Solutions for stressed out students: Modelling relationships between physical activity, subjective well-being, and stress in Chinese university students
Bibliographic record
Abstract
Research has shown that physical activity is linked with both physical and mental health outcomes. The present study examined two physical activity intensities and their relationship to stress and subjective well-being in a sample of mainland Chinese university students (N = 317; 54.9% female). Students completed a survey package in simplified Chinese measuring their leisure time physical activity, subjective well-being, perceived stress, and demographic variables. Two-step structural equation modelling was used to examine the relationships between vigorous and moderate intensity physical activity, and stress and subjective well-being. Sex and exercise as a mechanism to cope with stress were included as covariates in the final model. Moderate physical activity was significantly negatively related to stress (? = -.18), and also shared a relationship with subjective well-being (? = .11). Vigorous physical activity was related to stress (? = -.10). Although the latter two relationships did not meet the p < .05 cutoff, this research implies that physical activity may be used to mitigate stress and improve subjective well-being in Chinese students, and should be promoted by facilitators at Chinese institutions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".