Exercise adherence in breast cancer survivors training for a dragon boat race competition: a preliminary investigation
Bibliographic record
Abstract
Recent research has applied the theory of planned behavior (TPB) to understanding exercise after a cancer diagnosis, but studies are few and have been limited by retrospective designs, self-report measures of exercise and varied results. In the present study, we extended this research by using a prospective design and an objective measure of exercise adherence. Participants were a convenience sample of 24 breast cancer survivors attending a twice weekly, 12-week training program in preparation for a dragon boat race competition. Participants completed a baseline questionnaire that assessed demographic and medical variables, past exercise, and the TPB (i.e. beliefs, subjective norm, attitude, perceived behavioral control and intention). Program attendance was monitored over a 12-week period by the class instructor. Overall, participants attended 66% of the training sessions. Multiple regression analyses indicated that: (a) intention was the sole determinant of program attendance and explained 35% of the variance; (b) the TPB constructs explained 49% of the variance in intention with subjective norm being the most important determinant; and (c) the key underlying beliefs were support from physician, spouse, and friends, and confidence in being able to attend the training class when having limited time, no one to exercise with, fatigue, and other health problems. Based on this preliminary study, it was concluded that the TPB may provide a good framework on which to base interventions designed to promote exercise in breast 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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".