Le coaching de gestionnaires : mieux le définir pour mieux intervenir
Bibliographic record
Abstract
Résumé Le coaching de gestionnaires vise à fournir aux cadres les divers instruments nécessaires pour se développer et pour accroître leur efficacité. Cet article présente une synthèse de la documentation sur le coaching de gestionnaires. Après avoir expliqué l’engouement pour cette pratique de gestion, nous décrivons les étapes du coaching de gestionnaires et les modèles d’intervention sur lesquels il s’appuie. Nous traitons aussi des contextes où l’on fait appel au coaching de gestionnaires, à savoir le changement organisationnel, la transition professionnelle, l’amélioration continue et une performance déficiente. Ensuite, nous décrivons les avantages et les inconvénients du recours à un coach interne ou à un coach externe et nous relevons les compétences et les caractéristiques d’un bon coach . Finalement, nous donnons des conseils pour optimiser l’efficacité des interventions de coaching de gestionnaires et s’assurer qu’il respecte des considérations éthiques.
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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 | 0.009 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 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".