Measuring Health Promotion in Sports Club Settings: A Modified Delphi Study
Bibliographic record
Abstract
Settings-based approaches have become an increasing health promotion focus since the World Health Organization's 1986 Ottawa Charter. While schools, cities, and prisons have implemented this approach, its development within sports environments is recent. Sports are a popular leisure-time activity, requiring validated tools to measure health promotion activity. This study's aim was to develop a measurement tool based on international consensus that measures perceptions of health promotion within sports clubs. It is grounded in the settings-based approach and builds on theory from previous works expanding their context and knowledge. An online, three-round international Delphi study was conducted, inviting experts in sports and health fields to participate in designing the tool. Round 1 created a collaborative list of items; Round 2 validated items based on relevance, importance, and feasibility; and the final round classified items into one determinant category-social, cultural, environmental, or economic. Panelists (69 experts) from 13 countries participated, creating a final list of 62 items at 3 organizational levels; the sports club level included 23 items, the officials level retained 20 items, and the coaching level contained 19 items. This study provides several innovations: (1) applying the settings-based approach to health promotion within sports clubs, (2) defining each club level (sports club, official, coaching) and determinants (social, cultural, environmental, economic) within 3-levels, (3) creating a tool that measures perceptions of health-promotion activities per level and determinant, and (4) obtaining expert consensus on included items. These advancements allow further research on promoting health within sports clubs.
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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 | 0.046 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".