The association between mother‐to‐child HIV transmission knowledge and antenatal care utilisation in Cameroon
Bibliographic record
Abstract
While the health benefits of antenatal care (ANC) utilisation for mothers and their infants have been well documented, very few studies have explored the association between mother-to-child transmission (MTCT) knowledge of human immunodeficiency virus (HIV) and mothers' utilisation of ANC in HIV endemic regions such as Cameroon. To address this void in the literature, we use the 2018 Cameroon Demographic and Health Survey to examine the association between mother's knowledge of MTCT of HIV and the three strands of ANC utilisation (i.e., number of ANC visits, timing to first ANC visit, and place of delivery). We found that women with adequate MTCT knowledge were more likely to have four to seven ANC visits (relative risk ratio [RRR] = 1.39, p < 0.001) and more than eight ANC visits (RRR = 1.43, p < 0.05), compared to their counterparts with inadequate knowledge. Similarly, women with adequate MTCT knowledge were more likely to attend ANC within the first trimester (odds ratio [OR] = 1.16, p < 0.05) and to give birth in a health facility (OR = 1.37, p < 0.001) than their counterparts with inadequate MTCT of HIV knowledge. These results remained robust after controlling for theoretically relevant variables. Based on these findings, we discussed several implications for policymakers and recommendations for future research.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| 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) | 0.002 | 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".