Best Practices and Challenges to Sex Worker Community Empowerment and Mobilisation Strategies to Promote Health and Human Rights
Bibliographic record
Abstract
Abstract Sex workers face a number of health and human rights challenges including heightened risk for HIV infection and suboptimal care and treatment outcomes, institutional and interpersonal violence, labour rights violations, and financial insecurity. In response, sex worker-led groups have been formed and sustained across geographic settings to address these challenges and other needs. Over the last several decades, a growing body of literature has shown that community empowerment approaches among sex workers are associated with significant reductions in HIV and other sexually transmitted infections. Yet legal and policy environments, as well as funding constraints, have often limited the reach, along with the impact and sustainability, of such approaches. In this chapter, we first review the literature on community empowerment and mobilisation strategies as a means to collectively address HIV, violence, and other health and human rights issues among sex workers. We then utilise two case studies, developed by the sex worker-led groups APROASE in Mexico and Ashodaya Samithi in India, to illustrate and contextualise community empowerment processes and challenges, including barriers to scale-up. By integrating the global literature with context-specific case studies, we distil lessons learned and recommendations related to community empowerment approaches among sex workers.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".