Findings from a participatory mapping study to increase HIV and STI testing with female sex professionals in Pointe Noire, Congo
Bibliographic record
Abstract
Background Sex workers are disproportionately affected by HIV and sexually transmitted infections (STI) in the Republic of Congo. We conducted a one-group pre-post test participatory mapping (PM) intervention to increase HIV/STI testing uptake with female sex professionals (FSP) in Pointe Noire, Congo. Methods We engaged a peer-driven sample of FSP in Pointe Noire in a 2-day PM intervention. Voluntary HIV and STI testing and treatment was offered at Time 2 (T2) (directly post-intervention) until Time 3 (T3) (8-week post intervention). To measure HIV/STI testing changes between Time 1 (T1) (baseline, lifetime HIV/STI testing), T2, and T3, we used a generalized estimating equation model with robust standard errors, using an unstructured correlation matrix to account for within-subject correlations. Results Among n = 99 participants (mean age: 25.5, standard deviation = 6.5), in analyses adjusted for age and sex work duration, there were significant increases at T2 in uptake of HIV testing (adjusted Odds Ratio [aOR] = 2.42; 95% CI = 1.31–4.48) and STI testing (aOR = 2.40; 95% CI = 1.27–4.54), as well as at T3 (HIV testing: aOR = 7.37; 95% CI = 2.82–19.23, STI testing: aOR = 5.88; 95% CI = 2.82–12.24), compared to baseline. Conclusions Findings signal the promise of community-based approaches such as participatory mapping for increasing HIV/STI testing uptake with FSP in Pointe Noire.
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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 | 0.009 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| 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".