Workplace Mental Health Status Among Academic Staff: Psychological Distress, Burnout, and Organisational Culture at a South African University
Bibliographic record
Abstract
Mental health challenges in academic settings are increasingly recognised, yet research on staff wellbeing remains limited, particularly within African universities. This study provides the first institution-wide assessment of psychological distress and burnout among academic staff at a South African university. A cross-sectional survey using validated tools, the 28-item General Health Questionnaire (GHQ-28 ) and the Oldenburg Burnout Inventory (OLBI) was administered to 157 academic employees, and data were analysed using descriptive statistics, non-parametric tests, and ordinal regression. The median age of participants was 42 years (Interquartile range [IQR] = 34–50; SD = 11.4), and the majority of participants were female (n = 110, 70%). The sample included staff across academic ranks, with lecturers being the most common (n = 64, 41%). Results showed that nearly half of participants (49%) exhibited severe psychological distress, and over a quarter (27%) reported high levels of burnout. Female staff reported significantly higher distress and burnout scores compared to their male counterparts. Less than a third (28%) of participants reported feeling safe to disclose mental health concerns, while over half expressed dissatisfaction with institutional support. Participants indicated strong support for both individual-level services, such as confidential counselling and workshops, and systemic changes, including flexible work arrangements and leadership-driven mental health initiatives. Findings highlight the need for integrated, participatory mental health strategies that are culturally and contextually tailored. These results offer timely evidence to inform the development of institutional strategies, policies, and practices to promote mental health among academic staff.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".