My Active Health Retreat: A Qualitative Study on the Experiences and Perspectives of Breast Cancer Survivors
Bibliographic record
Abstract
Introduction:From the time of diagnosis, breast cancer survivors' physical activity (PA) tends to decline, which is accentuated during treatment because of side effects such as fatigue, depression, decreased muscle mass, and treatment toxicity. The My Active Health Retreat has been created to provide community-based support for breast cancer survivors to engage in a healthy lifestyle and educate about health matters. This qualitative study explores participants' perspectives and past, present, and future experiences regarding the workshops offered during the My Active Health retreat. Materials and Methods:My Active Health retreat has been designed to provide strategies to engage in a physically active lifestyle. Participants who consented to be part of this qualitative study filled out a questionnaire to provide their experiences and perspectives of the retreat. Themes were generated to help understand the participants' views about their experiences with the workshop's content in the retreat. Results:The participants were 21 women (mean age of 54.5 years) who filled out the post-retreat questionnaire. Workshops on yoga, meditation, exercise, and walking were highly praised for reducing stress and anxiety and promoting mindfulness. The importance of social contact in alleviating survivors' feelings of isolation was emphasized. The participants expressed their intention to incorporate the knowledge from the retreat into their daily lives. Conclusions:This study provides significant insights that can guide the development of content in health retreats focused on PA for 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 | 0.011 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.010 | 0.008 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".