What factors are associated with a woman's decision to take hormone replacement therapy? Evaluated in the context of a decision aid
Bibliographic record
Abstract
OBJECTIVES: To understand the factors associated with a post-menopausal woman deciding to take hormone replacement therapy (HRT) after reviewing a decision aid (DA) and having a counselling visit with her physician as well as the factors associated with the act of taking HRT 2 months after the counselling interview. DESIGN: A secondary analysis of data collected for a randomized controlled trial evaluating two DAs. MAIN OUTCOME RESULTS: Although 28% of women were uncertain regarding their decision after the counselling interview, only 2.4% of women, at the assessment at 2 months, had not made a decision. The most significant factor associated with the decision to take HRT, after the physician visit, was the physician preference (OR: 62, 95% CI: 13.3, 289.7). Physician preference (OR: 78, 95% CI: 6.2, 975) remained the most significant factor for taking HRT 2 months after the counselling interview followed by low uncertainty about the decision (OR: 0.4, 95% CI: 0.2, 0.7). CONCLUSION: Physician preference was the factor that was most associated with the woman's decision following counselling and 2 months later. Qualitative evaluation of the interview process involving the patient and physician would determine whether the patient and physician are reaching a shared decision or is the physician preference influencing the patient.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".