Health Science Popularization Strategies in Senior Sports Events: A Case Study of “Leaping Cup Badminton”
Bibliographic record
Abstract
As the ageing of the population has been experienced globally, prevention of chronic diseases and health promotion have become core issues regarding health. The purpose of this study is to examine how amateur senior sports events can serve as platforms for popularizing health science, using the “Leaping Cup Badminton” tournament as a case. Based on public resources, reports from five editions of the media (2018-2024), and semi-structured interviews, we conducted a qualitative content analysis to understand how medical knowledge can be integrated into events without compromising the sporting experience. Results suggest short discussions with experts, on-site demonstrations, champion demonstrations, and visual handouts as the strategies, supported by emcee recaps and short-video media. These strategies enhanced recollection of knowledge, promoted health-protective behaviors, and created trust in partnering health brands. The findings indicate that the presence of a physician and the first-service brand improve credibility, whereas repeated exposure and modular content have a stronger effect on retention. The paper proposes an event-based dissemination model by which on-site learning, services to participants, data collection, and follow-up are interconnected. This model is an effective and repeatable channel to increase senior health communication. Community sports events may be used as a means of promoting health beyond merely being a competition, but also as an excellent way of national health promotion in relation to the Healthy China 2030 and even an extensive national health agenda.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.013 | 0.004 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".