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Record W3034308358 · doi:10.2196/17742

Improving Adherence to Adjuvant Hormonal Therapy Among Disadvantaged Women Diagnosed with Breast Cancer in South Carolina: Proposal for a Multimethod Study

2020· article· en· W3034308358 on OpenAlexvenueno aff
Tisha M. Felder, Sue P. Heiney, James R. Hébert, Daniela B. Friedman, Ronit Elk, Regina Franco, Lucy Gansauer, Barbara L. Christensen, Marvella E. Ford

Bibliographic record

VenueJMIR Research Protocols · 2020
Typearticle
Languageen
FieldMedicine
TopicMedication Adherence and Compliance
Canadian institutionsnot available
FundersNational Cancer InstituteHollings Cancer Center, Medical University of South CarolinaNational Institutes of HealthUniversity of South Carolina
KeywordsMedicineBreast cancerHormonal therapyPsychological interventionAdjuvantHormone therapyDisadvantagedAdjuvant therapyOncologyCancerFamily medicineInternal medicineGynecologyPsychiatry

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.093
metaresearch head score (Gemma)0.085
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Protocol · Consensus signal: none
Teacher disagreement score0.093
Threshold uncertainty score0.492

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0930.085
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.004
Bibliometrics0.0040.005
Science and technology studies0.0080.002
Scholarly communication0.0040.004
Open science0.0040.009
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.0040.000

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.

Opus teacher head0.233
GPT teacher head0.534
Teacher spread0.300 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreProtocol

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".

Quick stats

Citations6
Published2020
Admission routes1
Has abstractyes

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