Factors Associated with Non-Institutional Delivery among Pregnant Women in Nepal
Bibliographic record
Abstract
Delivery location may influence maternal morbidity and mortality, especially in developing countries such as Nepal. The aim of this study was to determine factors associated with place of delivery among pregnant women in Nepal in order to inform health policy makers attempting to improve mother and child health. Data from the Multiple Indicator Cluster Survey, conducted in 2014, were retrospectively reviewed. In the survey, women aged 15-49 years were interviewed face-to-face using a structured questionnaire. Study subjects were women who had giving birth within the previous two years. A total of 2,086 women (48.9%) had non-institutional delivery (46.5% at home). Logistic regression models were used to identify significant factors influencing non-institutional delivery. Results showed that increasing educational level and wealth quintile index corresponded to a decreasing percentage of non-institutional delivery. More than half (55.5%) of women from rural areas had a non-institutional delivery. Multiparous women (57.2%) and those having less than 4 antenatal care visits (66.8%) had relatively higher rates of non-institutional delivery. In conclusion, there is a need to intensify education for pregnant women, especially those who have had previous childbirth experience. It is also crucial to target women from poor households, to increase their awareness, and promote institutional delivery.
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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 | 0.000 | 0.003 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 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".