Levels, Trends, and Determinants of Unintended Pregnancy in Iran: The Role of Contraceptive Failures
Bibliographic record
Abstract
The rate of contraceptive use in Iran is high, but because abortion is illegal, many unintended pregnancies among married women are likely to be terminated by clandestine and often unsafe procedures, resulting in adverse health outcomes. Drawing upon data from the 2009 Tehran Survey of Fertility, this study estimates the levels and trends of unintended pregnancy and examines determinants of pregnancy intentions for the most recent birth, using multinomial logistic regression analysis. The level of unintended pregnancy decreased from 32 percent in 2000 to 21 percent in 2009, while contraceptive use increased. Unintended pregnancies in the five years preceding the 2009 survey resulted from failures of withdrawal (48 percent) and of modern contraceptive use (20 percent), together with contraceptive discontinuation (26 percent) and nonuse (6 percent). Multivariate findings show that, compared with women experiencing withdrawal failures, the risk of unintended pregnancy was higher among women reporting modern contraceptive failure and lower among those reporting contraceptive discontinuation and nonuse. The high risk of unwanted pregnancy among women experiencing failures in practicing withdrawal or using modern contraceptive methods points to an unmet need for family planning counseling and education rather than to a shortage of contraceptive methods.
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".