Sterilization, Infecundity, and Reproductive Autonomy in Rural, Suburban, and Urban America: Results From a National Survey
Bibliographic record
Abstract
OBJECTIVE: Rural women are significantly more likely than urban women to be sterilized. This study aims to understand why rural women depend so heavily on this method of fertility control, whether they are more likely than suburban and urban women to desire sterilization reversal, and the impact of female sterilization on rural women's ability to achieve their fertility goals. METHODS: Data from 10,081 sexually active women aged 15 to 49, who participated in the National Survey of Family Growth (2015-2019), were analyzed using binary and multinomial logistic regression analyses. Unadjusted and adjusted predicted probabilities were calculated to estimate the prevalence of (1) female sterilization, (2) desire for sterilization reversal or wanting a(nother) child if sterilized, and (3) unwanted infecundity among rural, suburban, and urban women. RESULTS: Rural women (24.2%) are substantially more likely than suburban (15.3%) or urban (13.9%) women to receive tubal ligation. These disparities are not explained by women's demographic, reproductive, religious, and socioeconomic characteristics. Rural women who are sterilized are not more likely than suburban or urban women to desire sterilization reversal or to want to have (more) children. However, because more rural women rely on tubal ligation, a significantly higher fraction of rural women (34.8%) than urban women (23.8%) who want to have a(nother) child are infecund. Roughly, 40% of infecund rural women who wish to conceive had tubal ligation. CONCLUSIONS: Limited contraceptive choice undermines rural women's ability to conceive wanted births. These results highlight another important reason for expanded reproductive health care in rural America.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".