Patterns and Predictors of Insufficient Antenatal Care Utilization in Nigeria over a Decade: A Pooled Data Analysis Using Demographic and Health Surveys
Bibliographic record
Abstract
This study investigated the patterns of antenatal care (ANC) utilization and insufficient use of ANC as well as its association with some proximate socio-demographic factors. This was a cross-sectional study using pooled data Nigeria Demographic and Health Surveys from years 2008, 2013 and 2018. Participants were 52,654 women of reproductive age who reported at least one birth in the five years preceding the surveys. The outcome variables were late attendance, first contact after first trimester and less than four antenatal visits using multivariable logistic regression analysis. The overall prevalence of late timing was 74.8% and that of insufficient ANC visits was 46.7%. In the multivariable regression analysis; type of residency, geo-political region, educational level, household size, use of contraceptives, distance to health service, exposure to the media and total number of children were found to be significantly associated with both late and insufficient ANC attendance. About half of the pregnant women failed to meet the recommendation of four ANC visits. Investing on programs to improve women's socio-economic status, addressing the inequities between urban and rural areas of Nigeria in regard to service utilization, and controlling higher fertility rates may facilitate the promotion of ANC service utilization in Nigeria.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".