Explaining adolescent exercise behavior change: A longitudinal application of the transtheoretical model
Bibliographic record
Abstract
The developmental decline and benefits of exercise are documented, however, relatively little is known about the mechanisms and motivations underlying adolescent exercise behavior This project investigates which variables drive exercise or are a consequence thereof, within the Transtheoretical Model (TTM). Baseline questionnaires (N = 819) were collected through 5 Canadian high schools. For this longitudinal investigation, all baseline participants were approached for a 3-year follow up. Follow-up questionnaire completers (n = 400: mean baseline age = 14.89, SD = 1.15, mean follow-up age = 17.62 years, SD = 1.18) were not different from noncompleters (n = 419) on all baseline variables, except for sex (54. 75% and 43. 68% females, respectively; p <. 003). Stages, processes, self-efficacy, pros and cons of exercise from the TTM, and self-reported exercise were assessed. Panel analyses revealed that although the directions of the relations were as hypothesized, the processes did not significantly lead to exercise or vice versa. As hypothesized, exercise leads to self-efficacy and pros and cons, showing that the TTM can serve as a framework to understand adolescent exercise behavior Future research needs to incorporate shorter assessment intervals and use larger samples to be able to look at adjacent stage transitions.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".