The association between HIV self-test awareness and recent HIV testing uptake in the male population in Gambia: data analysis from 2019–2020 demographic and health survey
Bibliographic record
Abstract
BACKGROUND: The Gambian Ministry of Health is supportive of HIV self-testing (HIVST) and HIVST initiatives are being piloted as an additional strategy to increase HIV testing for individuals not currently reached by existing services, particularly men. This study aimed to determine awareness of HIVST among Gambian men, and whether prior awareness of HIVST is associated with recent HIV testing uptake. METHODS: We used men's cross-sectional data from the 2019-2020 Gambian Demographic and Health Survey. We employed design-adjusted multivariable logistic regression to examine the association between HIVST awareness and recent HIV testing. Propensity-score weighting was conducted as sensitivity analyses. RESULTS: Of 3,308 Gambian men included in the study, 11% (372) were aware of HIVST and 16% (450) received HIV testing in the last 12 months. In the design-adjusted multivariable analysis, men who were aware of HIVST had 1.76 times (95% confidence interval: 1.26-2.45) the odds of having an HIV test in the last 12 months, compared to those who were not aware of HIVST. Sensitivity analyses revealed similar findings. CONCLUSION: Awareness of HIVST may help increase the uptake of HIV testing among men in Gambia. This finding highlights HIVST awareness-raising activities to be an important intervention for nationwide HIVST program planning and implementation in Gambia.
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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 | 0.002 | 0.004 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".