Burden and correlates of mental health diagnoses among sex workers in an urban setting
Bibliographic record
Abstract
BACKGROUND: Women involved in both street-level and off-street sex work face disproportionate health and social inequities compared to the general population. While much research has focused on HIV and sexually transmitted infections (STIs) among sex workers, there remains a gap in evidence regarding the broader health issues faced by this population, including mental health. Given limited evidence describing the mental health of women in sex work, our objective was to evaluate the burden and correlates of mental health diagnoses among this population in Vancouver, Canada. METHODS: An Evaluation of Sex Workers Health Access (AESHA) is a prospective, community-based cohort of on- and off-street women in sex work in Vancouver, Canada. Participants complete interviewer-administered questionnaires semi-annually. We analyzed the lifetime burden and correlates of self-reported mental health diagnoses using bivariate and multivariable logistic regression. RESULTS: Among 692 sex workers enrolled between January 2010 and February 2013, 338 (48.8%) reported ever being diagnosed with a mental health issue, with the most common diagnoses being depression (35.1%) and anxiety (19.9%). In multivariable analysis, women with mental health diagnoses were more likely to identify as a sexual/gender minority (LGBTQ) [AOR=2.56, 95% CI: 1.72-3.81], to use non-injection drugs [AOR=1.85, 95% CI: 1.12-3.08], to have experienced childhood physical/sexual trauma [AOR=2.90, 95% CI: 1.89-4.45], and work in informal indoor [AOR=1.94, 95% CI: 1.12 - 3.40] or street/public spaces [AOR=1.76, 95% CI: 1.03-2.99]. CONCLUSIONS: This analysis highlights the disproportionate mental health burden experienced by women in sex work, particularly among those identifying as a sexual/gender minority, those who use drugs, and those who work in informal indoor venues and street/public spaces. Evidence-informed interventions tailored to sex workers that address intersections between trauma and mental health should be further explored, alongside policies to foster access to safer workspaces and health services.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| 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".