Towards an understanding of the multilevel factors associated with maternal health care utilization in Uttar Pradesh, India
Why this work is in the frame
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Bibliographic record
Abstract
BACKGROUND: This paper explores the multilevel factors associated with maternal health utilization in India's most populous state, Uttar Pradesh. 3 key utilization practices: registration of pregnancy, receipt of antenatal care, and delivery at home are examined for district and individual level predictors. The data is based on 5666 household surveys conducted as part of a baseline evaluation of the Uttar Pradesh Technical Support Unit (UPTSU.) program. OBJECTIVES: This intervention aims to assist the Government of Uttar Pradesh in increasing the efficiency, effectiveness, and equity of service delivery across a continuum of reproductive, maternal, new-born, child, and adolescent health (RMNCH+A) outcomes. METHODS: The paper employs multilevel models that control for individuals being nested within districts in order to understand the predictors of maternal health care utilization. RESULTS: The study identifies several individual-level predictors of health care utilization, including: literacy of the woman, the husband's schooling, age at marriage, and socio-economic factors. Key predictors of pregnancy registration include husband's schooling (OR 1.49, 95% CI 1.26-1.76), having a bank account (OR 1.36, 95% CI 1.11-1.68), and owning a house (OR 2.28, 95% CI 1.85-2.80). Factors affecting antenatal care include the woman's literacy (OR 1.49, 95% CI 1.28-1.73), the respondent having had a job in the last year (OR 1.39, 95% CI 1.10-1.77), and owning a house (OR 2.83, 95% CI 2.27-3.53). Home delivery tends to be associated with woman's literacy (OR 0.62, 95% CI 0.54-0.72) and marriage age of 15 and younger (OR 1.48, 95% CI 1.26-1.73). CONCLUSIONS: Interventions having equity considerations need to disrupt existing patterns of the health gradient. Successful implementation of such interventions, necessitate understanding the mechanisms that can disrupt the unequal utilization patterns and target domains of disadvantage. Knowledge of key predictors of utilization can aid in the implementation of such complex interventions.
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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 | 0.000 | 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 | 0.001 | 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 it