Strategies to promote the meaningful involvement of sex workers in HIV prevention and care
Bibliographic record
Abstract
PURPOSE OF REVIEW: We review the recent evidence regarding strategies for engaging sex workers in HIV prevention and care programs. We searched Pub Med on 19 March 2019 using terms 'Sex Work' And 'HIV infections'. Our search was limited to articles published since 2017. RECENT FINDINGS: Community empowerment approaches where sex workers work collaboratively to address their specific priorities and concerns, including those beyond HIV, are those most likely to meaningfully engage sex workers. Community-driven programs that combine structural, behavioral and biomedical approaches can facilitate improved HIV outcomes by tackling barriers to uptake and retention of services along all steps in the prevention and care cascades. Microplanning, network-based recruitment and mobile-phone interventions can also help reach and support sex workers to mobilize and to engage with a range of services. Sex worker-led groups and initiatives including economic strengthening and community drug refill groups can both build social cohesion and address structural barriers to HIV outcomes including financial insecurity. Interventions which focus narrowly on increasing uptake of specific steps in prevention and care cascades outside the context of broader community empowerment responses are likely to be less effective. SUMMARY: Comprehensive, community-driven approaches where sex workers mobilize to address their structural, behavioral and biomedical priorities work across HIV prevention and treatment cascades to increase uptake of and engagement with prevention and care technologies and promote broader health and human rights. These interventions need to be adequately supported and taken to scale.
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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 | 0.005 | 0.015 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".