Unintended pregnancy and gender inequality worldwide: an ecological analysis
Bibliographic record
Abstract
Unintended pregnancy compromises many women's and girls' ability to pursue the lives that they want. The conditional unintended pregnancy rate (CUPR) is a measure of unintended pregnancy among women who wish to avoid getting pregnant. Using the CUPR, we explore the relationship between gender inequality and unintended pregnancy across 132 countries. We used gender inequality indicators from the UNDP Human Development Report and estimates of the incidence of unintended pregnancy published by the Guttmacher Institute and WHO. We regressed the CUPR on several measures of gender inequality using least squares with a percentile bootstrap to account for sampling error and the additional uncertainty in the model-based unintended pregnancy estimates. We find that unintended pregnancy is positively correlated with multiple composite measures of gender inequality, even after controlling for countries' levels of economic development. Of the components of gender inequality, gender disparities in educational attainment were most strongly correlated with unintended pregnancy in multivariable regressions. We also find that female educational attainment is a stronger predictor of the CUPR than male educational attainment. Analyses with the standard unintended pregnancy rate, a measure that does not take into account differences across settings in the proportion of women who wish to avoid getting pregnant, obscured the strength of the observed relationships. Further exploration of the factors underlying this relationship can inform policies to improve the quality of women's lives.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".