Hospital Resilience to the COVID-19 Pandemic in Five Countries: A Multiple Case Study
Bibliographic record
Abstract
Since the beginning of the pandemic, hospitals have been central to the COVID-19 response, often experiencing severe financial, material, and human constraints. In this special issue, we present some of the findings of the HoSPiCOVID research project. One of its main objectives was to compare hospital responses to the first and second waves of the COVID-19 pandemic in Brazil, Canada, France, Japan, and Mali. Studying and comparing how nine different hospitals coped with the pandemic in terms of preparedness and response allowed us to: 1) identify strengths and weaknesses of their responses, including challenges for hospital professionals; and 2) produce lessons learned, using a systematic approach to reflect and analyze their potential of resilience to the crisis. In the five countries, research teams conducted in-depth qualitative studies focused on nine large hospitals, using observation sessions, semistructured interviews with hospital professionals, and lessons learned workshops. The empirical work was supported by an original analytical framework on hospital resilience and a heuristic tool focused on configurations. The studies demonstrate that the hospitals were able to absorb and/or adapt to the crisis by deploying different coping mechanisms, which often required extensive involvement of hospital professionals. More extended study periods would be needed to assess the sustainability of these coping mechanisms and discern whether they have transformative potential. These international comparisons of hospital resilience, based on studies of contrasting contexts and epidemiological situations, allowed researchers to identify lessons learned to support hospital decision-makers in thinking more deeply about managing future health 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 | 0.009 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.012 | 0.005 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".