Improving Adherence to Adjuvant Hormonal Therapy Among Disadvantaged Women Diagnosed with Breast Cancer in South Carolina: Proposal for a Multimethod Study
Bibliographic record
Abstract
BACKGROUND: Current clinical guidelines recommend that hormone receptor-positive breast cancer survivors take adjuvant hormonal therapy (AHT) for 5 to 10 years, following the end of definitive treatment. However, fewer than half of patients adhere to the guidelines, and suboptimal adherence to AHT is associated with an increased risk of breast cancer mortality. Research has extensively documented sociodemographic and disease-specific factors associated with adherence to AHT, but very little evidence exists on behavioral factors (eg, knowledge, patient-provider communication) that can be modified and targeted by interventions. OBJECTIVE: The goal of this study is to develop and test a theory-based, multilevel intervention to improve adherence to AHT among breast cancer survivors from racially and socioeconomically disadvantaged backgrounds (eg, Medicaid-insured). The specific aims are to (1) explore multilevel (eg, patient, health care system) factors that influence adherence to AHT; (2) develop a theory-based, multilevel intervention to improve adherence to AHT; and (3) pilot test and evaluate the intervention developed in Aim 2. METHODS: For Aim 1, we will recruit breast cancer survivors and health care professionals to participate in semistructured interviews to gain their perspectives about barriers and facilitators to AHT use. We will conduct a directed content analysis of the Aim 1 qualitative interview data. For Aim 2, we will integrate Aim 1 findings and current literature into the design of a multilevel intervention using an Intervention Mapping approach. For Aim 3, we will recruit Medicaid-insured breast cancer survivors to assess the feasibility of the pilot intervention. RESULTS: From May 2016 to July 2018, we completed interviews with 19 breast cancer survivors and 23 health care professionals in South Carolina. We will conduct a directed content analysis of the qualitative interview data. Results from this analysis will be used, in combination with current literature, to design (Aim 2) and pilot test a theory-based multilevel intervention (Aim 3) in Summer 2021. Results of the pilot are expected for Fall 2021. CONCLUSIONS: This study will provide a deeper understanding of how to improve adherence to AHT, using a novel and multilevel approach, among socioeconomically disadvantaged breast cancer survivors who often experience disproportionate breast cancer mortality. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/17742.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".