Professional development programs in health promotion: tools and processes to favor new practices
Bibliographic record
Abstract
Developing innovative interventions that are in sync with a health promotion paradigm often represents a challenge for professionals working in local public health organizations. Thus, it is critical to have both professional development programs that favor new practices and tools to examine these practices. In this case study, we analyze the health promotion approach used in a pilot intervention addressing children's vulnerability that was developed and carried out by participants enrolled in a public health professional development program. More specifically, we use a modified version of Guichard and Ridde's (Une grille d'analyse des actions pour lutter contre les inégalités sociales de santé. In Potvin, L., Moquet, M.-J. and Jones, C. M. (eds), Réduire les Inégalités Sociales en Santé. INPES, Saint-Denis Cedex, pp. 297-312, 2010) analytical grid to assess deductively the program participants' use of health promotion practices in the analysis and planning, implementation, evaluation, sustainability and empowerment phases of the pilot intervention. We also seek evidence of practices involving (empowerment, participation, equity, holism, an ecological approach, intersectorality and sustainability) in the intervention. The results are mixed: our findings reveal evidence of the application of several dimensions of health promotion (equity, holism, an ecological approach, intersectorality and sustainability), but also a lack of integration of two key dimensions; that is, empowerment and participation, during various phases of the pilot intervention. These results show that the professional development program is associated with the adoption of a pilot intervention integrating multiple but not all dimensions of health promotion. We make recommendations to facilitate a more complete integration. This research also shows that the Guichard and Ridde grid proves to be a thorough instrument to document the practices of participants.
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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 | 0.080 | 0.060 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.007 | 0.015 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.002 | 0.019 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".