Modelling prenatal health care utilization in Tajikistan using a two-stage approach: implications for policy and research
Bibliographic record
Abstract
Since the transition from a centrally planned to a market economy, Tajikistan has witnessed a high rate of child and maternal mortality, a decline in the birth rate and a significant drop in public expenditures on health care. Against this backdrop, this paper analyses the determinants of prenatal health care utilization using Andersen's behavioural model, which has been modified to the context of Tajikistan. We applied a two-stage sequential model to data drawn from a nationally representative survey. Binary logit regression is used to predict and explain the probability of using prenatal health care services, while negative binomial regression is used to predict and explain the frequency of using these services. Findings suggest that higher educational attainment increases the utilization of prenatal care. Conversely, poverty, limited knowledge about matters related to sex, low quality of health care service, lack of public infrastructure, as well as absence of or long distance of travel to the nearest health facility, all reduce the utilization of prenatal health care. Health policy and research implications are presented 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 | 0.001 | 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 | 0.001 | 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".