Good Health Educational Management Strategies for Enhancing Sustainable Sports Participation of College Students
Bibliographic record
Abstract
This study aims to develop and validate effective health educational management strategies to enhance sustainable sports participation among college students. Specifically, it seeks to (i) assess the current and desirable conditions of students’ sports participation and identify supporting or limiting factors, (ii) formulate targeted strategies through structured analysis, and (iii) evaluate the adaptability and feasibility of these strategies. A mixed-method research design was employed, combining quantitative and qualitative approaches. A total of 384 college students were selected through stratified sampling, while 16 teachers, 12 focus group experts, and 5 evaluation experts were selected via purposive sampling. Research instruments included structured questionnaires, in-depth interviews, SWOT and TOWS matrix tools, and a five-level scoring scale. Data were analyzed using descriptive statistics, the Modified Priority Needs Index, and thematic content analysis. Results revealed significant gaps across all eight dimensions of sustainable sports participation, particularly in motivation and institutional support. The developed strategies addressed education system reform, campus environment optimization, and stakeholder collaboration. Evaluation results confirmed high levels of both adaptability and feasibility, indicating strong potential for implementation and long-term impact.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".