COVID-19 vaccination intention among people who use drugs in France in 2021: results from the international community-based research program EPIC
Bibliographic record
Abstract
BACKGROUND: COVID-19 vaccination is crucial to reduce the incidence of severe forms of the disease in the population. However, people who use drugs (PWUD) face structural and individual barriers to vaccination, and little is known about vaccination intention and factors associated with that intention among PWUD. This study aimed to estimate vaccination intention in PWUD and associated factors in the early stage of vaccination campaigns. METHODS: We conducted cross-sectional study in France among PWUD, as part of the international EPIC program, a community-based research study coordinated by Coalition PLUS. It included 166 unvaccinated PWUD attending harm reduction centers. A questionnaire collected data on sociodemographic characteristics, COVID-19 related difficulties, and mental health, among other things. Multivariate logistic regression was used to identify factors associated with low vaccination intention. RESULTS: Only 19% of participants reported strong intention to get vaccinated against COVID-19. Factors independently associated with low vaccination intention were younger age (aOR = 0.90, 95%CI = 0.85-0.95), lower education level (aOR = 2.67, 95% CI = 0.95-7.55), and unstable housing (aOR = 6.44, 95% CI = 1.59-40.34). The most-cited reasons for low intention were mistrust in COVID-19 vaccines (66.1%), fear of side effects (48.7%), and non-belief in vaccinations in general (25.2%). CONCLUSIONS: This study highlights the need for targeted COVID-19 information and interventions to increase vaccine uptake in PWUD, especially those living in precarity. Community-based interventions and targeted government assistance could play a crucial role in addressing vaccine hesitancy in this population, not only for COVID-19 but for future epidemics.
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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 | 0.008 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".