The benefits of being self‐determined in promoting physical activity and affective well‐being among women recently treated for breast cancer
Bibliographic record
Abstract
PURPOSE: In this study, changes in motivational regulations in women following treatment for breast cancer were described. Changes in motivational regulations as predictors of subsequent change in light and moderate-to-vigorous physical activity (PA) and affect were also examined. METHODS: Women [n = 150; M(age) = 54.41 (SD = 10.87) years] completed self-report questionnaires and wore an accelerometer for 7 days at Time 1 [M = 3.94 (SD = 3.08) months following primary treatment], as well as 3 (Time 2) and 6 (Time 3) months later. Data were analyzed using repeated-measures analysis of variance and path analysis using residual change scores. RESULTS: Identified regulation and self-determined motivation (i.e., combined intrinsic motivation and identified regulation) scores decreased over time (p < 0.05). In the path model [χ(2)(4) = 5.66, p = 0.22, root mean square error of approximation = 0.05 (90% CI: 0.0; 0.15), comparative fit index = 0.99, standardized root mean square of the residuals = 0.03], ΔTime(1-2) in external regulation was associated with ΔTime(2-3) in positive affect (β = -0.16), ΔTime(1-2) in introjected (β = 0.25) and amotivation (β = 0.19) were related to ΔTime(2-3) in negative affect, and ΔTime(1-2) in self-determined motivation was related to ΔTime(2-3) in positive affect (β = 0.40) and moderate-to-vigorous PA (β = 0.21). CONCLUSIONS: Changes in motivational regulations were related to changes in PA and affect in the aftermath of breast cancer. Given the benefits of self-determined motivation, additional research is needed to develop and test interventions aimed at enhancing this type of motivation.
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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 | 0.001 | 0.003 |
| 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 | 0.000 |
| 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".