Preventing sexually transmitted and blood borne infections (STBBIs) among sex workers: a critical review of the evidence on determinants and interventions in high-income countries
Bibliographic record
Abstract
BACKGROUND: Across diverse regions globally, sex workers continue to face a disproportionate burden of HIV and other sexually transmitted and blood borne infections (STBBIs). Evidence suggests that behavioural and biomedical interventions are only moderately successful in reducing STBBIs at the population level, leading to calls for increased structural and community-led interventions. Given that structural approaches to mitigating STBBI risk beyond HIV among sex workers in high-income settings remain poorly understood, this critical review aimed to provide a comprehensive synthesis of the global research and literature on determinants of HIV and other STBBIs and promising intervention practices for sex workers of all genders in high-income countries. METHODS: We searched for publications over the last decade (January 2005-March 2016) among sex workers (cis women, cis men, and trans individuals). Data obtained from quantitative peer-reviewed studies were triangulated with publicly available reports and qualitative/ethnographic research where quantitative evidence was limited. RESULTS: Research demonstrates consistent evidence of the direct and indirect impacts of structural factors (e.g., violence, stigma, criminalization, poor working conditions) on increasing risk for STBBIs among sex workers, further compounded by individual and interpersonal factors (e.g., mental health, substance use, unprotected sex). Sub-optimal access to health and STBBI prevention services remains concerning. Full decriminalization of sex work has been shown to have the largest potential to avert new infections in sex work, through reducing workplace violence and increasing access to safer workspaces. Promising practices and strategies that should be scaled-up and evaluated to prevent STBBIs are highlighted. CONCLUSIONS: The high burden of STBBIs among sex workers across high-income settings is of major concern. This review uniquely contributes to our understanding of multilevel factors that potentiate and mitigate STBBI risk for sex workers of all genders. Research suggests that multipronged structural and community-led approaches are paramount to addressing STBBI burden, and are necessary to realizing health and human rights for sex workers. Given the heterogeneity of sex worker populations, and distinct vulnerabilities faced by cis men and trans sex workers, further research utilizing mixed-methods should be implemented to delineate the intersections of risk and ameliorate critical health inequalities.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".