Determinants of prenatal care use: evidence from 32 low-income countries across Asia, Sub-Saharan Africa and Latin America
Bibliographic record
Abstract
While much has been written on the determinants of prenatal care attendance in low-income countries, comparatively little is known about the determinants of the frequency of prenatal visits in general and whether there are separate processes generating the decisions to use prenatal care and the frequency of use. Using the Demographic and Health Surveys data for 32 low-income countries (across Asia, Sub-Saharan Africa and Latin America) and appropriate two-part and multilevel models, this article empirically assesses the influence of a wide array of observed individual-, household- and community-level characteristics on a woman's decision to use prenatal care and the frequency of that use, while controlling for unobserved community level factors. The results suggest that, though both the decision to use care and the number of prenatal visits are influenced by a range of observed individual-, household- and community-level characteristics, the influence of these determinants vary in magnitude for prenatal care attendance and the frequency of prenatal visits. Despite remarkable consistency among regions in the association of individual, household and community indicators with prenatal care utilization, the estimated coefficients of the risk factors vary greatly across the three world regions. The strong influence of household wealth, education and regional poverty on the use of prenatal care suggests that safe motherhood programmes should be linked with the objectives of social development programmes such as poverty reduction, enhancing the status of women and increasing primary and secondary school enrolment rate among girls. Finally, the finding that teenage mothers and unmarried women and those with unintended pregnancies are less likely to use prenatal care and have fewer visits suggests that safe mother programmes need to pay particular attention to the disadvantaged and vulnerable subgroups of population whose reproductive health issues are often fraught with controversy.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".