Do women empowerment indicators predict receipt of quality antenatal care in Cameroon? Evidence from a nationwide survey
Bibliographic record
Abstract
BACKGROUND: World Health Organisation (WHO) recommends quality antenatal care (ANC) for all pregnant women, as one of the strategies for achieving targets 3.1 and 3.2 of the sustainable development goals. Maternal mortality ratio remains high in Cameroon (782 maternal deaths per 100,000 live births). Extant literature suggest a positive association between women empowerment indicators and maternal healthcare utilisation in general. In Cameroon, this association has not received scholarly attention. To fill this knowledge gap, we investigated the association between women empowerment indicators and quality ANC in Cameroon. METHODS: Data of 4615 women of reproductive age were analysed from the women's file of the 2018 Cameroon Demographic and Health Survey. Quality ANC (measured by six indicators) was the outcome of interest. Binary Logistic Regression was conducted. All results of the Binary Logistic Regression analysis were presented as adjusted odds ratios (aORs) with 95% confidence intervals (CIs). All analyses were done using Stata version 14. RESULTS: In all, 13.5% of the respondents received quality ANC. Women with low knowledge level (aOR = 0.66, CI 0.45, 0.98) had a lesser likelihood of receiving quality ANC compared to those with medium knowledge level. Women who highly approved wife beating (aOR = 0.54, CI 0.35, 0.83) had lesser odds of receiving quality ANC compared to those with low approval of wife beating. CONCLUSION: The study has pointed to the need for multifaceted approaches aimed at enhancing the knowledge base of women. The Ministry of Public Health should collaborate and intensify female's reproductive health education. The study suggests that women advocacy and maternal healthcare interventions in Cameroon must strive to identify women who approve of wife beating and motivate them to disapprove all forms of violence.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".