Physical activity information seeking among university students
Bibliographic record
Abstract
Research suggests postsecondary students are a unique population experiencing significant declines in physical activity participation during the transition to university, with unique motives for participation. The purpose of this study was to: (i) identify which media tools (e.g., social media, paper media, peer networks) are preferred for physical activity information seeking, and (ii) examine outcome expectations for physical activity participation (e.g., weight loss, socialization, health benefits), among University of Alberta students. This analysis examined the relationships between physical activity information seeking, preferences for format, and student demographics. Survey results (N=1046) showed female students cited weight loss for appearance purposes as the main reason they look for information more so than male students, x2 (11) = 22.26, p = .02, while male students cited muscle gain for appearance as a top reason more than females, x2 (11) = 38.24, p < .001. Additionally, females were significantly more likely than males to cite accessing information to improve muscle tone as a secondary reason x2 (11) = 29.07, p = .02. Finally, female students reported using internet social media sources x2 (1) = 18.93, p < .001, and friends x2 (1) = 4.55, p = .03, more than males to find physical activity information. These findings suggest overall, university students may prefer learning about physical activity from websites and social media rather than health professionals. Future research may wish to consider how these preferences for information seeking and reasons why students seek information, affect participation in physical activity.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".