Lessons learned from the resilience of Chinese hospitals to the COVID-19 pandemic: a scoping review
Bibliographic record
Abstract
Abstract As the SARS-CoV-2 pandemic has brought huge strain on hospitals worldwide, the resilience shown by China’s hospitals appears to have been a critical factor in their successful response to the pandemic. This paper aims to determine the key findings, recommendations and lessons learned in terms of hospital resilience during the pandemic, as well as the quality and limitations of research in this field at present. We conducted a scoping review of evidence on the resilience of hospitals in China during the COVID-19 crisis in the first half of 2020. Two online databases (the CNKI and WHO databases) were used to identify papers meeting the eligibility criteria, from which we selected 59 publications (English: n= 26; Chinese: n= 33). After extracting the data, we present an information synthesis using a resilience framework. We found that much research was rapidly produced in the first half of 2020, describing certain strategies used to improve hospital resilience, particularly in three key areas: human resources; management and communication; and security, hygiene and planning. Our search revealed that considerable attention was focused on interventions related to training, healthcare worker well-being, e-health/ telemedicine, and work organization, while other areas, such as hospital financing, information systems and healthcare infrastructure, were less well represented in the literature. We identified a number of lessons learned regarding how China’s hospitals have maintained resilience when confronted with the SARS-CoV-2 pandemic. However, we also noted that the literature was dominated by descriptive case studies, often lacking consideration of methodological limitations, and that there was a lack of both highly-focused research on individual interventions and holistic research that attempted to unite the topics within a resilience framework. Research on Chinese hospitals would benefit from a greater range of analysis in order to draw more nuanced and contextualised lessons from the responses to the crisis.
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".