Mental Health in Higher Education: A Comparative Stress Risk Assessment at an Open Distance Learning University in South Africa
Bibliographic record
Abstract
Universities depend on committed efforts of all staff members to function effectively. However, where occupational demands outweigh occupational resources, challenging work becomes stressful, followed by an exhausted, disengaged workforce. It is unlikely that disengaged university staff will provide adequate care and service to geographically distant and psychologically isolated learners. As students rely heavily on the support of both administrative staff, as well as academic staff, to manage their learning experience, the work stress experienced by both groups deserves research attention. This study employed a comparative mixed method design, including administrative and academic staff from an Open Distance Learning university in South Africa using the Job Demands-Resources measurement instrument. Findings established from 294 university staff members elucidated staff members’ experience of work stress within a mega-distance learning university in the developing world. Mindfulness about the stressors that influence university personnel can inform strategic interventions required to alleviate distress for each employment category.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 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".