Assessing the impact of TRAP laws on abortion and women’s health in the USA: a systematic review
Bibliographic record
Abstract
INTRODUCTION: Targeted Regulation of Abortion Providers (TRAP) laws impose extensive and sometimes costly requirements on abortion providers and facilities, potentially leading to barriers to care. Understanding the impact of these laws is important given their prevalence in the USA, but no review to date has summarised the available evidence. We conducted a systematic review of literature on TRAP laws and their impact on abortion trends and women's health. METHODS: We searched MEDLINE, PubMed and EconLit for original, quantitative studies where the exposure was at least one TRAP policy and the outcome was abortion and/or any women's physical or mental health outcome. RESULTS: Six articles met our inclusion criteria. The most common outcome was population-level abortion trends; studies also assessed the effect of TRAP laws on gestational age at presentation and measures of self-perceived burden. While certain TRAP laws (eg, admitting privilege requirements) appeared to have an effect on abortion outcomes, the impact of other laws - or combinations of laws - was unclear, due in part to heterogeneity between studies with respect to study design, geography, and exposure definition. CONCLUSIONS: TRAP laws may have an impact on the experience of obtaining an abortion in the USA. However, our review revealed a paucity of empirical research on their population and individual-level impact, as well as some disagreement about the effect of different TRAP laws on subsequent abortion outcomes. Future research should prioritise the specific TRAP laws that may have a uniquely strong effect on state-level abortion rates and other outcomes.
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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 | 0.012 | 0.069 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.007 |
| Bibliometrics | 0.012 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".