How Vaccine Ambivalence Can Lead People Who Inject Drugs to Decline COVID-19 Vaccination and Ways This Can Be Addressed: Qualitative Study
Bibliographic record
Abstract
BACKGROUND: People who inject drugs are disproportionately impacted by SARS-CoV-2 and COVID-19, yet they do not frequently accept vaccination against SARS-CoV-2 when offered. OBJECTIVE: This study aimed to explore why people who inject drugs decline free vaccines against SARS-CoV-2 and how barriers to vaccination can potentially be addressed. METHODS: We conducted semistructured qualitative interviews with 17 unvaccinated adult persons who inject drugs during August and September 2021 at a New York City syringe service program, where approximately three-fourth of participants identified as Latino (55%) or African American (22%). Interviews lasted roughly 20 minutes. The interview guide examined reasons for declining vaccination, participants' understanding of COVID-19 risks, and how messages could be developed to encourage vaccine uptake among people who inject drugs. RESULTS: Participants acknowledged that they faced increased risk from SARS-CoV-2 owing to their injection drug use but feared that long-term substance use may have weakened their health, making them especially vulnerable to side effects. Fears of possible side effects, compounded by widespread medical mistrust and questions about the overall value of vaccination contributed to marked ambivalence among our sample. The desire to protect children and older family members emerged as key potential facilitators of vaccination. CONCLUSIONS: Community-developed messages are needed in outreach efforts to explain the importance of vaccination, including the far greater dangers of COVID-19 compared to possible unintended side effects. Messages that emphasize vaccines' ability to prevent inadvertently infecting loved ones, may help increase uptake. Community-focused messaging strategies, such as those used to increase HIV and hepatitis C virus testing and overdose prevention among people who inject drugs, may prove similarly effective.
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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 | 0.016 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.010 | 0.010 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".