Learning from the resilience of hospitals and their staff to the COVID-19 pandemic: a scoping review
Bibliographic record
Abstract
Abstract Background The COVID-19 pandemic has brought huge strain on hospitals worldwide. It is crucial that we gain a deeper understanding of hospital resilience in this unprecedented moment. This paper aims to report the key strategies and recommendations in terms of hospitals and professionals’ resilience to the COVID-19 pandemic, as well as the quality and limitations of research in this field at present. Methods We conducted a scoping review of evidence on the resilience of hospitals and their staff during the COVID-19 crisis in the first half of 2020. The Stephen B. Thacker CDC Library website was used to identify papers meeting the eligibility criteria, from which we selected 65 publications. After having extracted data, we presented the results synthesis using an “effects-strategies-impacts” resilience framework. Results We found a wealth of research rapidly produced in the first half of 2020, describing different strategies used to improve hospitals’ resilience, particularly in terms of 1) planning, management, and security, and 2) human resources. Research focuses mainly on interventions related to healthcare workers’ well-being and mental health, protection protocols, space reorganization, personal protective equipment and resources management, work organization, training, e-health and the use of technologies. Hospital financing, information and communication, and governance were less represented in the literature. Conclusion The selected literature was dominated by quantitative descriptive case studies, sometimes lacking consideration of methodological limitations. The review revealed a lack of holistic research attempting to unite the topics within a resilience framework. Research on hospitals resilience would benefit from a greater range of analysis to draw more nuanced and contextualized lessons from the multiple specific responses to the crisis. We identified key strategies on how hospitals maintained their resilience when confronted with the COVID-19 pandemic and a range of recommendations for practice.
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How this classification was reachedexpand
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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".