Selling sex in unsafe spaces: sex work risk environments in Phnom Penh, Cambodia
Bibliographic record
Abstract
BACKGROUND: The risk environment framework provides a valuable but under-utilised heuristic for understanding environmental vulnerability to HIV and other sexually transmitted infections among female sex workers. Brothels have been shown to be safer than street-based sex work, with higher rates of consistent condom use and lower HIV prevalence. While entertainment venues are also assumed to be safer than street-based sex work, few studies have examined environmental influences on vulnerability to HIV in this context. METHODS: As part of the Young Women's Health Study, a prospective observational study of young women (15-29 years) engaged in sex work in Phnom Penh, we conducted in-depth interviews (n = 33) to explore vulnerability to HIV/STI and related harms. Interviews were conducted in Khmer by trained interviewers, transcribed and translated into English and analysed for thematic content. RESULTS: The intensification of anti-prostitution and anti-trafficking efforts in Cambodia has increased the number of women working in entertainment venues and on the street. Our results confirm that street-based sex work places women at risk of HIV/STI infection and identify significant environmental risks related to entertainment-based sex work, including limited access to condoms and alcohol-related intoxication. Our data also indicate that exposure to violence and interactions with the police are mediated by the settings in which sex is sold. In particular, transacting sex in environments such as guest houses where there is little or no oversight in the form of peer or managerial support or protection, may increase vulnerability to HIV/STI. CONCLUSIONS: Entertainment venues may also provide a high risk environment for sex work. Our results indicate that strategies designed to address HIV prevention among brothel-based FSWs in Cambodia have not translated well to street and entertainment-based sex work venues in which increasing numbers of women are working. There is an urgent need for targeted interventions, supported by legal and policy reforms, designed to reduce the environmental risks of sex work in these settings. Future research should seek to investigate sex work venues as risk environments, explore the role of different business models in mediating these environments, and identify and quantify exposure to risk in different occupational settings.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".