Predicting physical activity and outcome expectations in cancer survivors: an application of Self‐Determination Theory
Bibliographic record
Abstract
The purpose of this study was to examine the contributions of autonomous and controlled motives drawn from Self-Determination Theory (SDT; Intrinsic Motivation and Self-determination in Human Behavior. Plenum Press: New York, 1985; Handbook of Self-determination Research. University of Rochester Press: New York, 2002) towards predicting physical activity behaviours and outcome expectations in adult cancer survivors. Participants were cancer-survivors (N=220) and a non-cancer comparison cohort (N=220) who completed an adapted version of the Treatment Self-Regulation Questionnaire modified for physical activity behaviour (TSRQ-PA), an assessment of the number of minutes engaged in moderate-to-vigorous physical activity (MVPA) weekly, and the anticipated outcomes expected from regular physical activity (OE). Simultaneous multiple regression analyses indicated that autonomous motives was the dominant predictor of OEs across both cancer and non-cancer cohorts (R(2adj)=0.29-0.43), while MVPA was predicted by autonomous (beta's ranged from 0.21 to 0.34) and controlled (beta's ranged from -0.04 to -0.23) motives after controlling for demographic considerations. Cancer status (cancer versus no cancer) did not moderate the motivation-physical activity relationship. Collectively, these findings suggest that the distinction between autonomous and controlled motives is useful and compliments a growing body of evidence supporting SDT as a framework for understanding motivational processes in physical activity contexts with cancer survivors.
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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 | 0.005 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".