Active transition: A pilot study of a website-delivered physical activity intervention for university students
Bibliographic record
Abstract
Transition into university has been associated with dramatic declines in Moderate-Vigorous Physical Activity (MVPA; Kwan & Faulkner, 2010). While research has begun to uncover some of the reasons behind the decline, few attempts have addressed the population-specific perturbations in social and environmental conditions. Thus, Active Transition was developed; a theoretically informed website-delivered physical activity intervention. The current study piloted the 6-week intervention, examining its feasibility and impact on PA cognitions and behaviours. Sixty-five residence students ( n = 44 females) were assigned to either an intervention ( n = 38) or comparison ( n = 27) group. Over the intervention, both MVPA and PA cognitions declined significantly across both conditions. No significant differences in MVPA declines emerged, but the intervention condition and intervention users engaged in 60 more minutes of weekly MVPA. Repeated measures ANOVAs, found significant interactions between intervention conditions and intentions ( F (1,61)= 6.91, p F (1,61)= 3.73, p = .06); and intervention usage and perceived control ( F (1,61)= 5.13, p Acknowledgments: Social Sciences and Humanities Research Council of Canada
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 | 0.003 | 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 | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".