Exploring vaccine hesitancy among citizens of the Métis Nation of Ontario: a population-based data linkage study
Bibliographic record
Abstract
Objective and ApproachUnderstanding vaccine hesitancy among distinct communities is crucial to ensuring equitable uptake of vaccination. The objective was to examine the influence of psychological antecedents of vaccine uptake, known as the “5Cs”, on COVID-19 vaccination among Métis Nation of Ontario (MNO) citizens. The Métis people are one of three Indigenous peoples in Canada. A population-based online survey was implemented by the MNO, including the short version of the “5C” (confidence, complacency, constraint, calculation, collective responsibility) psychological antecedents of vaccination scale. Census sampling achieved a 39% response rate and respondents were linked to the COVID-19 vaccine database (COVAX) in Ontario (n=4,012). Analyses included logistic regression models (adjusted for sociodemographics) and exploratory latent class analyses. ResultsIn logistic regression models, MNO citizens who were less confident that COVID-19 vaccines were safe (OR=0.04; 95% CI=0.03-0.06) and did not agree vaccination was a collective action to prevent the spread of disease (OR=0.11, 95% CI: 0.07-0.17) had lower odds of being vaccinated. MNO citizens who disagreed the risk of COVID-19 was small (OR=14.87, 95% CI: 9.99-22.13) had a higher odds of being vaccinated. Vaccine hesitancy profiles with (i) lower confidence in availability/safety of COVID-19 vaccines, (ii) lower perceptions about severity of COVID-19 and (iii) higher everyday life constraints/stressors were associated with lower vaccination rates (males, OR: 4.69; CI: 3.18–6.92; females, OR: 4.77, CI: 3.03–7.51) in latent class analyses. Conclusions and ImplicationsMétis-specific analyses enabled a community-driven response, resulting in low vaccine hesitancy and high uptake.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.007 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| 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".