The Effect of Occupational Stress and Coping Strategies on Mental Health and Emotional Well-Being Among University Academic Staff During the COVID-19 Outbreak
Bibliographic record
Abstract
The level of stress among academics in higher education institutions has significantly increased over the past decade. Mental health and well-being of academics can be affected once they are exposed to stressful work conditions and use negative coping strategies. This study was set against the backdrop of the pandemic disease, COVID-19, which has challenged the daily work of academics and risen to the various new stressors. This study aims to investigate the current status of occupational stress, coping styles, mental health and emotional well-being of university academics during the COVID-19 outbreak in Northern Ireland, and examine the effect of stress and coping strategies on mental health and emotional well-being. A cross-sectional online survey was conducted using a sample size of 87 academic staff working in a university in Northern Ireland. SPSS version 25 was used to analyse the collected data. The results showed academics experienced moderate stress levels, and distraction behaviours were the most common form of coping mechanism. Academics were in the moderate status of mental health and poor emotional well-being. Occupational stress has a significant effect on mental health and emotional well-being. Positive reframing and acceptance coping styles have an impact on emotional well-being. This study contributes to the understanding of occupational stress, coping strategies, mental health and emotional well-being of academics in higher education in Northern Ireland. The findings can help to develop reliable methods to inform policy on health and well-being for university academics, which in turn lead to increased productivity at work.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 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".