Vision, planification et évaluation : les conditions clés du succès d'un changement?
Bibliographic record
Abstract
Résumé Le gestionnaire à qui l’on confie le mandat d’introduire un changement trouve dans la documentation beaucoup de conseils, de principes et de prescriptions à suivre. Cet article entend remettre en question trois leçons ou recommandations visant la conduite d’un changement planifié : l’idée qu’il faut créer une vision du changement et que cette vision doit être partagée, l’idée qu’il faut planifier minutieusement un changement d’envergure et l’idée qu’il faut évaluer ce changement selon l’atteinte des objectifs qui ont été formulés initialement. Notre étude de cas, menée dans un milieu hospitalier, confirme et illustre plutôt qu’un changement majeur prendra forme même si tout le personnel n’en a pas la même vision, qu’il résulte plus de l’action que de la planification et que sa réussite ou son échec s’avère une appréciation souvent très relative et subjective.
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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 | 0.050 | 0.215 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.015 |
| Scholarly communication | 0.016 | 0.013 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.011 | 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".