Identifying barriers and facilitators to COVID-19 vaccination uptake among People Who Use Drugs in Canada: a National Qualitative Study
Bibliographic record
Abstract
BACKGROUND: People Who Use Drugs (PWUD) have lower vaccination uptake than the general population, and disproportionately experience the burden of harms from vaccine-preventable diseases. We conducted a national qualitative study to: (1) identify the barriers and facilitators to receiving COVID-19 vaccinations among PWUD; and (2) identify interventions to support PWUD in their decision-making. METHODS: Between March and October 2022, semi-structured interviews with PWUD across Canada were conducted. Fully vaccinated (2 or more doses) and partially or unvaccinated (1 dose or less) participants were recruited from a convenience sample to participate in telephone interviews to discuss facilitators, barriers, and concerns about receiving COVID-19 vaccines and subsequent boosters, and ways to address concerns. A total of 78 PWUD participated in the study, with 50 participants being fully vaccinated and 28 participants partially or unvaccinated. Using thematic analysis, interviews were coded based on the capability, opportunity, and motivation-behavior (COM-B) framework. RESULTS: Many partially or unvaccinated participants reported lacking knowledge about the COVID-19 vaccine, particularly in terms of its usefulness and benefits. Some participants reported lacking knowledge around potential long-term side effects of the vaccine, and the differences of the various vaccine brands. Distrust toward government and healthcare agencies, the unprecedented rapidity of vaccine development and skepticism of vaccine effectiveness were also noted as barriers. Facilitators for vaccination included a desire to protect oneself or others and compliance with government mandates which required individuals to get vaccinated in order to access services, attend work or travel. To improve vaccination uptake, the most trusted and appropriate avenues for vaccination information sharing were identified by participants to be people with lived and living experience with drug use (PWLLE), harm reduction workers, or healthcare providers working within settings commonly visited by PWUD. CONCLUSION: PWLLE should be supported to design tailored information to reduce barriers and address mistrust. Resources addressing knowledge gaps should be disseminated in areas and through organizations where PWUD frequently access, such as harm reduction services and social media platforms.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".