Perceived autonomy support from healthcare professionals and physical activity among breast cancer survivors: A propensity score analysis
Bibliographic record
Abstract
The majority of women treated for breast cancer are physically inactive although physical activity (PA) could attenuate many adverse effects of cancer and treatment. Autonomy support from healthcare professionals may improve PA initiation, adherence and maintenance. This study aimed to determine, using a causal inference approach, whether or not perceived autonomy support (PAS) from healthcare professionals is associated with light, moderate, and vigorous intensity PA among women treated for breast cancer. Data were drawn from the longitudinal study "Life After Breast Cancer: Moving On" (n = 199). PAS was measured with the Health Care Climate Questionnaire and PA was assessed using GT3X triaxial accelerometers. Associations between PAS and PA were estimated with linear regressions and adjusted estimations were obtained using propensity score-based inverse probability of treatment weights (IPTW). Results reveal no association between PAS and PA of light ([Formula: see text](95%CI) = -0.09 (-0.68, 0.49)), moderate ([Formula: see text] (95%CI) = -0.03 (-0.17, 0.11)), or vigorous ([Formula: see text](95%CI) = 0.00 (-0.03, 0.02)) intensity. Different forms of engagement and support by healthcare professionals should be explored to identify the best intervention targets to encourage women to adopt and maintain regular PA in the cancer continuum.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".