A Pediatric- and Adolescent-Focused Medication Abortion Curriculum for Multidisciplinary Trainees
Bibliographic record
Abstract
Introduction: Post-Roe v. Wade, 22 states now ban or heavily restrict abortion, decreasing safe and timely access for adolescents and young adults (AYAs) and limiting the number of abortion providers being trained.Further, no AYA-focused medication abortion (MAB) curriculum exists.To fill this gap, we developed an AYA-focused MAB curriculum for pediatric trainees.Methods: Using Kern's Six Steps, we designed a blended curriculum of online modules (30-40 minutes) and a workshop (120 minutes).Using pre/post surveys, we assessed differences in multiple-choice knowledge questions and Likert scales evaluating values and intentions; open-ended responses were analyzed using directed qualitative content analysis.Results: Nine workshops were held over the 2023-2024 academic year, with a total of 52 learners completing the curriculum.Pre/post data are available from 29 learners, including 15 pediatric residents, 13 adolescent-focused nurse practitioner students, and one medical student.Learners demonstrated a significant increase in knowledge score after curriculum completion (60% vs. 90%; p < .01).Intentions to provide, refer, and advocate for MAB care did not change significantly (average of three questions on a 5-point Likert scale: 4.3 vs. 4.3; p = .92).Eighty-five percent of learners rated the overall curriculum as excellent or outstanding.Major themes included appreciating the opportunity to explore and anticipate challenging cases and finding case-based learning and role-play helpful.Discussion: Our curriculum improved trainees' knowledge of MAB provision for AYAs, instilled confidence, and helped learners anticipate challenging abortion cases.The generalizability of skills learned may vary by local political climate, legal restrictions, and/or institutional support.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".