Racial and Ethnic Differences in U.S. Women's Choice Of Reversible Contraceptives, 1995–2010
Bibliographic record
Abstract
CONTEXT: In the United States, unintended pregnancies disproportionately affect minority populations. Persistent disparities in contraceptive use between black and Hispanic women and white women have been identified, but it is unclear whether racial and ethnic differences in use of the most effective methods have changed. METHODS: Data on 4,727 women from the 1995 National Survey of Family Growth and 5,775 women from the 2006-2010 cycle were used to examine the association between race and ethnicity and women's choice of reversible contraceptives according to level of method effectiveness. Stepwise multinomial logistic regressions were used to identify changes in this association between cycles. Analyses controlled for demographic, socioeconomic, family, religious, behavioral and geographic characteristics. RESULTS: The proportion of women using the most effective reversible contraceptive methods increased from 46% in 1995 to 53% in 2006-2010. In 1995, black and Hispanic women's use of the most effective reversible contraceptives did not differ from that of white women. By 2006-2010, however, black women were substantially less likely than white women to use highly effective reversible contraceptive methods rather than no method (relative risk ratio, 0.6). An analysis that combined the two data sets and included a term for the interaction between survey year and race and ethnicity found that relative to white women, black women were less likely in 2006-2010 than in 1995 to use more effective methods rather than no method (0.6). CONCLUSIONS: Further research is needed to identify factors that may be causing racial and ethnic disparities in contraceptive decisions to widen.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".