S8-3 How do national sport federations implement health promotion? A case study analysis in France
Bibliographic record
Abstract
Abstract Purpose Sports club’s health promotion (HP) has been investigated, illustrating their need for resources, such as support from national sports federations (NSF), to develop into health-promoting settings. The present study investigates how French NSF promote health. Methods A two-step case study design was undertaken. A website analysis of 51 NSF, recognized as health promoting by the Olympic Committee, was executed to examine HP visibility, presence in strategic plans, committees and programs. Based on this search, four NSF were chosen for an in-depth study, including interviews with representatives and a document analysis. Data were analyzed based on the Ottawa Charter strategies for HP. Results NSF are committed to HP, but HP is not mentioned directly, not visible, nor properly understood. Rather, a health topic approach is adopted, specifically focusing on strategies to enhance social and physical health. HP implementation lacks coordination, where resource investment is based on the sports ministries focus on specific health topics, in a reactive manner, to implement national policies. NSF’s implementation of HP is aligned with findings from sports clubs, limiting further contributions towards guidelines to support HP in their affiliated clubs. Conclusions Future research should investigate determinants of HP implementation among NSF to increase their pro-active approach to promote health. Practical implications include a recognition of HP beyond health topics, an integration of health as a transversal aim in NSF’s policies and programs and a governance system coordinating HP activities. Funding The work was supported through a partnership between Santé Publique France, Université Côte d’Azur and the Université de Lorraine.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".