The impact of mental health and substance use issues on COVID-19 vaccine readiness: a cross sectional community-based survey in Ontario, Canada
Bibliographic record
Abstract
Abstract Background COVID-19 vaccines have been approved for use in Canada since December 2020. However, data about factors associated with vaccine hesitancy and the impact of mental health and/or substance use (MHSU) issues on vaccine uptake are currently not available. The goal of this study was to explore factors, particularly MHSU factors, that impact COVID-19 vaccination intentions in Ontario, Canada. Methods A community-based cross-sectional survey with recruitment based on age, gender, and geographical location (to ensure a representative population of Ontario), was conducted in February 2021. Multinomial logistic regression was used to test the relationship between COVID-19 vaccination status and plans and sociodemographic background, social support, anxiety about contracting COVID-19, and MHSU concerns. Results Of the total sample of 2528 respondents, 1932 (76.4%) were vaccine ready, 381 (15.1%) were hesitant, and 181 (7.1%) were resistant. Significant independent predictors of vaccine hesitancy compared with vaccine readiness included younger age (OR=2.11, 95%CI=1.62-2.74), female gender (OR=1.36, 95%CI=1.06-1.74), Black ethnicity (OR=2.11, 95%CI=1.19-3.75), lower education (OR=1.69, 95%CI=1.30-2.20), lower SES status (OR=.88, 95%CI=.84-.93), lower anxiety about self or someone close contracting COVID-19 (OR=2.06, 95%CI=1.50-2.82), and lower depression score (OR=.90, 95%CI=.82-.98). Significant independent predictors of vaccine resistance compared with readiness included younger age (OR=1.72, 95%CI=1.19-2.50), female gender (OR=1.57, 95%CI=1.10-2.24), being married (OR=1.50, 95%CI=1.04-2.16), lower SES (OR=.80, 95%CI=.74-.86), lower satisfaction with social support (OR=.78, 95%CI=.70-.88), lower anxiety about contracting COVID-19 (OR=7.51, 95%CI=5.18-10.91), and lower depression score (OR=.85, 95%CI=.76-.96). Interpretation COVID-19 vaccination intention is affected by sociodemographic factors, anxiety about contracting COVID-19, and select mental health issues.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.003 | 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".