Innovation en santé conduite par les médecins et infirmières : l’approche du design participatif à l’hôpital
Bibliographic record
Abstract
L’objectif de cet article est d’explorer la manière dont les professionnels de la santé contribuent à la conception d’une technologie en santé et d’identifier les éléments qui soulignent la pertinence d’une approche de design participatif dans ce contexte. Pour cela, notre réflexion prend appui sur un projet de conception d’une technologie en santé par les médecins et les infirmiers/ières qui a pour but de les aider à gérer les surcharges informationnelle, communicationnelle et cognitive à l’hôpital. Nous proposons dans cet article un retour réflexif sur cette approche de design participatif. Pour ce faire, nous examinerons l’engagement des professionnels dans la production d’une analyse de leur activité clinique et de leurs pratiques informationnelles, le tout participant au développement d’une technologie ( Machine Learning ) qui contribuera à réduire les différentes formes de surcharge qu’ils doivent quotidiennement gérer. Codes JEL : Y800, I190
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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 | 0.093 | 0.101 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.007 | 0.015 |
| Scholarly communication | 0.018 | 0.011 |
| Open science | 0.004 | 0.013 |
| Research integrity | 0.007 | 0.005 |
| Insufficient payload (model declined to judge) | 0.016 | 0.002 |
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".