School-wide publications
Permanent URI for this collectionhttps://dspace.njala.edu.sl/handle/20.500.144402/169
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Item Dataset for Psychosocial Stress Among Public Health Students in Sierra Leone(European Organization for Nuclear Research, 2025) Kamara, DanielThis dataset contains anonymized survey data used in the study examining the prevalence and determinants of psychosocial stress among public health students in Sierra Leone.Item Prevalence and determinants of occupational stress among healthcare workers at Bo Government Hospital, Sierra Leone: a cross-sectional study(Springer Science and Business Media LLC, 2026-09-28) Kamara, DanielBackground Occupational stress is a major determinant of healthcare worker wellbeing and health-system performance, yet empirical data from Sierra Leone's provincial referral hospitals are almost entirely absent. This study determined the prevalence of occupational stress, identified the predominant stressors, and examined the individual and occupational factors independently associated with high occupational stress among healthcare workers at Bo Government Hospital, Southern Province, Sierra Leone. Methods A descriptive cross-sectional study was conducted among 260 healthcare workers recruited by stratified proportionate random sampling from nursing, medical, laboratory, pharmacy, allied health, and administrative cadres. Occupational stress was measured using the validated 10-item Perceived Stress Scale (PSS-10) and organisational stressors using the Health and Safety Executive (HSE) Management Standards Indicator Tool. Data were analysed in SPSS v26 using descriptive statistics, Pearson's chi-square tests, and binary logistic regression; adjusted odds ratios (AOR) with 95% confidence intervals (CI) were reported, with statistical significance set at p < 0.05. Results The prevalence of high occupational stress (PSS-10 ≥ 27) was 38.5% (95% CI: 32.6–44.7), with a further 47.3% reporting moderate stress; the mean PSS-10 score was 21.4 (SD 6.8). Nurses recorded the highest prevalence of high stress (42.0%). The most frequently reported stressors were understaffing and high workload (87.3%), inadequate equipment and supplies (82.1%), and long or irregular working hours (79.6%). In the adjusted model, working more than 48 hours per week (AOR 3.05, 95% CI 1.89–4.91), low perceived job control (AOR 2.78, 95% CI 1.65–4.68), low-salary satisfaction (AOR 2.60, 95% CI 1.55–4.37), and nursing cadre (AOR 2.31, 95% CI 1.42–3.76) were the strongest independent predictors of high occupational stress. Conclusions Occupational stress is highly prevalent and structurally driven among healthcare workers at Bo Government Hospital. Modifiable, system-level determinants excessive working hours, low job control, poor remuneration and understaffing should be prioritised through enforced working-hour limits, participatory management, improved remuneration and adequate staffing to protect workforce wellbeing.Item Prevalence and determinants of psychosocial stress among public health students at Njala University, Sierra Leone(Springer Science+Business Media, 2026-09-18) Kamara, DanielPsychosocial stress is a growing public-health concern among university students worldwide and is shaped by local financial, institutional, and digital pressures. No quantitative baseline existed for public-health students at the Kowama Campus of Njala University, Bo, Sierra Leone. Against this backdrop, the present study aimed to determine the prevalence and determinants of psychosocial stress among public-health students at Njala University, Sierra Leone. This cross-sectional analytical study recruited 239 students across three academic levels: Preliminary (Prelim), Higher National Diploma (HND), and Bachelor of Science (BSc). Participants completed a structured questionnaire comprising the Perceived Stress Scale (PSS-10), the 12-item General Health Questionnaire (GHQ-12), and a 10-item checklist of institutional stressors. Data were analysed using descriptive statistics to summarise prevalence and severity, chi-square tests to examine categorical associations, Spearman’s rank correlations to explore relationships between continuous variables, and binary logistic regression to identify predictors of high stress. Both crude (cOR) and adjusted (aOR) odds ratios were reported, with high stress as the dependent variable defined as a PSS-10 score ≥ 27. Moderate-to-high stress (PSS-10 ≥ 14) affected 94.6% (95% CI 90.9–96.8) of students and high stress (≥ 27) affected 26.4% (95% CI 21.2–32.3); mean PSS-10 was 22.8 (SD 5.7). Probable psychiatric morbidity (GHQ-12 ≥ 4) was present in 82.8% (95% CI 77.6–87.1). The highest-ranked institutional stressors were heavy academic workload (mean stress-contribution score \[MSCS\] 4.12), unauthorised monetary demands (4.06), and university fee burden (3.83). In adjusted analysis, social-media use > 4 h/day (aOR 10.2, 95% CI 4.66–22.32), HND level (aOR 4.64, 95% CI 2.13–10.08), fee burden (aOR 3.73, 95% CI 1.57–8.87), high academic workload (aOR 3.44, 95% CI 1.45–8.14), self-financing (aOR 3.38, 95% CI 1.31–8.68), and female sex (aOR 2.24, 95% CI 1.07–4.73) independently predicted high stress. Psychosocial stress is highly prevalent and multifactorial in this population. Institutions should prioritise flexible fee arrangements, safeguards against informal monetary demands, and digital-wellbeing support for students.Item Integrated geospatial analysis of the 2014–2016 Ebola virus disease outbreak in Sierra Leone: district-level kernel density estimation, Getis–Ord Gi* hot-spot statistics and health-infrastructure mapping(Springer Science and Business Media LLC, 2026-08-12) Kamara, DanielBackground. The 2014–2016 Ebola virus disease (EVD) epidemic was the largest on record, and Sierra Leone reported more confirmed cases than any other affected country. Although the outbreak generated extensive epidemiological data, few analyses have combined visual density mapping with formal statistical validation of spatial clustering at the district level. This study applies an integrated geographic information systems (GIS) framework kernel density estimation (KDE), global Moran’s I, and the Getis–Ord Gi\* statistic to characterise the spatial structure of the outbreak and its relationship to health infrastructure across Sierra Leone’s fourteen districts. Methods. District-level confirmed case counts (n = 10,675) consistent with published World Health Organization and Ministry of Health and Sanitation cumulative figures were analysed together with the geographic coordinates of the principal referral hospital in each district. KDE was used to visualise transmission intensity. Global spatial autocorrelation was assessed with Moran’s I, and local clustering with the Getis–Ord Gi\* statistic using a 100 km fixed-distance spatial weights matrix and 999 permutations. Healthcare accessibility was proxied by the distance from each district to its nearest neighbouring referral hospital and to the Freetown hub. Results. Confirmed cases were highly concentrated: the Western Area (Urban and Rural) accounted for 46.2% of the national total and, together with Port Loko, for 59.8%. KDE identified a dominant western transmission core, an eastern origin cluster around Kailahun and Kenema, and intermediate nodes at Makeni and Bo. Global spatial autocorrelation was weak and not statistically significant at the district scale (Moran’s I = 0.045; p = 0.19). Local Gi\* analysis identified a statistically significant western hot spot Port Loko (95% confidence) with Western Area Urban and Rural (90% confidence) and cold spots at Bonthe (95%) and Bo (90%); Kambia and Moyamba reached hot-spot significance through spatial proximity (spillover) to the high-burden western cluster. Hot-spot districts lay closer to referral care (mean 41 km) and to the Freetown hub (mean 52 km) than non-significant and cold-spot districts (55–66 km and 155–213 km, respectively). Conclusions. The outbreak’s spatial signature was dominated by urban amplification and connectivity to the western coastal hub rather than by strong district-level autocorrelation. The weak global clustering indicates that the 14-unit district scale is too coarse for robust hot-spot inference; chiefdom-level (149-unit) analysis is required to validate localised clusters. The integrated KDE–Gi\*–infrastructure framework nonetheless offers a transparent, replicable basis for spatially targeted epidemic preparedness in Sierra Leone.