How Important are Individual, Household and Commune Characteristics in Explaining Utilization of Maternal Health Services? The Case of Vietnam?
Bibliographic record
Abstract
Using Vietnam’s latest National Household Survey data for 2001-2002 this paper assesses the influence of individual, household and commune-level characteristics on a woman’s decision to seek prenatal care, on the number of prenatal visits, and on the choice between giving birth at a health facility or at home. The decision to use any care and the number of prenatal visits is modeled using a two-part model. A random intercept logistic model is used to capture the influence of unobserved commune-specific factors found in the data regarding a woman’s decision to give birth at a health facility rather than at home. The results show that access to prenatal care and delivery assistance is limited by observed barriers such as low income, low education, ethnicity, geographical isolation and a high poverty rate in the community. More specifically, more prenatal visits increase the likelihood of giving birth at a health facility. Having compulsory health insurance increases the odds of giving birth at a health facility for middle and high income women. In contrast, health insurance for the poor increases the likelihood of having more prenatal visits but has little effect on the place of delivery. These results suggest that the existing safe motherhood programs should be linked with the objectives of social development programs such as poverty reduction, and that policy makers need to view both the individual and the commune as appropriate units for policy targeting.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".