Trainee Perspectives Regarding the Effect of the <i>Dobbs v. Jackson Women’s Health Organization</i> Supreme Court Decision on Obstetrics and Gynecology Training
Bibliographic record
Abstract
Objectives: We aimed to describe obstetrics and gynecology (OBGYN) trainees’ anticipation of how the Dobbs v. Jackson Women’s Health Organization (Dobbs) U.S. Supreme Court decision may affect their training. Methods: A REDCap survey of OBGYN residents and fellows in the United States from September 19, 2022, to December 1, 2022, queried trainees’ anticipated achievement of relevant Accreditation Council for Graduate Medical Education (ACGME) training milestones, their concerns about the ability to provide care and concern about legal repercussions during training, and the importance of OBGYN competence in managing certain clinical situations for residency graduates. The primary outcome was an ACGME program trainee feeling uncertain or unable to obtain the highest level queried for a relevant ACGME milestone, including experiencing 20 abortion procedures in residency. Results: We received 469 eligible responses; the primary outcome was endorsed by 157 respondents (33.5%). After correction for confounders, significant predictors of the primary outcome were state environment (aOR = 3.94 for pending abortion restrictions; aOR = 2.71 for current abortion restrictions), trainee type (aOR = 0.21 for fellow vs. resident), and a present or past Ryan Training Program in residency (aOR = 0.55). Although the vast majority of trainees believed managing relevant clinical situations are key to OBGYN competence, 10%–30% of trainees believed they would have to stop providing the standard of care in clinical situations during training. Conclusions: This survey of OBGYN trainees indicates higher uncertainty about achieving ACGME milestones and procedural competency in clinical situations potentially affected by the Dobbs decision in states with legal restrictions on abortion.
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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 | 0.006 | 0.019 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".