Understanding action control: Predicting physical activity intention-behavior profiles across 6 months in a Canadian sample.
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it