Occupational Stress, Coping Strategies, Health, and Well-Being Among University Academic Staff—An Integrative Review
Bibliographic record
Abstract
Occupational stress has been constantly rising among academics in universities globally, which affects their health and well-being. Although some studies reviewed occupational stress in academics, there has been less systematic evidence reviewed occupational stress of academic staff through the lens of the Transactional Model of Stress and Coping (TTSC). This integrative review aims to search, extract, appraise and synthesise recent evidence relating to occupational stress, coping strategies, health, and well-being of university academic staff. The Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) methodology provides a structure for searching and reporting the search outcomes. Primary studies relating to occupational stress, coping strategies, health, and well-being of academics in university published from 2010 onwards were selected from five databases, CINAHL, ERIC, PsycINFO, SCOPUS, and Web of Science in June 2020. Keywords included “stress”, “coping strategy”, “health”, “well-being”, “academics” and “university” in various combinations. The boolean operators “AND” and “OR” were also used. 17 out of 682 articles were included in this review. Most studies reported academics experienced moderate to high level of stress, and the heavy workload was one of the main stressors. Both positive and negative coping methods were used by academics to cope with stress. Occupational stress can contribute to poor mental health and decreased well-being of academics. This review can help to understand the work phenomenon of university academics and improve their health and well-being, which in turn can contribute to satisfaction and productivity within the educational institutes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".