The Use of Information and Communication Technologies by Sex Workers to Manage Occupational Health and Safety: Scoping Review
Bibliographic record
Abstract
BACKGROUND: In many countries, sex work is criminalized, driving sex work underground and leaving sex workers vulnerable to a number of occupational health and safety risks, including violence, assault, and robbery. With the advent of widely accessible information and communication technologies (ICTs), sex workers have begun to use electronic occupational health and safety tools to mitigate these risks. OBJECTIVE: This study aims to explore the use of ICTs by sex workers for managing occupational health and safety risks and strategies for reducing these risks. This paper aims to answer the following question: what is known about sex workers' use of ICTs in the delivery of occupational health and safety strategies? METHODS: A literature review following the methodological framework for scoping reviews was conducted to analyze studies describing the use of ICTs by sex workers to mitigate occupational health and safety risks. Experimental, observational, and descriptive studies, as well as protocol papers, were included in this scoping review. RESULTS: Of the 2477 articles initially identified, 41 (1.66%) met the inclusion criteria. Of these studies, 71% (29/41) were published between 2015 and 2019. In these studies, the internet was the predominant ICT (24/41, 58%), followed by text messaging (10/41, 24%) and assorted communication technologies associated with mobile phones without internet access (7/41, 17%; eg, voice mail). In 56% (23/41) of the studies, sex workers located in high-income countries created occupational health and safety strategies (eg, bad date lists) and shared them through the internet. In 24% (10/41) of the studies, mostly in low- and middle-income countries, organizations external to sex work developed and sent (through text messages) occupational health and safety strategies focused on HIV. In 20% (8/41) of the studies, external organizations collaborated with the sex worker community in the development of occupational health and safety strategies communicated through ICTs; through this collaboration, concerns other than HIV (eg, mental health) emerged. CONCLUSIONS: Although there has been an increase in the number of studies on the use of ICTs by sex workers for managing occupational health and safety over the past 5 years, knowledge of how to optimally leverage ICTs for this purpose remains scarce. Recommendations for expanding the use of ICTs by sex workers for occupational health and safety include external organizations collaborating with sex workers in the design of ICT interventions to mitigate occupational health and safety risks; to examine whether ICTs used in low- and middle-income countries would have applications in high-income countries as a substitute to the internet for sharing occupational health and safety strategies; and to explore the creation of innovative, secure, web-based communities that use existing or alternative digital technologies that could be used by sex workers to manage their occupational health and safety.
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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 | 0.045 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.018 | 0.016 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".