Development Guidelines for Human Resource Management in Leisure Sports Programs: A Systematic Analysis of Chinese Higher Education Institutions
Bibliographic record
Abstract
This study examines the development and implementation of human resource management practices within leisure sports programs at Chinese higher education institutions. The research addresses critical challenges emerging from the sector’s rapid expansion and increasing professional requirements. Using a sequential mixed-methods approach, the study collected data through quantitative surveys from 88 education professionals across five leading institutions and qualitative evaluations from seven expert panelists. The findings reveal significant gaps between current practices and desired standards in six core management components, with human resource planning and recruitment management showing the most substantial needs for improvement. Expert validation confirmed high suitability and implementation feasibility of the proposed management framework. Based on these findings, the study develops comprehensive guidelines for enhancing human resource management practices, focusing on strategic planning, recruitment processes, professional development, and performance evaluation systems. These evidence-based guidelines provide practical implications for improving collegiate leisure sports programs while contributing to the theoretical understanding of specialized program management in Chinese higher education contexts. The results suggest that systematic implementation of these guidelines could significantly enhance program quality and institutional effectiveness in leisure sports education.
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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 | 0.124 | 0.178 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.023 | 0.025 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".