Factors Mediating the Psychological Well-Being of Healthcare Workers Responding to Global Pandemics: A Systematic Review
Bibliographic record
Abstract
The worldwide outbreak of the novel coronavirus (COVID-19) and the likelihood of future pandemics has raised the attention to the effects of pandemics on the psychological well-being of individuals. Given their indispensable role in such situations, healthcare workers are at greater risk of mental health issues. This paper aimed to review the mediators of psychological well-being among healthcare workers responding to global pandemics. After registration on PROSPERO, a systematic review was performed in four databases. Following study selection (PRISMA guidelines), inclusion criteria and analysis methods were assessed. The quality of the included studies was assessed using the EPHPP criteria. Out of 1467 references, 39 studies were included in this review. In most studies, worse well-being outcomes, such as stress, depressive symptoms, anxiety, and burnout were related to demographic characteristics, direct contact with infected patients, and poor perceived support. In turn, self-efficacy, coping ability, altruism, and support from employers and organisations were found to be protective factors. Despite some limitations in the quality of the available evidence, this review highlights the prevalence of poor mental health outcomes in healthcare workers responding to global pandemics. Future interventions should target the identified mediators to promote psychological well-being among this population, particularly social and organisational support, which may improve workers’ mental health and reduce burnout and turnover.
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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 | 0.005 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".