Understanding action control: Predicting physical activity intention-behavior profiles across 6 months in a Canadian sample.
Bibliographic record
Abstract
Intention is theorized as the proximal determinant of behavior in some theories of motivation, but the need to understand predictors of action control (i.e., translating an intention into behavior) is warranted to tailor physical activity intervention efforts. The purpose of this study was to examine constructs of the transtheoretical model of behavior change (TTM) as predictors of physical activity intention-behavior profiles across 6 months in a large Canadian sample (N = 1,192). Results showed that 5 of the 8 possible intention-behavior profiles had a substantial number of participants: nonintenders, unsuccessful adopters, successful adopters, unsuccessful maintainers, and successful maintainers. Constructs of the TTM distinguished (p < .01) intention-behavior profiles. Self-efficacy and the behavioral processes of change were particularly good predictors of action control (p < .01), but disaggregated beliefs and processes identified specific intervention targets for successful physical activity adoption and maintenance. The results validate that both action planning and action control are important when understanding physical activity behavior.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".