HIV testing and counselling among women in Benin: a cross-sectional analysis of prevalence and predictors from demographic and health survey data
Bibliographic record
Abstract
OBJECTIVE: To examine the uptake of HIV testing and counselling (HTC) and its associated factors among women in Benin. DESIGN: We performed a cross-sectional analysis of data from the 2017-2018 Benin Demographic and Health Survey. A weighted sample of 5517 women was included in the study. We used percentages to present the results of the uptake of HTC. Multilevel binary logistic regression analysis was used to examine the predictors of HTC uptake. The results were presented using adjusted odds ratios (aORs), with 95% confidence intervals (CIs). SETTING: Benin. PARTICIPANTS: Women aged 15-49. OUTCOME MEASURE: Uptake of HTC. RESULTS: The overall uptake of HTC among women in Benin was found to be 46.4% (44.4%-48.4%). The odds of HTC uptake was higher among women covered by health insurance (aOR 3.04, 95% CI 1.44 to 6.43) and those with comprehensive HIV knowledge (aOR 1.77, 95% CI 1.43 to 2.21). The odds of HTC uptake increased with increasing level of education, with the highest odds among those in the secondary or higher level (aOR 2.06, 95% CI 1.64 to 2.61). Also, the age of the women, mass media exposure, region of residence, high community literacy level, and high community socioeconomic status were associated with higher odds of HTC uptake. Women residing in rural areas were less likely to use HTC. Religious affiliation, number of sexual partners, and place of residence were associated with lower odds of HTC uptake. CONCLUSION: Our study has shown that the uptake of HTC among women in Benin is relatively low. There is a need to enhance efforts to empower women, as well as reduce health inequities as they all have a substantial impact on HTC uptake among women in Benin, taking into consideration the factors identified in this study.
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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 | 0.001 |
| 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 | 0.000 |
| 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".