Factors associated with the difference in score between women's and doctors' decisional conflict about hormone therapy: a multilevel regression analysis
Bibliographic record
Abstract
OBJECTIVE: To explore factors associated with the difference in score between women's and doctors' decisional conflict about hormone therapy (HT). DESIGN: Secondary analysis. SETTING AND PARTICIPANTS: family doctors were randomized to prepare women for counselling about HT using either a decision aid or a pamphlet. MAIN VARIABLES STUDIED: After each counselling session, decisional conflict was assessed in women and doctors using the Decisional Conflict Scale (DCS) and the Provider Decision Process Assessment Instrument (PDPAI), respectively. The difference in score between the DCS and PDPAI was computed and entered as the dependent variable in a multilevel regression analysis. MAIN OUTCOME RESULTS: A total of 40 doctors and 167 women were included in the analysis. The intra-doctor correlation coefficient was 0.25. Factors associated with women experiencing higher decisional conflict than their doctor were: age of doctor >45 years, women who were undecided about the best choice after the counselling session, women with a university degree and women who said that their doctor usually does not give them control over treatment decision. Factors associated with doctors experiencing more decisional conflict than women were: doctors who were undecided about the quality of the decision, length of visit <30 min and women who thought that the decision was shared with their doctor. CONCLUSION: In order to reduce the disparities between women's and doctors' decisional conflict about HT, interventions aimed at raising awareness of doctors about shared decision-making should be encouraged.
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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 | 0.008 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.004 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".