Prevalence and determinants of burnout among staff of the university of Dschang: a cross-sectional study in a Sub-Saharan university
Bibliographic record
Abstract
BACKGROUND: Burnout results from prolonged exposure to chronic work-related stressors. Studies carried out on this syndrome around the world report a high prevalence among education professions. The scarcity of data on this subject in Cameroon motivated us to carry out this study at the University of Dschang. This study aimed to determine the prevalence and identify the determinants of burnout among staff at the University of Dschang. METHODS: This study was conducted in two parts: the first part was descriptive, while the second was analytical (a nested case-control study) and took place from January to May 2024. We distributed a questionnaire to collect sociodemographic and professional data, assess psychological well-being (using the comprehensive KARASEK and SIEGRIST questionnaires), and evaluate burnout (using the MASLACH scale) to staff members who consented to participate. Data analysis was carried out using SPSS version 23 software, and chi-square test and Student's t- tests were carried out to identify associated factors. A p-value < 0.05 was considered significant. RESULTS: We included 322 staff members out of 884, representing a response rate of 36.4%. The median age was 40 (IQR 35-48) years. More than 1/3 had administrative responsibility. Furthermore, 32.8% of staff were experiencing on jobstrain, and 47% did not have social support. The prevalence of burnout was 59.6%, including 75.5% mild burnout, 17.7% moderate burnout and 6.8% severe burnout. Associations were found with the lack of social support (ORa = 2.21; 95% CI [1.30-3.78], p = 0.003), the presence of administrative responsibility (ORa = 2.63; 95% CI [1.52-4.56], p = 0.001). More than half of the teaching staff were overworked. Lack of leisure time, insufficient sleep duration and work overload were not associated with burnout. CONCLUSION: The majority (59.6%) of the staff at the University of Dschang experienced burnout. This was associated with having administrative responsibility and lacking of social support. Staff support measures are recommended.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".