Effect of maternal education on choice of location for delivery among Indian women.
Bibliographic record
Abstract
BACKGROUND: Delivery in a healthcare facility is associated with better outcomes for both mother and child. However, in India, a large proportion of deliveries take place outside health facilities. We studied the effect of maternal education on the choice of location for delivery in the Indian population. METHODS: Data from the National Family Health Survey 3 (NFHS-3) were used. The survey included women who were selected using a multi-stage (2-stage for urban areas and 3- stage for rural areas), stratified (based on demographic or social factors) sampling technique; the primary sampling units selected were proportional to population size, and the subsequent steps used simple random sampling. Effect of maternal education on the choice of place for delivery (home, public or private facility) was investigated through a multinomial logistic regression model. The model adjusted for several factors at individual, household and community level, the survey design effect and included sampling weights. RESULTS: Of the 124 385 women aged 15-49 years included in the NFHS-3 dataset, 36 850 (29.6%) had had one or more childbirth during the past 5 years. A little more than half of all the deliveries were at home, and approximately a quarter each of the remaining deliveries were at public and private facilities, respectively. Maternal education was strongly and independently associated with the choice of location of delivery. For the choice sets of public facility versus home delivery and private facility versus home delivery, a clear dose-response relationship was apparent-higher maternal education was associated with a higher probability of delivery at a public or private health facility compared to home. CONCLUSION: Level of maternal education was a significant independent predictor of choice of location for childbirth among Indian women. Compared to cash incentives to increase facility-based delivery, improving maternal education may be a better way to achieve long term and sustained increase in facility deliveries in India.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".