Organizational interventions to support and promote the mental health of healthcare workers during pandemics and epidemics: a systematic review
Bibliographic record
Abstract
BACKGROUND: Understanding organizational mechanisms that protect the mental health of the healthcare workforce during pandemics and epidemics is critical to support decision-making related to worker health and safety. This systematic review aimed to identify organizational-level factors, strategies or interventions that support the mental health of healthcare workers during pandemics or epidemics. METHODS: A comprehensive search was used, including online databases, a grey literature review, and handsearching of reference lists. Studies were eligible for inclusion if they described implementing or testing organizational-level factors, strategies or interventions to support healthcare workers' mental health during pandemics or epidemics. There were no limitations by language, publication status, or publication date. Two reviewers independently conducted screening, data extraction, data analysis and quality appraisal, with conflicts resolved through discussion or third-party arbitration. Data analysis was guided by the Job Demands-Resources Model. A narrative synthesis is presented, given the high degree of heterogeneity across studies. RESULTS: A total of 10,805 articles from database searches and 190 records from other sources were screened. The final review included 86 articles. Studies were of low (n = 11), moderate (n = 39), and high quality (n = 36). Regarding job demands, 40 studies explored high work pressure or heavy workload factors, with the majority investigating working hours (n = 32). Increased working hours may be associated with an increased risk of diverse mental health outcomes. Regarding job resources, leadership factors, strategies (support, appreciation, responsiveness; n = 19) and leadership interventions (n = 3) may be associated with decreased burnout, anxiety, stress, and increased well-being. The availability and adequacy of personal protective equipment (n = 20) may be associated with decreased burnout, anxiety, depression, and stress. Mixed findings were reported on associations between diverse mental health outcomes and training and education (n = 28) or peer support (n = 3). Results should be interpreted cautiously given the high heterogeneity among factors, strategies, and interventions assessed and outcomes measured. CONCLUSIONS: Organizational-level mechanisms can critically influence the mental health of healthcare workers' during pandemics and epidemics. More focused attention is needed to explore and act on the integral role of leadership and the availability of protective equipment to support healthcare workers' mental health.
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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 | 0.012 | 0.055 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.007 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 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".