Volume 9 Number 2, 2024 Faculty of Social Sciences, Enugu State University of Science And Technology EDITOR-IN-CHIEF Prof. Nicholas Attamah MANAGING EDITOR Prof. Barnabas Nwankwo PUBLISHED BY ENUGU STATE UNIVERSITY OF SCIENCE & TECHNOLOGY JOURNAL OF SOCIAL SCIENCES & HUMANITIES 343 Socio-demographic Predictors of Quality of Life among Frontline Army Officers in Nigeria Kayode J. FAKOREDE, Abel C. OBOSI & Emmanuel E. UYE* Department of Psychology, University of Ibadan, Nigeria *Corresponding author: emmanuel.e.uye@gmail.com https://orcid.org/0000-0002-7867-270X Abstract Frontline army officers are the bulk manpower frequently deployed to fight the insurgent groups in Nigeria. While studies have investigated psychological predictors of quality of life among frontline army officers, studies linking socio-demographic variables with quality of life among these officers are lacking. Therefore, this study examined the predictive ability of socio- demographic factors on quality of life among frontline army officers in Nigeria. A cross- sectional survey design was adopted while purposive sampling technique was used to select Abuja and Maiduguri cantonments. Data were collected from 207 frontline army officers using validated questionnaires and analyzed using multiple regression analysis and independent samples t-test. Two hypotheses were tested and accepted at p =.001 level of significance. The result indicated that socio-demographic factors of age, religion, marital status, educational level and income level jointly predicted quality of life among study participants [R2=.27, F(5, 201) = 14.99, p =.00]. In addition, the result revealed that gender did not significantly influence quality of life among study participants [t(205) = 1.33, p >.05].The study concluded that socio-demographic factors tend to moderately predict quality of life among frontline army officers. It is recommended that military authority should regularly profile frontline army officers to ascertain their quality of life status. Keywords: Socio-demographic factors, Quality of life, Frontline army officers, Nigeria Introduction Quality of life has been variously defined by different authorities to reflect the social, economic and cultural dynamic of the individuals. For example, the World Health Organization (WHO, 2000) conceived quality of life as an “individual’s subjective view of their positions in life in line with their cultures and value systems in which they live and in relation to their goals, expectations, standards and concerns”. Similarly, Amao (2014) described quality of life as the individuals’ subjective judgments of themselves based on their overall assessment of what they hold as important to them at a point in time in their lives. Based on the above definitions, this study conceptualizes quality of life as that which encompasses an individual’s emotional, physical, material and social well-being in time and space. As quality of life measures individual and group well-being, one group of professionals whose quality of life needs to be determined and improved on is the frontline army officers. These are uniformed men and women in frontlines fighting to protect lives and property of the mailto:emmanuel.e.uye@gmail.com https://orcid.org/0000-0002-7867-270X 344 citizenry and defend the territorial boundaries of the Nigerian States. In Nigeria as well as in other sub-Saharan countries, the emergence of bandits, kidnappers, armed insurgents, separatists and other militia groups has resulted to frequent deployment of army officers to the frontline zones to fight with less regard to their personal needs and quality of life (Fakorede, 2023). Because of the duration of deployment to these battle zones, these officers suffered one type of deprivation or the other thus affecting their quality of life (Bature, 2019). Several predictors of quality of life have been investigated among uniformed officers. One group of factors considered in this study as likely predictors of quality of life among frontline army officers are socio-demographic factors of age, gender, religion, educational level, marital status and income level. Age has consistently been found to predict quality of life among different populations and across different samples. For example, Netuveli et al. (2006) found age as a strong predictor of QoL among their study participants. Also, Boswroth et al. (2000) found increased in age to lead to decrease in basic physical functioning, better mental health, and more vitality components of quality of life. However, Wiggins et al. (2004) found QoL to decrease with age. Also, gender is considered to predict quality of life. Females have been found to report lower quality of life compared to their male counterparts. Females tend to outlive males due to their quality of life. scores as measured by the SF-36 than their male counterparts (Boswroth et al., 2000). Furthermore, poor social support was found to predict lower QOL (Boswroth et al., 2000). However, some studies have found contradictory results about the relationship between QOL and gender. While Netuveli et al. (2006) found QoL to be significantly higher for women than men; on the other hand, Wiggins et al. (2004) found no gender difference in QoL among their study participants. Furthermore, an individual marital status tends to be a consistent predictor of quality of life (Netuveli et al, 2006). Similarly, family structure whether monogamous or polygamous could affect quality of life (Fakorede, 2023). Another socio-demographic factor is the income status of the individuals. Studies have shown that income status tends to predict quality of life among different populations including that of frontline officers. Low et al. (2007) found financial resources to have a direct and positive influence on quality of life. Studies by Blane et al. (2004) and Wiggins et al. 2004 have equally supported income level as a robust predictor of quality of life. However, Blane et al. (2004) did not find income level as a predictor of quality of life among their study 345 participants. Conclusively, Netuveli et al. (2006) found socio-demographic factors of age, marital status, family structure and income level as strong predictors of quality of life among veterans from combat operations. There has been a continuous report on the shortage of manpower and material resources to fight the insurgent groups in Nigeria. The frontline army officers are usually deployed at the shortest notice which has serious implications on their quality of life. Studies have investigated different predictors of quality of life among frontline army officers with different outcomes. Also, studies linking socio-demographic variables with quality of life among frontline army officers in Nigeria are lacking which leaves gap in knowledge to be filled. Therefore, this study examined socio-demographic factors as predictors of quality of life among frontline army officers in Nigeria. The specific objectives of this study were (1) to determine whether age, marital status, family background and income level would jointly and independently predict quality of life among frontline army officers in Nigeria; and (2) to establish whether gender would significantly influence quality of life among frontline army officers. The study would provide empirical data on the socio-demographic factors predicting quality of life among frontline army officers in the study population. Also, the study would inform the military authority on the necessary welfare packages that would cushion the quality of life among frontline army officers. Hypotheses The following hypotheses were tested: H1: Age, marital status, family background and income level would jointly and independently predict quality of life among frontline army officers in Nigeria. H2: There would be significant differences between male and female frontline army officers on quality of life. Research Design The study adopted a cross-sectional survey design using a validated scale to gather data. The independent variables were socio-demographic factors of age, gender, religious affiliations, marital status, educational attainment, and income level. The dependent variable was quality of life. 346 Setting The study was conducted among frontline army officers who have experienced combat operations in military cantonments in Abuja and Maiduguri. The reason for the choice of the cantonments was because Abuja hosts the Defense Headquarters, while Maiduguri is among the frontline for the insurgents in Nigeria. Instrument The World Health Organization Quality of Life Brief (WHOQOL-BREF) was used to evaluate the quality of life among frontline officers. The scale consisted of 26 items presented on a 5-point Likert’s format ranging from strongly disagrees to strongly agree. However, the study analyzed the items on the general QOL and general health status of the participants. These items were: “How much do you need any medical treatment to function in your daily life?” and “How healthy is your physical environment?” Vahedi (2010) obtained Cronbach’s alpha of 0.79 while in this study, Cronbach’s alpha of 0.81 was obtained. Socio-demographic variables of age, gender, marital status, religion, educational status, and income level were collected from the participants. Procedure Authority to conduct this study was from the letter of introduction from the Department of Psychology, University of Ibadan, Nigeria and Ethical Approval from the UI/UCH Ethics Committee (UI/EC/22/0348).The approval given by the military authority allowed the researchers to approach the potential participants in their barracks, homes, relaxation centers and after training sessions to solicit for their participation. They were briefed on the purpose of the study and asked for their consent to participate in the study. Based on their acceptance, they were told that participation was voluntary and that their responses would be treated confidentially. Officers who accepted to participate in the study were given questionnaires to fill which took less than 10 minutes to complete. A total of 211 questionnaires were distributed and collected on the spot. However, four questionnaires were half filled and were discarded thus left with 207 used for the final analysis. Data Analysis IBM SPSS version 26 was used for data analysis. Descriptive and inferential statistics were executed on the collected data. Hypothesis 1 was tested using multiple regression analysis 347 while hypothesis 2 was tested using independent samples t-test. All hypotheses were accepted at p = 0.001 level of significance. Results Participants A total of 207 frontline army officers participated in the study. The analysis showed that 64 (31%) of the participants were females while 143 (69%) were male participants. In terms of their ethnicity, 17(8%) were Hausa, 18(9%) were Igbo, 96(46%) were Yoruba while 76 (38%) were from other ethnic groups. The result indicated their marital status as: 98(47%) as singles, 103 (50%) as married while 6(3%) as either separated or divorced. Many of the participants were Christians, 155 (75%), 47 (23%) were Muslims while 4(2%) were Traditionalists. The family structure showed that 84 (41%) were from a monogamous family, 56 (27%) from polygamous family while 67(32%) were from a single parent family. Finally, the income status of the participants showed that 100 (48.3%) earned below N50, 000, about 80 (39%) of the participants earned between N50,000 and N150,000, about 14 (7%) earned between N151,000 and N250,000 while 13 (6%) earned above N250,000. H1: Age, marital status, family structure and income status would jointly and independently predict quality of life among frontline army officers in Nigeria. The hypothesis was tested using multiple regression analysis and the result is presented in Table 1. Table 1. Multiple regression analysis showing joint and independent predictors of quality of life army among frontline officers Predictors β t p R R2 F p Age .02 .35 >.05 . 52 .27 14.99 < .00 Marital status -.06 -.68 >.05 Family structure .49 8.00 <.00 Income level -.01 -.18 >.05 Dependent variable: Quality of Life Table 1 shows joint and independent predictors of quality of life among frontline army officers. The result indicated that socio-demographic factors of age, marital status, family structure and income level jointly predicted quality of life among study participants [R2= .27, F(5, 201) = 348 14.99, p =.00]. This implies that the predictors jointly accounted for 27% of the total observed variance in the dependent variable. Also, the result showed that family structure (=.49, p =.00) independently predicted quality of life among study participants. However, age ( =.02, p >.05), marital status (= -.06, p >.05) and income status (= -.01, p >.05) did not independently predict quality of life among study participants. Therefore, the hypothesis was partially supported. H2: There would be significant differences between male and female frontline army officers on quality of life. The hypothesis was tested using independent sample t-test for independent samples and the result is presented in Table 2. Table 2. Independent sample t-test of mean difference between male and female on quality of life among frontline army officers Gender N Mean SD t df p Male 143 56.99 14.06 1.33 205 >.05 Female 64 59.64 11.32 1.33 Dependent variable: Quality of Life Table 2 presents gender differences in quality of life among frontline army officers in Nigeria. The result showed no gender difference in quality of life among study participants [t(205) = 1.33, p >.05]. However, observation of the mean revealed that female frontline army officers scored higher in quality of life (M = 59.64, SD = 11.32) compared to their male counterparts (M = 56.99, SD = 14.06). Therefore, the hypothesis was partially accepted. Discussion The hypothesis that age, marital status, family structure and income status would jointly predict quality of life among frontline army officers in Nigeria was supported. The combined effects of these socio-demographic variables accounted for 27% of the variance observed in quality of life among study participants. This finding strongly showed the importance of these factors in quality of life including those of frontline army officers. The results supported various studies that found age (Netuveli et al., 2006), marital status (Bosworth et al., 2000; Netuveli et al., 2006), family structure (Netuveli et al., 2006) and income level (Netuveli et al., 2006) as significant predictors of quality of life among different populations and across different samples. More importantly, while other socio-demographic variables did not independently predict quality of life, family structure independently predicted quality of life among study participants. This explains the importance of family structure such as family social support in 349 quality of life determination. This finding lent credence to other previous studies relating family structure and family support as important factors in achieving quality of life (Keyes et al. 2002; Rustoen et al. 2005). Finally, the hypothesis that female frontline army officers would report higher quality of life than their male counterparts was partially supported. At the general levels, the result indicated that gender did not influence quality of life among frontline army officers. However, the mean difference revealed that females marginally enjoyed higher quality of life than their male counterparts. This could be that while male frontline army officers are the chief breadwinners for so many family members, their female counterparts are likely to have support from spouses to augment their financial status thus helping to improve their material quality of life. Again, female frontline army officers may perform supportive duties when on duty with less stress compared to their male counterparts’ thereby showing a differential in their quality of life measures. Previous studies have varied results. For example, Wood et al. 2005 and Stenzelius et al. (2005) found men to report higher quality of life, however, Fakorede (2023) and Netuveli et al. (2006) found females to have higher quality of life among frontline army officers. Conclusion This study has empirically confirmed age, marital status, family structure and income status as joint predictors of quality of life among frontline army officers. Also, female frontline army officers demonstrated better quality of life than their male counterparts. Recommendations The study makes the following suggestions to improve the quality of life among frontline army officers: Because socio-demographic variables jointly predict quality of life among study participants, there is need to look at each of these variables and address them in order to improve quality of life among study participants. Also, since quality of life tends to be better on the part of the female frontline officers, the military authority needs to motivate male frontline officers with more resources to enhance their quality of life. Limitation of Study The study has some limitations which need to be improved in further study. Using self-reported questionnaires introduced response bias. Further study would benefit from longitudinal studies and use of documentary evidence from the potential participants to triangulate data collected from self-reported questionnaires. The selection of only two frontline cantonments and the 350 sample size of 207 hinder generalization of study findings. Further study should include other frontline zones in Nigeria with increased sample size. Finally, the investigation of only socio- demographic variables left out psychological factors. Psychological factors such as personality traits, learned helplessness and social support should be investigated in further study. References Amao, F. L. (2014). Quality of life of poor residential neighbourhoods in Osogbo, Nigeria. International Scholarly and Scientific Research & Innovation, 8(4), 914-917. Bature, E.A.(2019). Combat operations in the Nigerian armed forces: Issues and way forward. Arts & Social Science Research, 9, 103-121. Blane, D., Higgs, P., Hyde, M., & Wiggins, R. D. (2004). Life course influences on quality of life in early old age. Social Science & Medicine, 58, 2171-9179. Bosworth, H. B., Siegler, I. C., Olsen, M. K., Brummett, B. H., Barefoot, J. C., Williams, R. B., Clapp-Channing, N. E., & Mark, D. B. (2000). Social support and quality of life in patients with coronary artery disease. Quality of Life Research, 9, 829-839. Fakorede, J. K. (2023). Psychological factors predicting quality of life among military combatants in Nigeria. BSc project submitted to the Department of Psychology, University of Ibadan, Nigeria. Pp. i + viii, 1-67. Keyes, C. L., Shmotkin, D., & Ryff, C. D. (2002). Optimizing well-being: The empirical encounter of two traditions. Journal of Personality & Social Psychology, 82, 1007- 1022. Low, G., Molzahn, A. E. (2007). Predictors of quality of life in old age: A cross-validation study. Research in Nursing & Health, 30, 141-150. Netuveli, G., Wiggins, R. D., Hildon, Z., Montgomery, S. M., & Blane, D. (2006). Quality of life at older ages: Evidence from the English Longitudinal Study of Aging (Wave 1). Journal of Epidemiological Community Health Studies, 60, 357-363. Netuveli, G., Wiggins, R. D., Hildon, Z., Montgomery, S. M., & Blane, D. (2006). Quality of life at older ages: Evidence from the English Longitudinal Study of Aging (Wave 1). Journal of Epidemiological Community Health Studies, 60, 357-363. Rustoen, T., Wahl, A. K., Hanestad, B. R., Lerdal, A, Paul, S., & Miaskowski, C. (2005). Age and the experience of chronic pain: Differences in health and quality of life among younger, middle-aged, and older adults. The Clinical Journal of Pain, 21, 513-523. Stenzelius, K., Westergren, A., Thorneman, G., & Hallberg, I. R. (2005). Patterns of health complaints among people 75+ in relation to quality of life and need of help. Archives of Gerontology and Geriatrics, 40, 85-102. 351 Vahedi, S. (2010).World Health Organization Quality-of-Life Scale (WHOQOL-BREF): Analyses of their item response theory properties based on graded responses model. Iran Journal of Psychiatry, 5(4), 140-153. Wiggins, R. D., Higgs, P. F. D., Hyde, M., Blane, D. B. (2004).Quality of life in the third age: Key predictors of the CASP-19 measure. Aging & Society, 24, 693-708. Wood, R. H., Gardner, R. E., Ferachi, K. A., King, C., Ermolao, A., Cherry, K. E., Cress, M. E., & Jazwinski, S. M. (2005). Physical function and quality of life in older adults: Sex differences. Southern Medical Association, 98, 504-512. World Health Organizations (2000). Constitution of the World Health Organizations. Geneva: World Health Organizations, Switzerland. Page 7