Uptake of SARS-CoV-2 vaccination among structurally-marginalized people who use drugs in Vancouver, Canada
Bibliographic record
Abstract
We sought to evaluate the rates and predictors of SARS-CoV-2 vaccination among members of a structurally-marginalized population of people who use drugs (PWUD) during a targeted, community-wide, vaccination campaign in Vancouver, Canada. Interviewer-administered data were collected from study participants between June 2021 and March 2022. Generalized estimating equation analysis was used to identify factors associated with SARS-CoV-2 vaccine uptake, ascertained through a province-wide vaccine registry. Among 223 PWUD, 107 (48.0%) reported receipt of at least two SARS-CoV-2 vaccine doses at baseline and this increased to 151 (67.7%) by the end of the study period. Using social media as a source of vaccine information was negatively associated with SARS-CoV-2 vaccine uptake (Adjusted odds ratio [AOR] 0.27, 95% confidence interval [CI] 0.09-0.81) and HIV seropositivity (AOR 2.68, 95% CI 1.12-6.39) and older age (AOR 1.27, 95% CI 1.07-1.51) were positively associated with SARS-CoV-2 vaccine uptake. These findings suggest that the targeted vaccination campaign in Vancouver may be an effective model to promote SARS-CoV-2 vaccination in other jurisdictions. However, using social media as a source of vaccine information likely reduced SARS-CoV-2 vaccine uptake in PWUD arguing for further efforts to promote accessible and evidence-based vaccine information among marginalized populations.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".