Development and evaluation of a breast cancer prevention decision aid for higher‐risk women
Bibliographic record
Abstract
OBJECTIVE: To develop and evaluate the effectiveness of a breast cancer prevention decision aid for women aged 50 and older at higher risk of breast cancer. DESIGN: Pre-test-post-test study using decision aid alone and in combination with counselling. SETTING: Breast Cancer Risk Assessment Clinic. PARTICIPANTS: Twenty-seven women aged 50-69 with 1.66% or higher 5-year risk of breast cancer. INTERVENTION: Self-administered breast cancer prevention decision aid. MAIN OUTCOME MEASURES: Acceptability; decisional conflict; knowledge; realistic expectations; choice predisposition; intention to improve life-style practices; psychological distress; and satisfaction with preparation for consultation. RESULTS: The decision aid alone, or in combination with counselling, decreased some dimensions of decisional conflict, increased knowledge (P < 0.01), and created more realistic expectations (P < 0.01). The aid in combination with counselling, significantly reduced decisional conflict (P < 0.01) and psychological distress (P < 0.02), helped the uncertain become certain (P < 0.02), and increased intentions to adopt healthier life-style practices (P < 0.03). Women rated the aid as acceptable, and both women and practitioners were satisfied with the effect it had on the counselling session. CONCLUSION: The decision aid shows promise as a useful decision support tool. Further research should compare the effect of the decision aid in combination with counselling to counselling alone.
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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 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".