Undesired Births, Contraception, and Abortion Before and After the Cairo Consensus: Trends in Conditional Undesired Birth Rates and the Impact of Contraception and Abortion
Bibliographic record
Abstract
The Programme of Action adopted after the International Conference on Population and Development (ICPD), and later the Beijing Declaration, affirmed commitments to the human right to decide on the number and spacing of one's children and have the information and means to do so. In this study, we estimate trends related to this component of reproductive agency-undesired births per thousand women who want to avoid pregnancy, the conditional undesired birth rate-with annual rates for five-year periods from 1975 to 2024. Worldwide, 36 million undesired births occurred annually in 2020-2024 compared to 45 million annually in 1990-1994, corresponding to a decrease in rate from 61 to 32. Had it not been for increases in contraceptive use since 1990-1994, the global average rate in 2020-2024 would have been 36 percent higher than it actually was. Had it not been for increasing proportions of pregnancies aborted, the rate would have been 58 percent higher. Comparing regional averages, excepting Sub-Saharan Africa and Oceania, the pace of decline in conditional undesired birth rates slowed by the 2000s; hence, the global average rate decreased by 22 percent in the latter half of the post-ICPD period after declining by 31 percent and 33 percent during the 15-year periods immediately before and after ICPD.
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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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".