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Record W4411339926 · doi:10.1371/journal.pgph.0004647

Everyday discrimination and barriers to primary care, mental health, and substance use services: Findings from a community-based cohort of sex workers in Vancouver, Canada (2015–2024)

2025· article· en· W4411339926 on OpenAlexafffundabout
Kirstin Kielhold, Kate Shannon, Charlie Zhou, Kaylee Ramage, Eileen V. Pitpitan, Andrea Krüsi, Jennie Pearson, Shira M. Goldenberg

Bibliographic record

VenuePLOS Global Public Health · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicSex work and related issues
Canadian institutionsSimon Fraser UniversityUniversity of British Columbia
FundersNational Institute on Drug AbuseCanadian Institutes of Health ResearchNational Institutes of Health
KeywordsMental healthOddsLogistic regressionMedicineCohortOdds ratioGeneralized estimating equationHealth carePsychiatryFamily medicineGerontologyDemography

Abstract

fetched live from OpenAlex

We evaluated the association between discrimination and access to primary, mental health, and substance use services among sex workers. Using baseline and semi-annual questionnaire data from a community-based cohort of sex workers in Vancouver, Canada (09/2015-02/2024), we used bivariate and multivariable logistic regression with generalized estimating equations to analyze the relationship between discrimination and access to primary care, mental health, and substance use services. Among 518 participants (2768 observations), the median discrimination score was 19 (IQR:11-25), indicating substantial discrimination. In separate multivariate models, every one-point increase in discrimination was associated with increased odds of experiencing barriers to health services (adjusted odds ratio (AOR):1.03, 95%CI:1.02-1.04), unable to access health services when needed (AOR:1.03, 95%CI:1.01-1.04), unmet need for mental health services (AOR:1.04, 95%CI:1.03-1.06), experiencing barriers to counseling for sexual trauma (AOR:1.04, 95%CI:1.02-1.05), and unmet need for substance use treatment (AOR:1.07, 95%CI:1.04-1.09). Discrimination is highly prevalent and associated with reduced access to primary, mental health, and substance use services among sex workers. There is a need for anti-discrimination efforts, including provider training and sex worker partnerships in primary care, alongside policy reforms.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.498
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.018
GPT teacher head0.286
Teacher spread0.268 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations2
Published2025
Admission routes3
Has abstractyes

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