COVID-19 vaccination intention and vaccine hesitancy among citizens of the Métis Nation of Ontario
Bibliographic record
Abstract
OBJECTIVE: The study objective is to measure the influence of psychological antecedents of vaccination on COVID-19 vaccine intention among citizens of the Métis Nation of Ontario (MNO). METHODS: A population-based online survey was implemented by the MNO when COVID-19 vaccines were approved in Canada. Questions included vaccine intention, the short version of the "5C" psychological antecedents of vaccination scale (confidence, complacency, constraint, calculation, collective responsibility), and socio-demographics. Census sampling via the MNO Registry was used achieving a 39% response rate. Descriptive statistics, bivariate analyses, and multinomial logistic regression models (adjusted for sociodemographic variables) were used to analyze the survey data. RESULTS: The majority of MNO citizens (70.2%) planned to be vaccinated. As compared with vaccine-hesitant individuals, respondents with vaccine intention were more confident in the safety of COVID-19 vaccines, believed that COVID-19 is severe, were willing to protect others from getting COVID-19, and would research the vaccines (Confident OR = 19.4, 95% CI 15.5-24.2; Complacency OR = 6.21, 95% CI 5.38-7.18; Collective responsibility OR = 9.83, 95% CI 8.24-11.72; Calculation OR = 1.43, 95% CI 1.28-1.59). Finally, respondents with vaccine intention were less likely to let everyday stress prevent them from getting COVID-19 vaccines (OR = 0.47, 95% CI 0.42-0.53) compared to vaccine-hesitant individuals. CONCLUSION: This research contributes to the knowledge base for Métis health and supported the MNO's information sharing and educational activities during the COVID-19 vaccines rollout. Future research will examine the relationship between the 5Cs and actual uptake of COVID-19 vaccines among MNO citizens.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".