Factors Associated with the Use of Withdrawal in Iran: Do Fertility Intentions Matter?
Bibliographic record
Abstract
The majority of unwanted pregnancies in Iran are due to withdrawal failures. This study seeks to explore factors associated with withdrawal use, which is highly prevalent among Iranian married couples. Multivariate logistic regression analyses are employed to estimate the likelihood of using withdrawal rather than modern contraceptives among a representative sample of 6199 Iranian married women using contraception, taken from the 2000 Iran Demographic and Health Survey. Among other findings, women’s fertility intentions strongly interacted with women’s parity, number of son, and wealth status, where birth limiters with a lower wealth status, lower parity, and no son were more likely to use withdrawal rather than modern contraceptives. Other results showed that higher education levels and economic status were strongly associated with the greater likelihood of using withdrawal rather than modern contraceptives. Women who lived in urban areas and were unemployed and who had a smaller number of children were more likely to use withdrawal rather than a modern contraceptive method. Compared with younger women, those aged 40-49 were more likely to use withdrawal than modern methods. A reduction in unwanted pregnancies can best be achieved by improving the current family planning program in regions and among subgroups of the population who regulate their fertility by using withdrawal.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.002 | 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".