Social-structural barriers to primary care among sex workers: findings from a community-based cohort in Vancouver, Canada (2014–2021)
Bibliographic record
Abstract
BACKGROUND: Due to social-structural marginalization, sex workers experience health inequities including a high prevalence of sexually transmitted and blood-borne infections, mental health disorders, trauma, and substance use, alongside a multitude of barriers to HIV and substance use services. Given limited evidence on sex workers' broader primary healthcare access, we aimed to examine social-structural factors associated with primary care use among sex workers over 7 years. METHODS: Data were derived from An Evaluation of Sex Workers Health Access (AESHA), a community-based open prospective cohort of women (cis and trans) sex workers in Metro Vancouver, from 2014 to 2021. Descriptive statistics were used to summarize the proportion of primary care use in the past six months and to assess primary care trends over time from 2014-2021. We used multivariate logistic regression with generalized estimating equations (GEE) to identify social-structural factors associated with primary care access (seeing a family doctor in the last six months), after adjusting for confounders. RESULTS: Amongst the 646 participants, most (87.4%) accessed primary care at some point during the study period, and primary care use in the last 6 months was relatively stable (ranging from 60-78%) across each follow-up period. At first available observation, participants faced a high burden of sexually transmitted and blood-borne infections (STBBIs) (48.0%, 11.5%, and 10.4% were HCV, HIV, or STI seropositive, respectively), 56.8% were diagnosed with a mental health disorder, 8.1% had recently overdosed, and 14.7% were recently hospitalized. In multivariable GEE analysis, exposure to intimate partner violence was associated with reduced primary care use (Adjusted odds ratios (AOR) 0.63, 95% Confidence interval (CI): 0.49-0.82), and limited English fluency was marginally associated (AOR 0.76 CI: 0.51-1.14). CONCLUSIONS: This study characterized primary care use and its social-structural determinants among sex workers over 7 years. Participants faced a high burden of STBBIs and other health disparities, and a proportion faced gaps in primary care utilization. Scale-up of trauma-informed, culturally and linguistically tailored, sex worker-friendly primary care models are needed, alongside structural interventions to decriminalize and destigmatize sex work and substance use.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".