A systematic process to formulate quality lessons learned about hospital resilience during the COVID-19 pandemic
Bibliographic record
Abstract
BACKGROUND: The urgency of the COVID-19 pandemic forced hospitals to adjust swiftly to the health crisis. They adopted a variety of solutions and encountered numerous challenges. To enhance hospitals' readiness for future crises, the HoSPiCOVID research project documented the experiences of seven hospitals in five countries (Brazil, Canada, France, Japan, Mali) to extract quality lessons learned (QLLs). These were subsequently refined through workshops involving healthcare professionals. OBJECTIVES: The aim of this study was to examine the process of conducting QLL formulation workshops in the HoSPiCOVID hospitals to identify facilitators and barriers encountered. METHOD: = 13) were conducted with researchers and professionals in the five countries. Interview recordings were imported into NVivo software, transcribed, and thematically analyzed. RESULTS: Although the professionals participated actively in the workshops, group dynamics were sometimes impeded by existing power dynamics among participants. Nonetheless, professionals perceived the workshops as an optimal method for formulating QLLs. Distributing summary sheets of workshop content beforehand and ensuring the alignment of content with participants' needs enhanced the effectiveness of the process. CONCLUSION: Despite obstacles encountered in the workshops, participants appreciated the initiative of documenting QLLs using a structured approach in which the QLLs were refined and professionals were able to compare experiences. The QLLs formulated could improve hospitals' responses to future health crises. The recommendations from this study could also enhance the organization of future workshops aiming to formulate QLLs.
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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 | 0.110 | 0.156 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.010 | 0.009 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.005 | 0.019 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".