Differential Impact Pathways of Sports Types on College Students' Mental Health
Bibliographic record
Abstract
This study employs qualitative research methods to systematically explore the mechanisms through which three types of sports—team competitive, individual competitive, and performance-aesthetic—affect college students' mental health. Based on positive psychology theory, social cognitive theory, and the dynamic model of psychological resilience, a dual-mediation theoretical model was constructed: "sports type → self-efficacy/psychological resilience → mental health." The findings indicate: (1) team competitive sports exert benefits via social support pathways ("Team cooperation → Belongingness → Psychological resilience") and role identity pathways ("Positional specialization → Role responsibility → self-efficacy"); (2) individual competitive sports function through skill refinement pathways ("Technical mastery → Competence confidence → self-efficacy") and independent coping pathways ("Independent coping → Emotion regulation → Psychological resilience"); (3) performance-aesthetic sports impact through body awareness pathways ("Movement precision → Body control → self-efficacy") and rhythm regulation pathways ("Rhythmic synchronization → Emotion regulation → Psychological resilience"). This study offers a systematic theoretical framework and practical guidance for implementing precision sports psychological interventions in higher education institutions.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 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".