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Record W4404002897 · doi:10.18103/mra.v12i10.5962

Impact of the United States Supreme court Dobbs versus Jackson Health Women’s Organization Decision on Abortion Curricula in United States Medical Schools

2024· article· en· W4404002897 on OpenAlexaff
Alyssa Famy, Angela Fleming, Laura Baecher-Lind, Rashmi Bhargava, Katherine T. Chen, Helen Morgan, Christopher M. Morosky, Celeste S. Royce, Jonathan Schaffir, Shireen Madani Sims, Jill M. Sutton, Tammy Sonn, The Undergraduate Education

Bibliographic record

VenueMedical Research Archives · 2024
Typearticle
Languageen
FieldMedicine
TopicReproductive Health and Contraception
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsSupreme courtAbortionCurriculumLawPolitical scienceMedicinePregnancy

Abstract

fetched live from OpenAlex

This study investigated the effect of the reversal of Roe versus Wade, with the 2022 United States (U.S.) Supreme Court Dobbs1 decision, on medical school reproductive health curriculum in U.S. An electronic survey was distributed to 248 U.S. medical school obstetrics and gynecology clerkship directors in March 2023 to assess faculty demographics, reproductive health topics included in medical school curriculum and challenges in abortion education after Dobbs. One hundred forty-eight faculty completed the survey (60% response rate) from 40 states and the District of Columbia; 45% of respondents were from states with abortion restrictions in the first and second trimesters. There were no significant changes in curricular content during the months following Dobbs. Thematic analysis of text responses indicates concerns about legal implications of teaching, state audits and restrictions on materials, as well as limits on abortion education in medical schools in states with restrictive abortion laws, even prior to Dobbs. PRECIS: In the months following the Dobbs decision, there was no change in abortion curricula in pre-clerkship courses or Obstetrics and Gynecology clerkship in U.S. medical schools.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.025
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.723
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.025
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.004
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.041
GPT teacher head0.446
Teacher spread0.404 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2024
Admission routes1
Has abstractyes

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