Effects of a Print-mediated Intervention on Physical Activity during Transition to the First Year of University
Bibliographic record
Abstract
Transition to the first year of university is linked to steep declines in moderate-vigorous physical activity (MVPA). The purpose of this study was to investigate the effects of a targeted, theory-driven, print-based intervention on MVPA during transition to university. Volunteer participants from five Canadian universities (n=255) completed measures of MVPA at the start of their first semester at university and were randomly assigned to conditions receiving a first-year-student physical activity and action-planning brochure, Canada's Physical Activity Guide (CPAG), or a no-intervention control group. Six weeks later, a follow-up measure of MVPA was obtained as well as retrospective accounts of physical activity action-planning strategies and self-efficacy for scheduling physical activity. At the follow-up, students who received the targeted first-year student physical activity brochure reported significantly higher levels of MVPA compared to controls (p<.05) and a trend towards higher MVPA compared to the CPAG group (p=.06). However, there were no differences between groups on action planning or self-efficacy. A theory-driven and targeted print media intervention can offer low-cost and broad-reaching effects that may help students stay more active or curb declining levels of MVPA that occur during transition to university.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".