Correlates of Physical Activity Participation among Individuals Diagnosed with Cancer: An Application of the Multi-Process Action Control Framework
Bibliographic record
Abstract
Background: The purpose of this study was to test Multi-Process Action Control (M-PAC) processes as correlates of physical activity (PA) intention formation and translation (i.e., action control) in individuals diagnosed with cancer. Methods: This study was a cross-sectional survey, completed from July to November of 2020 during the COVID-19 pandemic. PA and M-PAC processes were self-reported using the Godin Leisure-Time Exercise Questionnaire and questionnaires for reflective (instrumental/affective attitudes, perceived opportunity/capability), regulatory (e.g., goal-setting, planning), and reflexive processes (habit, identity). Separate hierarchical multinomial logistic regression models determined correlates of intention formation and action control. Results: Participants (n = 347; Mage= 48.2 ± 15.6) were primarily diagnosed with breast cancer (27.4%) and at a localized stage (85.0%). Most participants intended to perform PA (70.9%), yet only 50.4% met guidelines. Affective judgements (p < 0.001) and perceived capability (p < 0.01) were significantly associated with intention formation. Preliminary models indicated employment, affective judgements, perceived capability, and self-regulation to be significant (ps < 0.05) correlates of action control, but in the final model, only surgical treatment (p = 0.02) and PA identity (p < 0.001) were significantly associated with action control. Conclusion: Reflective processes were associated with PA intention formation, while reflexive processes were associated with PA action control. Behavior change efforts for individuals diagnosed with cancer should extend beyond social-cognitive approaches to include regulatory and reflexive processes of PA behavior (i.e., PA identity).
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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 | 0.008 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".