Women’s Preferences for Body Image Programming: A Qualitative Study to Inform Future Programs Targeting Women Diagnosed With Breast Cancer
Bibliographic record
Abstract
Purpose: This paper describes women’s opinions of the attributes of the ideal body image program to inform the design, development, and implementation of future programs for those diagnosed with breast cancer. Methods: Deductive-inductive content analysis of semi-structured interviews with 26 women diagnosed with breast cancer (mean age = 55.96 years; mean time since diagnosis = 2.79 years) was performed. Findings: Participants’ opinions regarding the ideal body image program are summarized into five themes, mapping the where (community-based, hospital-based, or online), when (across the cancer continuum or at specific points), how (peer-led programs, professional help, events, presentations/workshops, resources, support groups), what (self-care, counseling and education for one self, education for others, support for addressing sexuality/sexual health concerns, and concealing treatment-related changes), and who (team approach or delivered by women, health professionals, make-up artists). Conclusion: This study provides useful data on what women believe are the attributes of the ideal body image program, which can contribute to efforts aimed at developing and delivering body image programs for women diagnosed with breast cancer that prioritize their needs and preferences.
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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 | 0.013 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".