Comment favoriser la réussite d’une démarche d’implantation d’un programme au sein d’un milieu d’intervention : leçons tirées d’une étude de cas
Bibliographic record
Abstract
Abstract: Using a case study methodology, this article describes the process of implementing a cognitive-behavioural program in eight residential facilities in a Centre jeunesse du Québec. The objectives were to better understand the steps required to consolidate the implementation of the program and to identify structural factors that facilitate and impede the progress of successful implementation. Documentary analysis and qualitative interviews allowed deconstruction of the implementation process into seven phases: initial reluctance, training, exploration, resistance, application, reconciliation, and integration. The analysis shows that several structural factors have a noteworthy influence on the implementation process in this case study: the opportunities for discussion, the quality of the program to be implemented, the organizational context, and the team social climate. These factors largely shape the framework of the implementation process. The results of the case study may be useful in other implementation processes, allowing identification of potential difficulties and the mechanisms required for their resolution.
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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 | 0.017 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.004 | 0.003 |
| 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".