Assessing the antenatal care-seeking determinants associated with the penetration of the WHO eight-visit antenatal care policy across states in Nigeria
Bibliographic record
Abstract
BACKGROUND: Following the adoption and implementation of the 2016 WHO eight-visit antenatal care (8vANC) policy in Nigeria, a national cross-sectional survey conducted in 2021 indicated significant state-level differences in the utilization rates of 8vANC. METHODS: We used a post-implementation sample, n = 9,416, obtained from the Nigeria Multiple Indicator Cluster Survey 2021 data to perform secondary analyses using the theory, model, and framework (TMF) implementation research approach. The outcome was defined as the penetration of the WHO 8vANC policy and measured as the proportion of women who used a minimum of 8vANC out of the total number of women who had live births within two years before the survey in 2021. We used multilevel modeling mixed effects logistic regression for the statistical analyses because of its ability to examine contextual effects in cross-sectional data. RESULTS: The results revealed that the residual variation in state-level 8vANC utilization--after accounting for the explanatory variables-attributable to between-state differences went from 46% in the unadjusted model to11% in the final adjusted model. The findings indicated that women in Southern states had the highest odds of 8vANC utilization. We also found that women who self-reported their perception of life satisfaction as 'very happy' (OR: 1.81, 95% C.I: 1.25-2.63, p = 0.002), and 'somewhat happy' (OR: 1.63, 95% C.I: 1.14-2.40, p = 0.012) had the highest odds of 8vANC utilization across states. Conversely, the perception of wife beating justified 'if she goes out without telling husband' had low odds (OR: 0.77, 95% C.I: 0.61-0.97, p = 0.032) on the penetration of the WHO 8vANC policy across states. CONCLUSION: A greater proportion of the observed differences in 8vANC penetration occurred among women nested within states than between states. Our findings support the need for a revised policy that promotes the integration of routine prenatal mental health screening into the current ANC model. CLINICAL TRIAL NUMBER: Not applicable.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".