Birth preparedness and complication readiness: Evaluating the “know-do” gap among women receiving antenatal care in Benin City, Nigeria
Bibliographic record
Abstract
Across several African countries, birth preparedness and complication readiness (BPACR) among pregnant women is poor. The practice of BPACR, though improving in recent years, is not commensurate with the knowledge available to pregnant women. Maternal health indices remain sub-optimal. This study evaluates the determinants of this "know-do' gap among women receiving antenatal care at a secondary health facility in Benin City, Nigeria. A cross-sectional study involving 427 pregnant women was conducted between October and December 2020 using a structured interviewer-administered questionnaire. The prevalence of knowledge and practice were described, and the determinants of BPACR practice evaluated using bivariable (chi-square) analysis and multivariable ordinal logistic regression with post-estimation predictive margins analysis. About 77% of respondents had good birth preparedness practice. Multivariable regression revealed that respondents with poor knowledge and moderate knowledge of components of BPACR had statistically significant lower odds (OR:0.05 (95% CI: 0.02-0.13) and 0.10 (95% CI: 0.03-0.30) times, respectively) for greater practice of BPACR when compared to those with good knowledge. Respondents with poor knowledge of danger signs had statistically significant lower odds (OR: 0.08 (95% CI: 0.03-0.26) for greater practice of BPACR when compared to those with good knowledge. But predictive margins analyses demonstrates that knowledge, though critical to practice, is insufficient to optimize practice. The optimum number of danger signs women need to know to improve practice may be between eight to ten. Beyond this number, practice may not change significantly. Other predictors of BPACR practice include income level, parity, gravidity, and residential settings. The number of antenatal clinic visits had no statistically significant correlation with BPACR practice. Interventions to facilitate practice at the community level may be helpful to improve outcomes and bridge the know-do gap with respect to BPACR within the study context.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".