Development of a user-informed decision aid for adolescents and young adults’ contraceptive care (Preprint)
Bibliographic record
Abstract
BACKGROUND Adolescents and young adults seeking contraceptive care face many considerations due to differences in contraceptive indications, knowledge levels, and non-contraceptive benefits, which are often overlooked and can lead to contraceptive non-adherence or non-use and adverse health outcomes. OBJECTIVE Develop a digital decision-aid tool that meets the contraceptive decisional needs of adolescents and young adults from diverse backgrounds. METHODS We developed a web-based decision aid using a user-centered design framework by Elwyn et al. and the International Patient Decision Aid Standards. The design and development process were informed by a literature review, consultations with scientific experts, health informatics specialists, clinicians experienced in adolescent reproductive health, and a diverse group of adolescent and young adult advisors. We gathered feedback on the decision aid's content and functionality from clinicians, adolescents, and young adult stakeholders during focus group interviews conducted across two user testing cycles. Their feedback was recorded, transcribed, and analyzed to refine the content and improve the decision aid's functionality. RESULTS Twenty-four clinicians, adolescents, and young adult participants from diverse backgrounds shared their perspectives on the decision aid's content, relevance, design, and usability over two user testing cycles from February 2023 to June 2024. The decision aid features a survey with a decision algorithm that provides contraceptive method recommendations based on user preferences and health history, as well as infographics and a provider summary view. CONCLUSIONS This approach resulted in a functional decision aid prototype, MyPlanMyChoice©, which is now ready for pilot and feasibility evaluation in a clinical setting.
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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 | 0.018 | 0.053 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.021 | 0.005 |
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".