Apprentissage organisationnel en promotion de la santé : une expérience québécoise
Bibliographic record
Abstract
OBJECTIVE: This article focuses on health promotion laboratories, a Quebec professional development program offered by the Public Health Department of the Montréal Region to teams of professionals and managers working in health promotion within local public health organizations. The objective is to examine the process of translating the knowledge gained by participants as a result of the program over the longer term within the organization. METHOD: This was a qualitative descriptive study. The work was guided by Nonaka’s Organizational Knowledge Creation Model. Data were collected from participants at several types of discussion and development events held in the three months following the end of the pilot project. A thematic content analysis was performed using a grid derived from Nonaka’s model. RESULTS: The analysis revealed the presence of both externalization and internalization in two of the sites, as well as a considerable volume of combinations in the four sites studied. In the latter case, the learnings reused over the longer term were similar to those that had been transferred in the short term (e.g. ideas and methods relating to partnership, planning, etc.). CONCLUSION: These results are important, in that they confirm the laboratories’ potential to propagate the learnings throughout the organization, beyond the short-term gains made by participants during the laboratories. These learnings could potentially pave the way for new practices.
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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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.023 | 0.011 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".