Perspectives of Women with Breast Cancer and Healthcare Providers Participating in an Adherence-Enhancing Program for Adjuvant Endocrine Therapy: A Qualitative Study
Bibliographic record
Abstract
BACKGROUND: Adjuvant endocrine therapy (AET) is prescribed for 5-10 years to women with hormone-sensitive breast cancer to prevent recurrence. However, a significant proportion of women do not adhere to AET. We developed SOIE, a one-year program designed to enhance the AET experience and adherence. SOIE was pilot-tested in a mixed-methods randomized controlled trial. This report presents the experience of women and healthcare providers (HCPs) with SOIE. METHODS: A descriptive qualitative study using semi-structured interviews and thematic analysis was conducted with 20 women and 7 HCPs who participated in the program. RESULTS: Most women and HCPs reported high satisfaction with the program. Women felt it addressed their need for information and strategies to manage side effects. They felt supported and developed a more positive attitude toward AET, which contributed to their intention to pursue AET. They perceived that the program helped them navigate the AET experience and reduced their stress or fear regarding AET. HCPs corroborated these benefits. CONCLUSIONS: Findings suggest that SOIE can enhance the experience and motivation to pursue the AET treatment by meeting important needs for information, side-effects management, and psycho-emotional support. Programs like SOIE can have benefits beyond adherence by improving patients' well-being during this crucial long-term treatment.
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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 | 0.008 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 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".