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 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 | 0.009 | 0.095 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".