The <i>Dobbs</i> Decision and Undergraduate Medical Education: The Unintended Consequences and Strategies to Optimize Reproductive Health and a Competent Workforce for the Future
Bibliographic record
Abstract
The June 2022 U.S. Supreme Court decision on Dobbs v Jackson Women's Health Organization resulted in state-specific differences in abortion care access across the country. The primary concern in the obstetrics and gynecology education community has been the impact on resident and fellowship training programs. However, the impact on undergraduate medical education and the broad implications for future generations of physicians are crucial to address. It is estimated that 48% of matriculants to MD-granting medical schools will receive their medical education in the 26 states with significant abortion restrictions or bans. Undergraduate medical educators need to continue to adequately teach the basic science, clinical care, and population health outcomes of reproductive medicine, including pregnancy and abortion. In addition, students in states with more restrictions on abortion will have less or no clinical exposure, and those in states with few restrictions may be excluded due to overcrowding of learners from restricted states. Students' own health care also needs to be considered, as access to abortion care for themselves or their partners may create applicant pool demographic shifts by state as applicants consider options for where to pursue their medical education. It is important to ensure that teaching of foundational science of pregnancy, abortion, and reproductive health continues throughout the United States. Undergraduate and graduate medical educators will need to closely monitor the downstream impact of decreased clinical exposure of abortion. Further study of the personal health impact of abortion care access for medical students and awareness of the changing applicant pool demographics by state is needed.
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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 | 0.013 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.012 |
| Scholarly communication | 0.012 | 0.006 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.029 | 0.023 |
| Insufficient payload (model declined to judge) | 0.013 | 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".