Study on the correlation between physical exercise and sleep quality of normal university students
Bibliographic record
Abstract
This study explores the correlation between physical exercise and sleep quality among college students in normal universities, providing reference for improving the level of physical exercise and enhancing sleep quality. The study used a stratified cluster sampling method and surveyed 240 college students from two normal universities in Guangxi using the Physical Activity Readiness Survey (PARS-3) and the Pittsburgh Sleep Quality Index (PSQI) questionnaires. The results show that physical exercise is higher in male students, rural students, and third-year students compared to other types of students within the group (t/F values of 2.03, 6.62, and 6.87, P<0.05). Sleep quality scores are higher in female students and fourth-year students compared to other categories within the group (t/F values of -2.23 and 5.42, P<0.05). The correlation coefficient between physical exercise and sleep quality is r=-0.259 (P<0.01), and the regression model indicates that physical exercise has a significant negative predictive effect on sleep quality (β=-0.25, P<0.01). In conclusion, there are significant differences in physical exercise among college students in normal universities based on gender, hometown, and grade. There are also significant differences in sleep quality among college students in normal universities based on gender and grade. Furthermore, there is a significant negative correlation between physical exercise and sleep quality. It is recommended that schools encourage students to participate in physical exercise to improve their sleep quality and achieve physical and mental health.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".