Gestion complexe des partenariats lors d'une campagne de promotion de la santé
Bibliographic record
Abstract
This article discusses an analysis of partnerships in the context of health promotion. The 5/30 Health Challenge, or "Défi Santé 5/30", is a campaign to promote healthy eating habits in Quebec. The authors employ this as a case study in order to 1) describe the actors and the nature of their involvement during the campaign's development, design and dissemination; 2) illustrate the interaction of these actors during the conceptualization and rollout of the campaign; 3) propose a paradigm that supports the identification of factors that contribute to or impede partner relationships. The "Défi Santé 5/30" example demonstrates that the creation and maintenance of a partnership network depends on the following key factors: dialogue between partners and the organization responsible for the campaign; the participation of partners at every stage of the campaign (no matter how many there are); allocation of sufficient time for the conceptualization of campaign materials. Dialogue between partners and the central organizer must be guaranteed through the establishment and use of a managerial contract that clearly outlines the role of each actor in the campaign. Further, the partners' activities during the campaign should be regulated through both a formal agreement and a code of ethics. Any campaign's efficiency is directly linked to these factors, among others. The study of partnerships between public, public-private, and private organizations within the framework of health promotion campaigns, thus, merits further study. In addition, to maintain alliances with partners, it is important to demonstrate the benefits of such arrangements to each partner and to equally ensure the contributions of each, be they public, private, media, or community-based organizations.
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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 | 0.007 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.009 |
| Scholarly communication | 0.011 | 0.004 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 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".