On the socio-economic determinants of antenatal care utilization in Azerbaijan: evidence and policy implications for reforms
Bibliographic record
Abstract
Azerbaijan is a country with one of the highest child mortality rates in the regions of Eastern Europe and the former Soviet Union. Drawing on the nationally representative Demographic and Health Survey, this study examines the utilization of antenatal care in Azerbaijan to identify the socio-economic determinants of the usage, and its frequency, timing and quality. Consequently, binomial logit, two ordered logit and negative binomial regression models are specified to estimate the effect of various socio-economic characteristics on the likelihood of utilization. Place of living is an important determinant of antenatal healthcare utilization in Azerbaijan. It is important in determining the likelihood of utilization, its timing and quality of care received, whereas it is not significant in the model predicting the frequency of antenatal utilization. Women’s education is also significant in three models out of four. Education is important in explaining the frequency and timing of utilization as well as the quality of services received, but it is not significant in predicting the likelihood of utilization. Wealth gradient is another important determinant of antenatal care utilization in Azerbaijan inasmuch as it is significant in explaining the likelihood of prenatal care utilization and its frequency. In addition, two variables, birth order and desirability of the last child or current pregnancy, are significant only in explaining the likelihood of utilization. Therefore, we confirm the findings of previous studies, which reported that the utilization of prenatal health care is a multistage process in which decisions are sequential. Although the same set of factors may affect decision-making at all stages, the effect of these factors is different at different stages. Implications for reforms in the healthcare sector to improve antenatal care utilization in Azerbaijan are provided and discussed.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".