Characterizing the Use of Healthcare Access Supports Among People Who Use Drugs in Vancouver, Canada, 2017 to 2020: A Cohort Study
Bibliographic record
Abstract
Background: For structurally marginalized populations, including people who use drugs (PWUD), equitable access to healthcare can be achieved through healthcare access supports. However, few studies characterized utilization of formal (eg, outreach workers, healthcare professionals) and informal (eg, friends/family) supports. Therefore, we sought to estimate the prevalence of and factors associated with receiving each type of support among PWUD. Methods: We used data from 2 prospective cohort studies of PWUD in Vancouver, Canada, in 2017 to 2020. We constructed separate multivariable generalized linear mixed-effects models to identify factors associated with receiving each of the 3 types of supports (ie, healthcare professionals, outreach workers/peer navigators, and informal supports) compared to no supports. Results: Of 996 participants, 350 (35.1%) reported receiving supports in the past 6 months at baseline, through informal supports (6.2%), outreach workers (14.1%), and healthcare professionals (20.9%). In multivariable analyses, HIV positivity, chronic pain, and avoiding healthcare due to the past mistreatment were positively associated with receiving supports from each of healthcare professionals and outreach workers. Men were less likely to receive any types of the supports (all P < .05). Conclusions: Utilization of healthcare access supports was relatively low in this sample. However, formal supports appeared to have reached PWUD exhibiting more comorbidities and experiencing discrimination in healthcare. Further efforts to make formal supports more available would benefit PWUD with unmet healthcare needs, particularly men.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| 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".