Barriers Identification as Intervention to Engage Breast Cancer Survivors in Physical Activity
Bibliographic record
Abstract
This study was designed to demonstrate the advantage of adding cancer barriers to components of decision-making in the transtheoretical model (TTM). In study 1, questionnaires were completed by 139 breast cancer survivors including decisional balance, cancer-related barriers and stages of readiness. In study 2, efficiency of directly tackling cancer-related barriers through motivational-style conversation was tested in a quasi-experimental design. From study 1, all decisionmaking variables were related to stages of readiness, but cancer-related barriers were the sole predictors of engagement in physical activity. Out of the three groups of study 2, only the group with motivational-style conversation displayed a significant progress for engagement in physical activity. Demonstrating that cancer-related barriers predict stage of change above the effects of the two components of decisional balance provides a validation of positions that put cancer-related barriers as uniquely related to stages of change, and suggests that adding them in decision making variables in TTM’s model can provide a genuinely new contribution to the understanding of physical activity adherence. Regarding implication for cancer survivors, these results suggest that in order to stimulate progress in early stages of change, a greater emphasis may be needed on reducing cancer-related barriers.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".