Bibliographic record
Abstract
CONTEXT: Abortion is severely restricted in Iran, and many women with an unwanted pregnancy resort to clandes-tine, unsafe abortions. Accurate information on abortion incidence is needed to assess the extent to which women ?experience unwanted pregnancies and to allocate resources for contraceptive services. METHODS: Data for analysis came from 2,934 married women aged 15-49 who completed the 2009 Tehran Survey of Fertility. Estimated abortion rates and proportions of known pregnancies that end in abortion were calculated for all women and for demographic and socioeconomic subgroups, and descriptive data were used to examine women's contraceptive use and reasons for having an abortion. RESULTS: Annually, married women in Tehran have about 11,500 abortions. In the year before the survey, the estimated total abortion rate was 0.16 abortions per woman, and the annual general abortion rate was 5.5 abortions per 1,000 women; the general abortion rate peaked at 11.7 abortions among those aged 30-34. An estimated 8.7 of every 100 known pregnancies ended in abortion. The abortion rate was elevated among women who were employed or had high levels of income or education, as well as among those who reported a low level of religiosity, had two children or wanted no more. Fertility-related and socioeconomic reasons were cited by seven in 10 women who obtained an abortion. More than two-thirds of pregnancies that were terminated resulted from method failures among women who had used withdrawal, the pill or a condom. CONCLUSIONS: Estimated abortion rates and their correlates can help policymakers and program planners identify subgroups of women who are in particular need of services and counseling to prevent unwanted pregnancy.
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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 | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".