The association between reproductive rights and access to abortion services and mental health among US women
Bibliographic record
Abstract
Background: This study examines whether living in US states with (1) restrictive reproductive rights and (2) restrictive abortion laws is associated with frequent mental health distress among women. Methods: We operationalize reproductive rights using an overall state-level measure of reproductive rights as well as a state-level measure of restrictive abortion laws. We merged data from the 2018 Behavioral Risk Factor Surveillance System (BRFSS) with these state-level exposure variables and other state-level information. We used multilevel logistic regression to assess the relationship between these two measures and the likelihood of reporting 14 or more days of frequent mental health distress. We also tested whether associations differed across race, household income, education, and marital status. Results: In the adjusted models, a standard deviation-unit increase in the reproductive rights score was significantly associated with decreased odds of reporting frequent mental health distress (OR = 0.95, 95% CI = 0.91, 0.99). Women in states with very hostile abortion restrictions had higher odds of frequent mental health distress. Associations between state-level abortion restrictions were larger among women 25-34 years old and women with a high school degree. For example, women aged 25-34 years residing in moderate (OR = 1.54, 95% CI = 1.14, 2.04), hostile (OR = 1.59, 95% CI = 1.15, 2.18), and very hostile (OR = 1.29, 95% CI = 1.02, 1.64) states were more likely to report frequent mental health distress than women living in states with less restrictive abortion policies. Conclusion: We found the association between state-level restrictions on reproductive rights and abortion access and frequent mental health distress differed by age and socioeconomic status. These results suggest abortion rights restrictions may contribute to mental health inequities among women.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".