Where to deliver? Analysis of choice of delivery location from a national survey in India
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
BACKGROUND: In order to reduce maternal mortality, the Indian government has increased its commitment to institutional deliveries. We assess the determinants of home, private and public sector utilization for a delivery in a Western state. METHODS: Cross sectional analyses of the National Family Health Survey - 2 dataset. SETTING: Maharashtra state. The dataset had a sample size of 5391 ever-married females between the ages of 15 to 49 years. Data were abstracted for the most recent birth (n = 1510) and these were used in the analyses. Conceptual framework was the Andersen Behavioral Model. Multinomial logistic regression analyses was conducted to assess the association of predisposing, enabling and need factors on use of home, public or private sector for delivery. RESULTS: A majority delivered at home (n = 559, 37%); with private and public facility deliveries accounting for 32% (n = 493) and 31% (n = 454) respectively. For the choice set of home delivery versus public facility, women with higher birth order and those living in rural areas had greater odds of delivering at home, while increasing maternal age, greater media exposure, and more then three antenatal visits were associated with greater odds of delivery in a public facility. Maternal and paternal education, scheduled caste/tribe status, and media exposure were statistically significant predictors of the choice of public versus private facility delivery. CONCLUSION: As India's economy continues to grow, the private sector will continue to expand. Given the high household expenditures on health, the government needs to facilitate insurance schemes or provide grants to prevent impoverishment. It also needs to strengthen the public sector so that it can return to its mission of being the safety net.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it