Seeking evidence to support efforts to increase use of antenatal care: a cross-sectional study in two states of Nigeria
Bibliographic record
Abstract
BACKGROUND: Antenatal care (ANC) attendance is a strong predictor of maternal outcomes. In Nigeria, government health planners at state level and below have limited access to population-based estimates of ANC coverage and factors associated with its use. A mixed methods study examined factors associated with the use of government ANC services in two states of Nigeria, and shared the findings with stakeholders. METHODS: A quantitative household survey in Bauchi and Cross River states of Nigeria collected data from women aged 15-49 years on ANC use during their last completed pregnancy and potentially associated factors including socio-economic conditions, exposure to domestic violence and local availability of services. Bivariate and multivariate analysis examined associations with having at least four government ANC visits. We collected qualitative data from 180 focus groups of women who discussed the survey findings and recommended solutions. We shared the findings with state, Local Government Authority, and community stakeholders to support evidence-based planning. RESULTS: 40% of 7870 women in Bauchi and 46% of 7759 in Cross River had at least four government ANC visits. Women's education, urban residence, information from heath workers, help from family members, and household owning motorized transport were associated with ANC use in both states. Additional factors for women in Cross River included age above 18 years, being married or cohabiting, being less poor (having enough food during the last week), not experiencing intimate partner violence during the last year, and education of the household head. Factors for women in Bauchi were presence of government ANC services within their community and more than two previous pregnancies. Focus groups cited costly, poor quality, and inaccessible government services, and uncooperative partners as reasons for not attending ANC. Government and other stakeholders planned evidence-based interventions to increase ANC uptake. CONCLUSION: Use of ANC services remains low in both states. The factors related to use of ANC services are consistent with those reported previously. Efforts to increase uptake of ANC should focus particularly on poor and uneducated women. Local solutions generated by discussion of the evidence with stakeholders could be more effective and sustainable than externally driven interventions.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 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".