Factors Associated with Deworming Medication Utilization among Pregnant Women in Benin: Evidence from the Demographic and Health Survey
Bibliographic record
Abstract
Deworming medication utilization is a useful strategy to reduce the burden of anemia among pregnant women. Yet, we know very little about the prevalence and correlates of deworming medication utilization among pregnant women in sub-Saharan Africa, including Benin. To address this void in the literature, we used the 2017–2018 Benin Demographic and Health Survey and applied logistic regression analysis to explore the demographic, socioeconomic, and healthcare factors associated with deworming medication utilization in Benin. We found that deworming medication coverage was 65% at the national level. We observed that women aged 35–49 years were less likely to use deworming medication compared to those aged 15–24 years (OR = 0.79, p < 0.01). Compared to Christian women, Muslim women (OR = 0.70, p < 0.01) and women of other religions (OR = 0.51, p < 0.01) were also less likely to use deworming medication. Moreover, women with lower levels of education and household wealth, as well as unemployed women, were less likely to use deworming medication in comparison to their educated, richer, and employed counterparts. Women who visited ANC fewer than eight times were also less likely to use deworming medication compared to their counterparts who did so eight times or more (OR = 0.65, p < 0.001). Based on these findings, we discussed several implications for policymakers.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".