Characteristics of Immigrants Obtaining Abortions and Comparison with U.S.-Born Individuals
Bibliographic record
Abstract
Background: Little information exists about individuals born outside of the United States who seek abortion services from U.S.-based providers. Baseline data are necessary to identify future changes in the profile of this population. Materials and Methods: Using the Guttmacher Institute's Abortion Patient Survey, we pooled two national samples of individuals obtaining abortions from 2008–2009 to 2013–2014 to provide data on 17,873 respondents, 16% of whom were immigrants. We estimated the distribution of immigrant and U.S.-born respondents across demographic and circumstantial characteristics such as age, poverty level, and gestational age at abortion. We compared the distribution of characteristics by nativity status using chi-square tests. Results: The majority of immigrants obtaining abortions were in their 20s (51%), had poverty-level (50%) or near poverty-level incomes (23%), and had graduated from high school (78%). Almost half (45%) were uninsured and a similar proportion had been in the United States for less than 10 years (44%); nearly one-quarter completed their survey in Spanish. Compared with U.S.-born respondents, a larger proportion of immigrants were older, uninsured, and had not completed high school. A smaller proportion of immigrants compared with nonimmigrants had their abortions after 12 weeks (8% vs. 11%) or traveled over 50 miles to obtain their abortion (9% vs. 16%). Conclusions: Particularly with the continued rise in both restrictive abortion and immigration policies in the United States, it is critical to monitor how immigrants' use of and access to abortion services are impacted in the changing environment. Ensuring that policies and clinical practices facilitate abortion access for immigrants will serve to better support the reproductive health needs of all women.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".