High COVID-19 vaccine uptake following initial hesitancy among people in Australia who inject drugs
Bibliographic record
Abstract
BACKGROUND: Previous studies have reported high COVID-19 vaccine hesitancy among people who inject drugs. We aimed to examine COVID-19 vaccine coverage, motivations and barriers to vaccination, and factors associated with uptake among this population in Australia, 1.5 years after vaccine rollout commenced. METHODS: In June-July 2022, 868 people (66.0 % male, mean age 45.6 years) who regularly inject drugs and reside in an Australian capital city reported the number of COVID-19 vaccine doses they had received and their primary motivation (if vaccinated) or barrier (if unvaccinated) to receive the vaccine. We compared vaccine uptake to Australian population estimates and used logistic regression to identify factors associated with ≥ 2 dose and ≥ 3 dose uptake. RESULTS: Overall, 84.1 % (n = 730) had received at least one COVID-19 vaccine dose, 79.6 % (n = 691) had received ≥ 2 doses, and 46.1 % (n = 400) had received ≥ 3 doses. Participants were less likely to be vaccinated than the Australian general population (prevalence ratio: 0.82, 95 % confidence interval [CI]: 0.76-0.88). Key motivations to receive the vaccine were to protect oneself or others from COVID-19, while barriers pertained to vaccine or government distrust. Opioid agonist treatment (adjusted odds ratio [aOR]: 2.49, 95 % CI: 1.44-4.42), current seasonal influenza vaccine uptake (aOR: 6.76, 95 % CI: 3.18-16.75), and stable housing (aOR: 1.58, 95 % CI: 1.02-2.80) were associated with receipt of at least two vaccine doses. Participants aged ≥ 40 years (versus < 40 years; aOR: 1.66, 95 % CI: 1.10-2.53) or who reported a chronic health condition (aOR: 1.71, 95 % CI: 1.18-2.47) had higher odds of receiving at least three vaccine doses. CONCLUSION: We observed higher COVID-19 vaccine uptake than expected given previous studies of vaccine acceptability among people who inject drugs. However, it was lower than the general population. People who inject drugs and reside in unstable housing are a subpopulation that require support to increase vaccine uptake.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".