Informing interventions to improve uptake of adjuvant endocrine therapy in women with breast cancer: a theoretical-based examination of modifiable influences on non-adherence
Bibliographic record
Abstract
Abstract Purpose To inform intervention development, we measured the modifiable determinants of endocrine therapy (ET) non-adherence in women with breast cancer, using the Theoretical Domains Framework (TDF) and examined inter-relationships between these determinants and non-adherence using the Perceptions and Practicalities Approach (PAPA). Methods Women with stages I–III breast cancer prescribed ET were identified from the National Cancer Registry Ireland (N = 2423) and invited to complete a questionnaire. A theoretically based model of non-adherence was developed using PAPA to examine inter-relationships between the 14 TDF domains of behaviour change and self-reported non-adherence. Structural equation modelling (SEM) was used to test the model. Results A total of 1606 women participated (response rate = 66%) of whom 395 (25%) were non-adherent. The final SEM with three mediating latent variables (LVs) (PAPA Perceptions: TDF domains, Beliefs about Capabilities,Beliefs about Consequences; PAPA Practicalities: TDF domain, Memory, Attention,Decision Processes and Environment) and four independent LVs (PAPA Perceptions: Illness intrusiveness; PAPA Practicalities: TDF domains, Knowledge,Behaviour Regulation; PAPA External Factors: TDF domain, Social Identity) explained 59% of the variance in non-adherence and had an acceptable fit (χ2(334) = 1002, p < 0.001; RMSEA = 0.03; CFI = 0.96 and SRMR = 0.07) Knowledge had a significant mediating effect on non-adherence through Beliefs about Consequences and Beliefs about Capabilities. Illness intrusiveness had a significant mediating effect on non-adherence through Beliefs about Consequences. Beliefsabout Consequences had a significant mediating effect on non-adherence through Memory, Attention, Decision Processesg and Environment. Conclusions By underpinning future interventions, this model has the potential to improve ET adherence and, hence, reduce recurrence and improve survival in breast cancer.
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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 | 0.011 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".