Roots and Causes of Occupational Stress amongst Female Academics in Universities of Technology in South Africa
Bibliographic record
Abstract
Stress and stress-related problems have negative human resource and financial implications for Universities of Technology (UoT) in terms of absenteeism, productivity, organizational effectiveness, employee morale and medical aid subsidies. For tertiary institutions, the impact of stressed academics on core business activities relating to students and examinations are far-reaching. The paper assessed the roots and causes of occupational stress amongst female academics in a UoT in South Africa. The paper adopted a qualitative research approach with a focus group of selected female academics in the UoT. The paper revealed that workload and performance management, as well as family life and personal life; teaching vs research and administration; Covid-19 and online teaching and learning; holidays and leave and lack of leave; meetings and support deficiency; resources and lack of care and empathy, as well as poor HR, bullying and imposition and a lack of professionalism; nepotism and favouritism; retrenchments and instability, along with poor recognition and appreciation, were the roots that contribute to occupational stress in the UoT in SA. The paper recommends that effective interventions be implemented by the UoT in order to manage the stress of these female academics, thereby reducing the negative impact thereof on themselves and the institution. University policy-makers should devise a variety of solutions in a well-balanced package that places responsibility on both the university and staff to manage occupational stress.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".