Developing and evaluating a patient decision aid for hormone therapy to manage symptoms of surgical menopause: the story behind the “SheEmpowers” patient decision aid
Bibliographic record
Abstract
OBJECTIVES: To develop and evaluate an evidence-based patient decision aid (PDA) that can support women making decisions on hormone therapy (HT) for the management of early surgical menopause. METHODS: The PDA development was guided by the Ottawa Decision Support Framework and the International Patient Decision Aid Standards and involved three phases: an exploratory phase to identify women's decisional needs; a development phase to identify evidence related to treatment options and draft initial prototype; and an evaluation phase to evaluate the prototype and elicit views on acceptability in women (N = 12). All phases were driven by a multidisciplinary group of researchers, clinicians, and patient stakeholders to ensure women's priorities were met. RESULTS: A prototype PDA was drafted based on needs identified from the exploratory phase. The PDA has five domains: information on surgical menopause and HT; HT outcome probabilities; patient stories; values clarification; and guidance in deliberation. Participants in the evaluation phase perceived the tool as acceptable and offered suggestions for modifications. CONCLUSION: Through our adopted, systematic approach the SheEmpowers PDA was developed to help women overcome deterrents to decision-making related to lack of knowledge, decision-making skills, and involvement in therapy decisions. The decisional effectiveness of the tool is to be assessed in future studies.
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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 | 0.079 | 0.128 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".