Fertility Preservation for Gender Diverse Youth: Design and Evaluation of Patient-Centered eLearning Modules
Bibliographic record
Abstract
Purpose: We examined transgender and gender diverse (TGD) youths' learning needs regarding fertility preservation (FP). We developed patient-centered eLearning modules for TGD youth with ovaries or testes. We evaluated the modules' impact on knowledge and confidence in decision-making and youths' perceptions of the modules. Methods: Three-phase study (2022-2024) that involved pubertal 10-18-year-old TGD youth from a gender clinic. Phase 1 utilized a needs assessment (TGD youth), semistructured interviews (interprofessional gender care and fertility medicine providers), and community partner and patient-education expert input. In phase 2, eLearning modules were designed and piloted with youth and health care providers. Feedback (content, language, and length) informed revisions. Phase 3 involved completion of pre- and post-module questionnaires (20 TGD youth). Outcomes included youths' knowledge, confidence in decision-making regarding FP consultation, and module perceptions. Results: Phase 1: Of 21 youth (13 assigned female at birth [AFAB], 8 assigned male at birth [AMAB]), learning preferences were reading text (15/21), additional resources (14/21), photographs (7/21), and interactivity (6/21). Phase 2: Of 13 participants (7 youth, 6 providers), 12 reported content being clear. All reported acceptable and inclusive module language. Phase 3: 20 participants (14 AFAB and 6 AMAB) with mean age 15.6 ± 1.0 years. All felt more knowledgeable about FP and 16/20 reported greater decision-making confidence. Modules were reported as engaging/interactive (18/20), informative (18/20), with high overall satisfaction (19/20), and no negative responses. Conclusion: These eLearning tools addressed the lack of patient-centered education resources regarding FP for TGD youth and improved knowledge and confidence in decision-making. Dissemination of modules may promote education and improve FP-related decision-making.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".