Assessing the quality of life among African medical and health science students using the WHOQOL-BREF tool

Study design

This is a descriptive cross-sectional institutional-based study aimed at assessing the quality of life among students in the Medical and Health Sciences faculties at the International University of Africa (IUA). Ethical approval was obtained from the Research Ethical Committee (REC) of the Faculty of Medicine at IUA (IRB-21).

Study area/setting

The study was conducted at the IUA, located in Khartoum State, Sudan. IUA has a diverse student body of approximately 10,400 students, representing 84 different nationalities. The university includes various faculties and programs, including applied and humanitarian faculties. This study focused on the medical and health science faculties: Medicine, Nursing, Pharmacy, Medical Laboratory, and Dentistry with 3,000 students.

Inclusion and exclusion criteria

The study included students enrolled in the faculties of Medicine, Nursing, Pharmacy, Medical Laboratory, and Dentistry at the International University of Africa, who were aged 18 years or older and provided informed consent to participate. Students who were not enrolled in these specific faculties, were under the age of 18, or who did not provide consent were excluded from the study. Additionally, students who were unable to complete the questionnaire due to language barriers or health issues were also excluded to ensure data quality and reliability.

Study population

The study was conducted from May 1st to May 19th, 2024. This cross-sectional study was conducted at the IAU in Khartoum, Sudan, specifically within the faculties of Medicine, Dentistry, Nursing, Pharmacy, and Medical Laboratory Sciences were the medium of instruction is English. We choose cross-sectional studies because they are widely used to assess the QoL in student populations, as they provide a snapshot of health or well-being at a single point in time. Several studies, such as those by Pagnin & De Queiroz (2015) and Li et al. (2020), have successfully employed cross-sectional designs to explore QoL and its associations with academic factors. Thus, cross-sectional study design was quick, cost-effective, allow large sample sizes, help assess the prevalence of QoL issues, compare subgroups within African students, generate hypotheses for future research, and involve minimal attrition. The curriculum durations were as follows: Medicine and Dentistry programs each extend over 5 years with a two-semester system per year, while the Nursing, Pharmacy, and Medical Laboratory Sciences programs are each structured as four-year courses. The program follows clinical posting of students from first year of training except for pharmacy faculty. The objectives of the study were clearly explained to the participants through the questionnaire, ensuring the confidentiality of their information. The original English version of WHOQOL-BREF was sent to the participants with through email and other electronic medium. The questionnaire began with an electronic consent form, reassuring participants of their autonomy to withdraw from the survey at any time.

Sample size and sampling technique

The sample size calculation was performed using Epi Info software, a tool developed by the Centers for Disease Control and Prevention (CDC) (Dean et al., 2000). With a population size of 3,000 African medical and health science students and a desired confidence level of 95% with a 5% margin of error, the software determined that a sample size of approximately 350 participants would be sufficient to ensure statistical validity and reliability in assessing the quality of life among these students. A non-probability sampling technique, specifically purposive sampling, was employed to recruit students for the medicine and other health science faculty (Fig. 1). This method was chosen because it allowed us to focus on specific subgroups within the health sciences that were of interest for assessing quality of life. By using purposive sampling, we were able to ensure that participants from each relevant discipline were adequately represented, thereby providing insights into the differences in quality of life across these disciplines. The contact details of the participants were obtained from the registration section of the IUA.

Data collection tool

The WHOQOL-BREF tool, developed by the World Health Organization (WHO), was used for data collection, and formal permission was obtained from WHO to use the tool (The WHOQOL Group, 1998b). The questionnaire consisted of two parts:

• 1)

  Socio-demographic data: This section gathered basic information about the participants.

• 2)

  Quality of life assessment: This section included 26 items across four domains—physical health, psychological health, social relationships, and environment. Each item was rated on a 5-point Likert scale ranging from 1 (very poor/very dissatisfied/never/none) to 5 (very good/very satisfied/always/extremely). Additionally, two items assessed the overall satisfaction with the students’ quality of life. The scoring was converted to 0–100 scoring through a standard guideline (World Health Organization, 1996). The scoring was further categorized as excellent quality of life (70–100%), moderate quality of life (50–69%), poor quality of life (30–49%), very poor quality of life (0–29%).

Reliability: The internal consistency of the 26-item WHO-QoL scale, as evaluated by Cronbach’s alpha coefficient, was 0.904. The coefficient for the Physical Health domain (Q3, Q4, Q10, Q15, Q16, Q17, and Q18) was 0.715, showing a slight improvement to 0.735 upon the deletion of Q3, which pertains to physical pain. In the psychological domain (Q5, Q6, Q7, Q11, Q19, and Q26), the coefficient was 0.713, showing a slight increase to 0.746 after removing Q26, which was associated with negative feelings. The social relationship domain (Q20, Q21, and Q22) had a coefficient of 0.663, with no observed improvement upon removing any item. Similarly, the Environment domain (Q8, Q9, Q12, Q13, Q14, Q23, Q24, and Q25) showed a coefficient of 0.773, with no improvement noted upon removing any item.

Data collection technique

Data were collected through a self-administered, close-ended questionnaire, formatted in Google Forms and distributed via various online platforms, including WhatsApp and Telegram.

Statistical analysis

The statistical analysis encompassed three primary components. Firstly, the socio-demographic characteristics of the sample were summarized using frequency, percentage, mean, and standard deviation. Demographic data were analyzed using a one-sample binomial test for dichotomous variables and a one-sample chi-square test for categorical variables with more than two levels. Internal consistency was then assessed through the computation of Cronbach’s alpha coefficient. Addressing missing data involved replacing them with the median value derived from all respondents for the respective question. Any missing value for a domain beyond 20% was subsequently removed to maintain data integrity. To compare the QoL among college students across different groups based on variables such as years, specialties, and gender, multiple linear regression analysis was employed. All key assumptions for linear regression were tested and satisfied. Linearity was confirmed through scatter plots and partial regression plots. The independence of residuals was validated using the Durbin–Watson test. Homoscedasticity was assessed with the Breusch–Pagan test and residual plots. The normality of residuals was verified using Q-Q plots, the Shapiro–Wilk test, and histograms. Multicollinearity was checked with variance inflation factors (VIFs) and tolerance levels. Finally, outliers and influential points were identified and managed through Cook’s distance and leverage values. All statistical analyses were carried out using SPSS software (version 20.0; SPSS Inc., Chicago, IL, USA) for Windows. A significance level of p < 0.05 was employed to determine statistical significance.

The sample’s demographic characteristics

The study sample consisted of 349 African medical and science college students from the International University of Africa, representing 11.6% of the total student population (n = 3,000). The sample comprised n = 135 (38.7%) males and n = 214 (61.3%) females. The majority of student’s hail from East Africa (n = 171, 49.0%), while only 12 (3.4%) are from other regions (Chad, Ethiopia, Egypt, India, and Mozambique). Regarding age distribution, most students fell within the 20–23 years’ category (n = 151, 43.3%), followed by those aged over 23 years (n = 158, 45.3%), with the fewest below 20 years (n = 40, 11.5%). In terms of marital status, the majority were unmarried (n = 294; 84.2%), while the minority were divorced (n = 10; 2.9%). Furthermore, a predominant number of students belonged to the medicine department (n = 119, 34.1%), with the dentistry department comprising the smallest proportion (n = 39, 11.2%). In terms of academic year, the majority of students were in their fourth year, while the first-year cohort represented the smallest group (n = 38, 10.9%) (Table 1).

Differences in QoL with field of study, academic year, gender, marital status, and region. The overall QoL among African medical students showed moderate levels (M = 67.5%, SD = 10.8), with the physical health domain scoring the highest (M = 69.3%, SD = 12.0) and the environmental domain the lowest (M = 62.9%, SD = 12.0). The variation of QoL with demographic data is visually depicted in Fig. 2.

The multiple linear regression model explained 6.8% of the variance in overall QoL scores (R² = 0.068), though it was not statistically significant (F (16, 332) = 1.51, p = 0.094). Dentistry students were notably significant predictors of higher QoL (β = 7.059, p = 0.002). In the physical health domain, the model accounted for 4.6% of the variance (R² = 0.046), but was not significant (F (16, 332) = 0.996, p = 0.461); however, both laboratory (β = 5.223, p = 0.045) and dentistry students (β = 6.328, p = 0.015) scored significantly higher. The psychological health domain was more robust, explaining 7.5% of the variance (R² = 0.075) and reaching statistical significance (F (16, 332) = 1.685, p = 0.048), with dentistry (β = 8.415, p = 0.003) and medicine students (β = 4.433, p = 0.044) scoring higher. The social relationships domain accounted for 7.2% of the variance (R² = 0.072), marginally significant (F (16, 332) = 1.601, p = 0.067), where dentistry students (β = 7.823, p = 0.024) and those aged 20–23 (β = 10.296, p = 0.009) showed higher scores, in contrast to lower scores from second (β = −11.146, p = 0.007), third (β = −13.629, p = 0.002), and fourth-year students (β = −10.144, p = 0.012). The environmental domain had a variance of 6.6% (R² = 0.066) without significant change (F (16, 332) = 1.461, p = 0.112), yet dentistry students still scored higher (β = 7.017, p = 0.007) (Table 2).

Implications for interventions

The study highlights several areas where targeted interventions could improve QoL among African medical students. Developing support systems for fresher students and those in high-stress specialties like pharmacy and nursing could mitigate some of the negative impacts on their QoL. Programs that enhance coping skills, resilience, and provide psychological support could be particularly beneficial.

Additionally, the high mean score but not significant role of marital status in psychological well-being suggests that fostering strong social support networks within the university might help unmarried students enhance their QoL. Peer support groups, mentorship programs, and counseling services addressing specific needs and challenges faced by medical students could be instrumental.

Limitations and future research

This study has several limitations. The cross-sectional design provides a snapshot of QoL at a single point in time, limiting the ability to infer causal relationships. Longitudinal studies would be beneficial to track changes in QoL over time and identify key influencing factors. Reliance on self-reported data introduces the potential for response bias. Incorporating objective measures of well-being, such as academic performance or physiological indicators of stress, could provide a more comprehensive assessment of QoL. Future research should explore the impact of specific stressors unique to the African context, such as economic constraints, political instability, and cultural factors, on medical students’ QoL. Comparative studies across different universities and countries within Africa could provide deeper insights into regional differences and help tailor interventions more effectively.