Understanding the Intention to Undergo Regular HIV Testing Among Female Sex Workers in Benin
Bibliographic record
Abstract
BACKGROUND: HIV testing constitutes an entry point for HIV prevention and access to care. Although access to tests has increased in most low- and middle-income countries in recent years, regular HIV testing among high-risk populations remains a challenge. Understanding the determinants of regular HIV testing is the key to improving treatment-as-prevention programs and access to care. This study aimed to identify psychosocial factors associated with the intention to be HIV tested every 3 months among female sex workers (FSWs) in Benin. METHODS: We developed an interview questionnaire based on the Theory of Planned Behavior and other theoretical variables. We interviewed 450 FSWs in their work place. Using Amos software, we applied structural equation modeling to identify the determinants of intention. RESULTS: Previous testing was reported by 87% of FSWs, 40% of whom reported having been tested in the last 3 months. More than half of the FSWs (69%) showed a strong intention to be HIV tested during the next 3 months. The structural model indicates that 55% of the variance in intention is explained in descending order of importance (standardized coefficient weight, β) by perceived control, descriptive norms, control beliefs, habits, attitude, risk perception, and normative beliefs. CONCLUSIONS: This is the first theoretically based study identifying determinants of intention to undergo regular HIV testing among FSWs in sub-Saharan Africa. The results can inform development of interventions to maintain and increase regular HIV testing among FSWs, thus reinforcing primary prevention and supporting early access to care.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".