Soutenir les micro-innovations comme moteur du changement en santé grâce à des approches participatives
Bibliographic record
Abstract
Les systèmes de santé font face à des défis croissants qui nécessitent des innovations adaptées au terrain. En nous appuyant sur la place centrale des professionnels et des utilisateurs dans le Consolidated Framework for Implementation Research (CFIR; Breimaier et collab., 2015) pour instaurer et implanter le changement, nous mettons de l’avant le concept des micro-innovations. Ces initiatives locales peuvent transformer les pratiques organisationnelles. Nous présentons d’abord une micro-innovation locale qui a transformé les soins en fin de vie grâce à un protocole visant à protéger le patient et ses proches de la COVID-19 afin d’assurer du soutien émotionnel aux soins intensifs. Nous suggérons ensuite trois approches participatives pour faciliter l’émergence de micro-innovations : le design thinking, les hackathons et la cocréation avec les patients et proches-partenaires. Pour générer un réel impact, ces micro-innovations nécessitent un accompagnement organisationnel. En les valorisant, les systèmes de santé peuvent évoluer de manière plus agile, inclusive et durable.
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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 | 0.086 | 0.082 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.006 | 0.022 |
| Scholarly communication | 0.018 | 0.013 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.011 | 0.003 |
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".