated life time 

occurrence of between 8 to 18% in the general population (Alaadin 

& Ansul, 2008). Prevalence rates for depression and post-traumatic 

stress disorder in South Sudan are significantly high. Post-traumatic 

stress disorder is likely to be co-morbid with other mental health 

disorders, including with depression. Further, substance abuse is a 

well-documented co-morbid factor in many psychological disorders, 

including for both depression and post-traumatic stress disorder. 

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Considering the potential risks posed by the possible co-occurrence 

of drinking and depression in conditions of prolonged exposure to 

war such as is the case in South Sudan, can lead to elevated levels of 

depression, drinking and PTSD, this study investigated their ability 

to predict perceived social support and quality of life amongst ex-

combatants and civilians. 

There are currently no studies related to drinking and depression as 

predictors of social support and quality of life comparing ex-

combatants and civilians. This study uses Structural Equation 

Modeling (SEM), to address this gap. The objective is realized 

through literature review, evaluation of the research hypotheses 

using SEM. Key findings are reported, limitations and implications 

discussed, and suggestions for future research are offered. 

LITERATURE REVIEW 

Theoretical framework 

Theories of social behaviour, in particular social learning theory 

(Bandura, 1977) and normative theory (Paton-Simpson, 2001), 

contend that perceived or real social norms exert a strong influence 

on social behaviour, where social norms refer to the expected 

behaviours in specific situations (Hagman, Clifford, Noel, 2007). 

According to social learning theory, drinking including heavy 

drinking can be influenced by observing peers, imitation and or 

modeling (Bandura, 1977). In this context therefore drinking both 

within ex-combatants and civilians is likely to be influenced by 

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dominant local cultures, as most will imitate, exhibit and maintain 

socially desirable and acceptable drinking behaviours. Larimer et al., 

(2009) found that the influence of perceived approved behaviours of 

peers was one of the strongest influences on personal drinking 

amongst college students. I believe that this will be the case with ex-

combatants and civilians, in keeping with social learning theory. In 

view of the above discussion, I hypothesized as follows: 

H1: Drinking will be negatively correlated to Social 

Support and Quality of Life. 

Drinking and Depression 

Few studies investigated the effects of alcohol use on the 

relationship between stress and depression, and found that light to 

moderate drinkers had less depression when compared to non-

drinkers and or heavy drinkers (Lipton, 1994). Depression has been 

correlated with poor health, overall task performance (Ameresekere 

et al., 2012) and elevated substance abuse and anxiety (Andrew and 

Wilding, 2004). Arthur (2004) found that depressed individuals 

lacked many necessary interpersonal skills, and made unrealistic 

demands on themselves and others. Research shows that mental 

health conditions that co-exist with substance use and or abuse have 

a far more debilitating psychological effect than those that do not 

(Ayazi et al., 2012). I posited therefore that, many ex-combatants 

will have difficulties with relationships during the re-integration 

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period leading to poor social support and depression. In view of the 

foregoing discussion the following was hypothesized. 

H2: Depression will be negatively correlated to Social 

Support and Quality of Life. 

Social Support and Suicide 

Social support research emphasizes the importance of external 

factors and availability of social support for coping with challenging 

life events. The process of re-integration is going to place enormous 

adjustment demands (social, physical, emotional, financial and 

otherwise) on the ex-combatants, their families and the communities 

which they are to become part of, which additional stressors do not 

necessarily arise for civilians. Without the right levels of social 

support, ex-combatants may experience painful social isolation 

which may impact negatively on their health and psychological 

wellbeing. Social isolation and loneliness have been related to 

chronic illness and poor health status, with links to increased alcohol 

use, depression and suicidal ideation (Swami et al., 2001) On the 

other hand, social support has been shown to act as a psychological 

buffer against stress and has been associated with lower levels of 

stress, (Negga and Applewhite, 2007) and is positively correlated 

with high levels of social coping; (Zimet, 1998). Other studies show 

that deterioration in the quality of the relationship, regardless of 

whether one drinks or does not, tends to lead to depression and also 

that there is a positive correlation between depression and suicide 

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(Kashbeck and Christensen, 1995). In view of above discussion I 

hypothesized as follows:  

H3: Suicidal Ideation will be negatively correlated to 

Social Support and Quality of Life. 

Domestic Violence and PTSD 

Domestic violence is defined as ‘any means of establishing power 

and control over the victims by both physical and psychological 

methods of coercion’ (Pence and Paymar, 1993; Shephard, 1992). 

Radford and Russell (1992) observe that domestic violence is used 

to protest a setback in power relations regarding women in society, 

and often allows men to get away with such violent behaviour 

towards women. While Londt (2004) notes that domestic violence is 

progressive in its debilitation and often lethal. Current research 

findings indicate that domestic violence is a problem in South 

Sudan. It is complicated by social, cultural traditions and 

institutional practices that seem to condone it. Individuals who suffer 

from post-traumatic stress disorder may have difficulties controlling 

their impulse and or coping with elevated levels of stress, which may 

render them vulnerable to committing increased acts of domestic 

violence as they attempt to regain control in a situation. In view of 

the above discussion I hypothesized the following: 

H4: Post Traumatic Stress Disorder will be negatively 

correlated to Social Support and Quality of Life. 

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METHODOLOGY 

Procedures 

The data was collected from verified ex-combatants and civilians in 

Northern Bahr El Ghazal (NBEG) and Western Bahr El Ghazal 

(WBEG). Several instruments were utilized to collect data: A 

Background Information Form (BIF), Clinically Administered Post 

Traumatic Stress Scale (CAPS), Social Support Scale (SS), Quality 

of Life Scale (QOL) and several scales to measure Depression, 

Alcohol consumption, Domestic Violence and Suicidal Ideation. The 

BIF was used to record information pertaining to area, affiliation, 

ethnicity, age, gender, rank, years of service, type of vocational 

training and employment status. 

 

Participants 

The sample consisted of 238 respondents, made up of 108 (45.4%) 

ex-combatants and 130 (54.6%) civilians aged 18-79 years. The 

sample was made up of 129 (54.2%) (77 NBEG & 52 WBEG) male 

and 109 (45.8%) (50 NBEG & 59 WBEG) females of whom 55 

males and 53 females were ex-combatants. With 127(53.4%) 

participants from NBEG and 111(46.6%) coming from WBEG. 

Most research participants (37.4%) were between the ages of 35-49 

years, with: 10.9% (18-24), 28.6 %( 25-34), 10.1% (50-59), and 13% 

(60-79). Ethnic composition of the sample was 63.4% Dinka, 16.4% 

Jur/Nueri, 12.6% Balanda, and 2.5% each for Mundari and Zande, 

with 1.7% Falata and 0.8% Magayai. Only 5.5% of participants were 

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employed full-time, 8.4% part-time with the remainder, 86.1% 

identifying as ‘not-working’. Sixty-five percent (65.1%) had no 

formal schooling with 24.4% attending primary school but not 

literate, while only 10.5% can be regarded as literate. 

 

Measures 

To measure quality of life, the Quality of Life (QOL) scale was 

administered to participants, the 13-item questionnaire like all other 

questionnaires is anchored from 1 (“Strongly Disagree”) to 5 

(“Strongly Disagree) and was developed by Lee, Bobko, Earley, 

Lokke et al., (1991). The scale was reverse coded so that lower 

values reflect higher scores and the Cronbach Alpha for this scale 

was .912. I developed the scales for Suicidal Ideation (3-items), 

Drinking (8-items) and Domestic Violence (6-items), all the scales  

had a high and excellent reliability ranging from .873 to a high of 

.978 which is better than .70 required for such research (Hair, 1998, 

Nunnally, 1978).  

RESULTS 

The proposed quality of life model presented in Figure 1 was tested 

using latent variable structural equation modelling (SEM) to 

evaluate research hypotheses by using the LISREL computer 

program (version 8.30,  Joreskog & Sorbom, 1996). A major 

strength of using structural equation modelling (SEM) is that it uses 

latent variables which allow for the estimation of relationships 

among theoretically interesting constructs that are free of the effects 

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of measurement unreliability. The covariance matrix was used as the 

input for all models, and the maximum likelihood estimation 

procedure was employed to produce the model parameters. To 

examine model fit, measures of absolute fit were employed, 

incremental fit, and parsimonious fit in order to determine how well 

the data fit the hypothesized model (Hair, Anderson, Tatham, & 

Black, 1998; Mueller, 1996). The means, standard deviations and 

zero-order correlations for the model were calculated. 

Common Method Variance Tests 

Since all constructs were measured using self-report measures, we 

examined whether common method variance was a serious issue. As 

recommended by Padsakoff and Organ (1986), Harman’s one-factor 

test analysis was conducted. In this test, all variables were entered 

together into an unrotated factor analysis and the results were 

examined. If substantial common method variance is present, then 

either a single factor would emerge or one general factor would 

account for most of the total variance explained in the items 

(Podsakoff & Organ, 1986). After entering all the items into the 

factor analysis model four factors emerged from the analysis, and the 

first factor accounted for 19 percent of total variance, however, no 

general factor emerged from the factor analysis. Thus, common 

method variance was not considered to be a serious issue in this 

study. 

 Model Fit Indicators  

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The following fit indices were used to assess the fit of the 

nomological network developed in Figure 1. The goodness-of-fit 

index (GFI) is a measure of absolute fit of the model by comparing 

the fitted model with the actual data, and ranges from 0 to 1. Values 

that are greater than 0.90 demonstrate that the model fits the data 

well (Hair et al, 1998). The absolute fit measures, maximum 

likelihood ratio chi-square (χ2) and goodness-of-fit index (GFI), 

provide a measure of the extent to which the covariance matrix 

estimated by the hypothesized model reproduces the observed 

covariance matrix. In addition the root mean square error of 

approximation (RMSEA) was considered as it provides an estimate 

of the measurement error. Another fit index, the non-normed Fit 

Index (NNFI), was used to assess model fit; the NNFI assess the 

penalty for adding additional parameters to the model. The normed 

fit index (NFI) provides information about how much better the 

model fits than a baseline model, rather than as a sole function of the 

difference between the reproduced and observed covariance matrices 

(Mueller, 1996; Bentler & Bonnett, 1980). In NFI and NNFI the 

nested models have a chi-square closer to zero, in which case it can 

be said that the model is parsimonious (Mueller, 1996; Marsh et al., 

1988). The comparative fit index (CFI) has similar attributes to the 

NFI and compares predicted covariance matrix to the observed 

covariance matrix and is least affected by sample size. 

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Figure 1: Conceptual Model for Quality of Life

 

AFFILIATION 

DOMESTIC 
VIOLENCE 

DEPRESSION 

GENDER 

DRINKING 
ALCOHOL 

QUALITY OF 
LIFE 

SOCIAL 
SUPPORT 

EDUCATION 

PTSD  

SUICIDAL 
IDEATION 

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Test of the Model  

The two step approach to Structural Equation Modeling was 

employed (Anderson & Gerbing, 1988) First, the measurement 

model was inspected for satisfactory fit indices. After establishing 

satisfactory model fit, the structural coefficients were interpreted.  

Model Measurement 

The measurement model had acceptable fit indices (see Table 1). 

That is, the Chi-square statistic was at its minimum, and the p-value 

was non-significant. The GFI was above its recommended threshold 

of 0.90 (Hair et al., 1998), and the root mean square error of 

approximation (RMSEA) was less than 0.08, indicative of an 

acceptable model (Steiger & Lind, 1980). The Chi-square divided by 

degrees of freedom co-efficient was less than three, which indicates 

an acceptable model fit (Marsh et al., 1988). The CFI, NFI and 

NNFI all indicated an acceptable fit of the model to the data.  

 

 

 

 

 

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Table 1: Fit Indices for the Quality of Life Measurement Model 

Model χ2 

(df)   

 p-

value 

χ2 

(df)   

RMSEA 

 

GFI NNF

I 

NF

I 

CF

I 

Base-

line 

0.00 

(1) 

1.00 0.00 0.0 1 1.10 1 1 

Statistics are based on a sample of 205 respondents. 
Degrees of freedom are in parentheses after the Chi-square value. 
RMSEA                   = Root mean square error of approximation. 
GFI                          = Goodness-of-fit index. 
NNFI                       = Non-Normed Fit Index. 
NFI                          = Normed Fit Index. 
CFI                          = Comparative Fit Index. 
df                             = Degrees of Freedom. 
 

Interpretation of Structural Equation Modelling 

Table 2 displays significant structural coefficients for the quality of 

life model. Drinking was found to be a statistically significant and 

negatively correlated predictor of quality of life, which seemed to 

indicate that the more one drank; the worse their quality of life was 

likely to be. Contrary to expectations, depression was not found be 

significant predictor of quality of life in this model. However, 

affiliation and gender emerged as statistically significant and 

negatively correlated to social support, which seems to suggest that 

woman and those who were not ex-combatants, were likely to 

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experience poor social support than men. Education emerged as a 

statistically significant and positive predictor of quality of life.  

Table 2: Unstandardized Structural Coefficients for the Quality of 
Life Model 

Parameter Path 

Coefficient 

T-value SMC 

SOCIAL SUPPORT   19% 

Affiliation -6.85 -2.77*  

Gender -6.87 -2.68*  

QUALITY OF LIFE   14% 

Alcohol use/ 

Drinking 

-0.44 -2.95*  

Education 0.28 2.75*  

Statistics are based on a sample of 238 respondents. 
These are the endogenous variables in the model; the exogenous 
variables are listed underneath.  
*Significant at the 0.05 level. 
SMC=Squared Multiple Correlation. 

 

 

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Overall, four factors affiliation, gender, drinking (alcohol use) and 

education were significant predictors of quality of life for the 

hypothesized quality of life model.  

Partial support was established for H1, which stated that ‘drinking 

will be negatively correlated to social support and quality of life’. 

Drinking was found to be statistically significant and negatively 

correlated with quality of life, but not to social support. Contrary to 

expectation, partial support was established for H2, which stated that 

‘depression will be negatively correlated to social support and 

quality of life’. As hypothesized, depression was found to be 

negatively correlated with social support, but unexpectedly 

positively correlated with quality of life, though both relationships 

were statistically insignificant.  

The third set of hypotheses, H3 predicted ‘suicidal ideation will be 

negatively correlated to social support and quality of life’. This was 

partially rejected when it was established that suicidal ideation was 

positively correlated with social support, but negatively correlated 

with quality of life, however both relationships were statistically 

insignificant as well. The last set of hypotheses, H4 stated that ‘post-

traumatic stress disorder will be negatively correlated with social 

support and quality of life’. This was partially affirmed, when PTSD 

was found to be negatively correlated to quality of life, but rejected 

when contrary to expectation it was shown to be positively 

correlated to social support, both associations were however, shown 

to be statistically insignificant.  

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None of the paths, in H1 a, H2 a,b, H3 a,b and H4 a,b were statistically 

significant, and only part of H1 b (drinking will be negatively 

correlated to quality of life) was statistically significant, but not H1 a 
(drinking will be negatively correlated to social support). The 

squared multiple correlations for Social Support and Quality of Life 

were 19% and 14% respectively. 

DISCUSSION 

The current research investigated the relationship between drinking 

and depression as predictors of social support and quality of life 

amongst ex-combatants and civilians in South Sudan. Using 

structural equation modelling techniques to evaluate the hypothesis, 

we found that affiliation and gender were significant predictors of 

social support, while drinking and education were significant 

predictors of quality of life. Drinking was found to be positively 

associated with social support, which may indicate that drinking is 

widely socially accepted as a norm and thus positively associated. 

Surprisingly, however, depression was not found to be as significant 

predictor of either. The significant negative association between 

gender and affiliation suggests that men and women who are not in 

the army may experience poor social support and thus overall poor 

quality of life. As it is, studies show that women exhibit high levels 

of depression and post-traumatic stress disorder. We note a curious 

positive association between suicide and social support, which my 

suggest that high social support, comes with high expectations 

especially for ex-combatants, thus putting pressure on them, and 

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leaving them probably more vulnerable to depression and suicide, 

which may explain why studies find high suicide rates, even though 

depression and suicidal ideation are not found to be significant 

predictors of either. With limited opportunities for socio-economic 

advancement, many would be likely to fulfil the high expectations 

that come with having served one’s country.  On the other hand, ‘too 

much’ social support may also be experienced as infantilizing, and 

produce additional stressors, rather than buffer against stress and 

depression. 

Considering the low literacy rate of the sample (10.5%), and the low 

rate of gainful employment of individuals (13.9%), the challenges 

for providing opportunities for self-actualization through work are 

desperately needed. Ayazi observed that exposure to traumatic 

events coupled with socio-economic disadvantage were significantly 

associated with PTSD or conditions where PTSD was often 

comorbid with depression. Importantly they note that individuals 

with socio-economic disadvantage were most likely to have 

comorbid conditions, and to have experienced more traumatic 

experiences demonstrated by elevated by high levels of 

psychological distress, than individuals with only PTSD and or those 

with depression alone (Ayazi et al., 2012). 

With few mental health professionals South Sudan may wish to 

emulate intervention programs introduced by the Centre for Victims 

of Torture in Liberia and Sierra Leone to help survivors in the 

community. The programs build lasting and continually improving 

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local peer counselling capabilities, support advocacy initiatives and 

educate the local population about mental health issues, reduce 

distress and increasing appropriate referrals and contribute toward 

professional development.  

The study provides additional evidence of factors that are predictors 

of social support and quality of life; that may be important in the 

design of relevant and effective mental health intervention programs 

to educate, identify, provide supportive counselling and treat those 

most at risk in the affected population segments.  

Contributions 

The findings from this study have important practical implications 

for decision makers and policy makers in government departments 

and for implementing partners. In considering different strategies to 

provide mental health resources, including basic training and 

capacity development (e.g. peer counseling) and advance training 

(e.g. Juba University), coupled with community based psycho-

education programs, so as to provide appropriate intervention and 

support to those at risk. These capacity development strategies may 

help prevent complications, improve daily functioning, and help 

reduce depression and the indicated high rates of suicide and suicidal 

ideation. Identifying some of the determinant factors of social 

support and quality of life can make an important contribution to the 

design of the mental health intervention protocol.  

Limitations 

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This study was the first to investigate factors that predict social 

support and quality of life amongst civilians and combatants, and as 

such, literature was found to be limited. Second, the cross sectional 

design of the study does not allow for causal inferences. Third, the 

research used trained research assistants who had to translate and 

interpret questions into local languages. Fourth, another limitation of 

the study is that all data were collected using self-report measures, 

which may lead to the problem of common method bias. However, 

Harmon’s one-factor test did not indicate a problem with common 

method variance.  

CONCLUSION 

The results of the study demonstrate significant predictors of the 

hypothesized quality of life model amongst ex-combatants and 

civilians, and point to possible areas of intervention to ameliorate the 

suffering. A future research would be able to use a well-established 

trauma scale and depression scale to test the latent variable model. 

Specifically, studies are needed to compare and contrast robust 

samples of ethnic group members in other states with more diverse 

ethnic groups and verified ex-combatants and areas of intense war 

and or prolonged skirmishes. 

 

 

 

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