Factors influencing non-institutional deliveries in afghanistan: secondary analysis of the afghanistan mortality survey 2010.
Bibliographic record
Abstract
Home delivery in unhygienic environments is common among Afghan women; only one third of births are delivered at health facilities. Institutional delivery is central to reducing maternal mortality. The factors associated with place of delivery among women in Afghanistan were examined using the Afghanistan Mortality Survey 2010 (AMS 2010), which was open to researchers. The AMS 2010 data were collected through an interviewer-led questionnaire from 18,250 women. Odds ratio (OR) and 95% confidence interval (CI) of non-institutional delivery were estimated by logistic regression analysis. When age at survey, education, parity, residency, antenatal care frequency, remoteness, wealth and regions were adjusted, the OR of non-institutional delivery was 8.37 (95% CI, 7.47-9.39) for no antenatal care relative to four or more antenatal care visits, 4.07 (95% CI, 3.45-4.80) for poorest household relative to women from richest household, 2.02 (95% CI, 1.43-2.84) for no education relative to higher education, 1.78 (95% CI, 1.52-2.09) for six or more deliveries relative to one delivery, and 1.50 (95% CI, 1.36-1.67) for rural relative to urban residency. Since antenatal care was strongly associated with non-institutional delivery after adjustment of the other factors, antenatal care service may promote institutional deliveries, which can reduce maternal mortality ratio in Afghanistan.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".