Development of My Decision/Mi Decisión, a web-based decision aid to support permanent contraception decision making
Bibliographic record
Abstract
Objective: To develop a patient-centered, web-based decision aid to support informed and value-concordant decision making among Medicaid enrollees considering tubal sterilization. Methods: We used the Ottawa Decision Support Framework and the International Patient Decision Aids Standards (IPDAS) to guide systematic development of our decision aid. We interviewed 15 obstetrician-gynecologists and 40 women, who had considered or were considering tubal sterilization. A Steering Committee-comprising healthcare providers, social scientists, reproductive health and justice advocates, and people with lived experience-provided feedback and direction. We developed English and Spanish prototypes, which were beta tested with 24 women. Results: tool (English/Spanish) includes written and video information about tubal sterilization procedures; an interactive table of contraception options; values clarification exercises; reflection and deliberation; knowledge checks; and a summary report to share with one's provider. Users found the decision aid to be informative, engaging, easy to use, and helpful in informing contraception decision making. Conclusion: is a scalable tool that could be implemented widely to support informed decision making about tubal sterilization. Innovation: . While designed for Medicaid enrollees, further investigation will explore more generalized use.
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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 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.021 | 0.004 |
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".