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
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.005 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it