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Obstetrician and Gynecologist Physicians’ Practice Locations Before and After the <i>Dobbs</i> Decision

2025· article· en· W4409628214 on OpenAlexaboutno aff
Becky Staiger, Valentin Bolotnyy, Sonya Borrero, Maya Rossin‐Slater, Jessica Van Parys, Caitlin Knowles Myers

Bibliographic record

VenueJAMA Network Open · 2025
Typearticle
Languageen
FieldMedicine
TopicReproductive Health and Contraception
Canadian institutionsnot available
Fundersnot available
KeywordsAbortionMedicineQuarter (Canadian coin)Supreme courtObstetrics and gynaecologyDemographyFamily medicineHealth careLawPregnancyPolitical science

Abstract

fetched live from OpenAlex

Importance: State abortion policies may influence the practice locations of obstetricians and gynecologists (OBGYNs), having potentially significant implications for access to and quality of reproductive health care. Objective: To explore changes in OBGYN practice locations from before to after the Dobbs v Jackson Women's Health Organization US Supreme Court decision in June 2022. Design, Setting, and Participants: National Plan & Provider Enumeration System data files were used in a descriptive cohort study assessing the association between state abortion policy environments and OBGYN practice locations in the US from January 1, 2018, to September 30, 2024, for all OBGYNs listed in the data files during the study period. Main Outcome and Measures: The number of OBGYNs practicing in states with differing abortion laws and the movement of OBGYNs between these states before and after the Dobbs decision. Results: The sample included 60 085 OBGYNs (59.7% women), of whom 3.8% were maternal-fetal medicine specialists and 12.9% were recent residency graduates. The mean increase in the per-quarter number of OBGYNs from before to after Dobbs was 8.3% (95% CI, 6.6%-10.1%) in states with total abortion bans, 10.5% (95% CI, 8.1%-13.0%) in states with gestational age limits or threatened bans, and 7.7% (95% CI, 5.9%-9.4%) in states with abortion protections. From the quarter immediately before Dobbs to the end of the study period, 95.8% of OBGYNs remained in protected states, 94.8% (95% CI, 94.3%-95.2%) remained in states threatening bans, and 94.2% (95% CI, 93.7%-94.7%) remained in states with abortion bans. Conclusions and Relevance: In this descriptive cohort study, there were no significant differences in trends in OBGYNs' practice locations across states with different abortion-related policy environments after the Dobbs decision. Although these findings do not provide insight into changes in the quality of care provided, they suggest that there are no major changes in the supply of OBGYNs associated with the Dobbs decision.

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)0.001
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.860
Threshold uncertainty score0.266

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.011
GPT teacher head0.323
Teacher spread0.312 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designOther design
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

Citations11
Published2025
Admission routes1
Has abstractyes

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