Impact of the COVID-19 pandemic on health emergency and disaster risk management systems: a scoping review of mental health support provided to health care workers
Bibliographic record
Abstract
OBJECTIVES: This systematic scoping review examined the strategies used by different countries and institutions to support the mental health of health care workers (HCWs) during the COVID-19 pandemic, to identify effective practices and the lessons learned in dealing with the associated challenges. METHODS: Of 1330 retrieved articles from PubMed, Scopus, and the Web of Science, 34 articles were ultimately included in the final analysis. RESULTS: The analysis revealed that mental health consultation services, especially telephone support lines, online interventions, and apps, played a critical role in addressing the psychological burden experienced by HCWs. Group activities and peer support strategies offered personalized support, and educational programs offered crucial information regarding stress management. Improvements in the work environment, such as the addition of dedicated rest areas, enhanced the well-being of HCWs. However, many interventions suffered from low participation and a lack of tailored content, despite their apparent effectiveness. CONCLUSIONS: Many interventions have focused on psychological support and resilience-building for HCWs, but they often overlook systemic issues. Comprehensive mental health support must address these systemic factors, such as adequate staffing, training, and resource allocation. Future strategies should emphasize leadership commitment to tackling root causes and actively involve HCWs in program design to ensure relevance and effectiveness. Educational resources and wellness interventions, although reported as effective, need to be tailored and adapted to specific emergencies. Additionally, research gaps, especially in low-resource settings, highlight the need for further studies to enhance preparedness for future crises.
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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 | 0.015 | 0.072 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.007 |
| Bibliometrics | 0.017 | 0.017 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| 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".