Identifying Barriers and Enablers to Vaccine uptake from Immunizers and Individuals Receiving a COVID-19 Vaccine in Saskatchewan
Bibliographic record
Abstract
Background: Vaccine hesitancy presents a challenge to public health, especially during a global pandemic. Understanding reasons for vaccine hesitancy in local populations may help policymakers and public health practitioners increase vaccine uptake. Objective: We surveyed individuals receiving a COVID-19 vaccine and immunizers in Saskatchewan, Canada and categorized their responses according to the Theoretical Domains Framework. This provides policymakers with evidence-based suggestions for behaviour change interventions that may promote vaccine uptake among hesitant individuals. Methods: Two online surveys were developed to better understand vaccine hesitancy in Saskatchewan: one aimed at individuals receiving a vaccination and the other at immunizers. Both surveys were available for a one-week period when vaccination uptake had plateaued in Saskatchewan. Individuals receiving a vaccine were asked what made them decide to get a vaccine, and both groups were asked what they thought would promote vaccine uptake among hesitant individuals. Responses were analyzed thematically based on the Theoretical Domains Framework and reported descriptively. Results: Individuals receiving a COVID-19 vaccine indicated that mandates and restrictions and having a positive attitude toward COVID-19 vaccines were the most common reasons for receiving a vaccine. Immunizers most frequently indicated that media issues led to vaccine hesitancy and that having access to, and trust in, reputable information sources would enable more vaccine hesitant individuals to seek a COVID-19 vaccination. Conclusion: Mandates and restrictions, promoting positive attitudes towards vaccines, and ensuring people have access to, and trust in, reputable information sources, are key enablers for promoting vaccine uptake among vaccine hesitant individuals.
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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 | 0.019 | 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 | 0.006 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".