Coping with burnout and the impact of the COVID-19 pandemic on workers’ mental health: A systematic review
Bibliographic record
Abstract
Introduction The COVID-19 pandemic had a negative impact on the psychological wellbeing of workers worldwide. Certain coping styles may increase burnout risk. To investigate the relationship between burnout and coping styles, a systematic review was performed. Methods Following the PRISMA statements, three databases were screened up until October 2022, including research articles written in English language and investigating the relationship between burnout and coping strategies in workers. The quality of articles was assessed by the Newcastle-Ottawa Scale. Results The initial search resulted in 3,413 records, 15 of which were included in this review. Most studies were performed on healthcare workers ( n = 13, 86.6%) and included a majority of female workers ( n = 13, 86.7%). The most used burnout assessment questionnaire was the Maslach Burnout Inventory ( n = 8, 53.3%), and the most used coping assessment tool was the Brief-COPE ( n = 6, 40.0%). Task-related coping was a protective factor for burnout in all four studies investigating its correlation with burnout dimensions. Two of the four studies investigating emotion-oriented coping found that it was protective while the other two found that it was predictive of burnout. All five studies investigating avoidance-oriented coping and burnout dimensions found that this coping style was predictive of burnout. Discussion Task-oriented and adaptive coping were protective for burnout, avoidance-oriented, and maladaptive coping were predictive factors of burnout. Mixed results were highlighted concerning emotion-oriented coping, suggesting that different outcomes of this coping style may depend on gender, with women relying more on it than men. In conclusion, further research is needed to investigate the effect of coping styles in individuals, and how these correlates with their unique characteristics. Training workers about appropriate coping styles to adopt may be essential to enact prevention strategies to reduce burnout incidence in workers.
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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 | 0.007 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.011 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".