Association between Physical Health and Well-being: A Quasi-experimental Study
Bibliographic record
Abstract
OBJECTIVE: To determine the relationship between physical health and well-being among college students in a state university and private college.METHODOLOGY: The study used a quantitative method, utilizing a pretest-posttest study design on 178 college students.The test group received three months of the health and well-being program while the control continued their activities of daily living (ADL).Nutrition, physical activity, and sleep were measured using the adapted Canadian Community Health Survey -Annual Component-2021.Well-being was evaluated using the modified positive emotion, engagement, relationships, meaning, and accomplishments (PERMA) questionnaire.Phase 1 includes gathering the participants' sociodemographic profiles, and the research concludes with the evaluation of the program.SPSS v.27 was used to analyze the data.RESULTS: Multiple regression analysis results for engagement (r(176) = .26,p = .92),relationships (r (176) = .21,p = .06),accomplishments (r(176) = .22,p = .31),and the overall PERMA (r(176) = .13,p = .42)were greater than the significance level of 0.05.However, positive emotion (r(176) = .26,p = .006)and meaning (r(176) = .23,p = .02)results were less than the significance level of a=0.05 indicating evidence of significant relationship.CONCLUSION: The study has established that positive emotion and meaning are significantly related to physical activity, nutrition, and sleep.
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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 | 0.008 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".