Healthcare Provider Perspectives on Adherence to Adjuvant Endocrine Therapy after Breast Cancer
Bibliographic record
Abstract
Adherence to adjuvant endocrine therapy (AET) for breast cancer is suboptimal. The purpose of this study was to: (1) explore the experiences and perspectives of healthcare providers (HCPs) in providing care to breast cancer survivors prescribed AET, (2) identify how social and structural factors influence the provision of AET-related care, and (3) ascertain HCP recommendations for optimizing AET adherence and related care. Individual, in-depth interviews were conducted with 14 HCPs using an interpretive descriptive approach to inquiry and the theoretical lens of relational autonomy. Data was analyzed using thematic and constant comparative techniques. Healthcare providers focused on four main components of AET-related care: (1) the importance of having careful conversations about AET, (2) difficulties in navigating transitions in care, (3) symptom management as a big part of their role, and (4) dealing with AET discontinuation. Recommendations to improve AET adherence focused on developing sustainable and efficient models of delivering high-quality care to women on AET. Healthcare providers play a pivotal role educating women about AET and supporting their adherence to therapy. Sustainable healthcare system innovations and new models of care that address current system gaps are needed to enhance survivorship care, AET adherence, and ultimately, reduce cancer recurrence and mortality.
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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 | 0.006 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| 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".