Clinician-Reported Barriers to Implementing Breast Cancer Chemoprevention in the UK: A Qualitative Investigation
Bibliographic record
Abstract
AIMS: The use of tamoxifen and raloxifene as preventive therapy for women at increased risk of breast cancer was approved by the National Institute for Health and Care Excellence (NICE) in 2013. We undertook a qualitative investigation to investigate the factors affecting the implementation of preventive therapy within the UK. METHODS: We recruited general practitioners (GPs) (n = 10) and clinicians working in family history or clinical genetics settings (FHCG clinicians) (n = 15) to participate in semi-structured interviews. Data were coded thematically within the Consolidated Framework for Implementation Research. RESULTS: FHCG clinicians focussed on the perceived lack of benefit of preventive therapy and difficulties interpreting the NICE guidelines. FHCG clinicians felt poorly informed about preventive therapy, and this discouraged patient discussions on the topic. GPs were unfamiliar with the concept of preventive therapy, and were not aware that they may be asked to prescribe it for high-risk women. GPs were reluctant to initiate therapy because it is not licensed, but were willing to continue a prescription if it had been started in secondary or tertiary care. CONCLUSIONS: Barriers to implementing preventive therapy within routine clinical practice are common and could be addressed by engaging all stakeholders during the development of policy documents.
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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 | 0.028 | 0.068 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.008 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.003 |
| 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".