Échanges interprofessionnels en temps de COVID-19 à l’hôpital Bichat Claude-Bernard : éclairages pour la recherche
Bibliographic record
Abstract
The management of the COVID-19 epidemic has disrupted the organization of healthcare in hospitals. As part of a research project on the resilience of hospitals and their staff to the COVID-19 pandemic (HoSPiCOVID), we have documented their adaptation strategies in five countries (France, Mali, Brazil, Canada, Japan). In France, at the end of the first wave (June 2020), a team of researchers and health professionals from the Bichat Claude-Bernard Hospital organized focus groups to acknowledge these achievements and to share their experiences. One year later, further exchanges were held to discuss and validate the research results. The objective of this short contribution is to describe the insights of these interprofessional exchanges conducted at the Bichat Claude-Bernard Hospital. We show that these exchanges allowed: 1) to create spaces for professionals to speak, 2) to enrich and validate the data collected through a collective acknowledgment of salient aspects related to the experiences of the crisis, and 3) to account for the attitudes, interactions, and power dynamics for these professionals in a crisis management context.
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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 | 0.035 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.009 | 0.009 |
| Scholarly communication | 0.011 | 0.008 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.006 | 0.009 |
| Insufficient payload (model declined to judge) | 0.006 | 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".