"It has to be more than exercise": Exploring optimal physical activity program delivery for breast cancer survivors across multiple stakeholder groups in cancer care
Bibliographic record
Abstract
Physical activity (PA) is an effective strategy for mitigating the negative physical and psychosocial effects of breast cancer treatment. Accordingly, PA programs have been developed to enhance clinical cancer care; however, many communities in Ontario remain without such programs. Further, it is unclear what constitutes an optimal PA program for breast cancer survivors. An integrated knowledge translation approach, defined as the involvement of knowledge users in the co-creation of knowledge, can assist in identifying optimal aspects of PA program delivery for breast cancer survivors. Consequently, the purpose of this study was to explore ideal PA program delivery for breast cancer survivors across multiple stakeholder groups in the cancer care community. Breast cancer survivors, health care professionals (e.g., oncologists, nurses, or allied health care professionals) and community-based PA program providers participated in four 60-minute heterogeneous focus group discussions. Broad discussions about the ideal program environment, program delivery team, and core program practices were encouraged. Focus groups were audio recorded, transcribed verbatim, and subjected to thematic analysis. Participants recommended that breast cancer survivors in clinical PA programs should have opportunities to experience wellness, a collective identity, cancer literacy, and self-efficacy. When possible, programs should be delivered in the community, accessible from an environmental and financial perspective, and integrated within the regional network of cancer care. These findings can be extended to provide a foundation for researchers and practitioners aiming to establish, deliver, and evaluate optimal PA interventions and programming for breast cancer survivors.Acknowledgments: The Canadian Breast Cancer Foundation
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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 | 0.012 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 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".