How religious and political affiliation influence belief in COVID-19 vaccine myths in Canada & the United States
Bibliographic record
Abstract
Using survey data from the CIHR-funded three country project "COVID-19's Differential Impact on Indigenous Peoples and Newcomers: A Socioeconomic Analysis of Canada, USA and Mexico", this thesis explores two central questions: How are community connections affecting COVID-19 vaccination rates, and how are these connections affecting our belief in COVID-19 myths? The study's findings reveal that our social connections, political and religious affiliations, social media usage, trust in institutions, and our social circles, play a significant role in shaping perceptions of vaccines and myths regarding coronavirus. Although political divisions affect vaccine uptake and myth beliefs in both countries, this pattern is stronger in the USA. Social media has also polarized opinions and has influenced vaccine uptake in both countries. The thesis employs social constructionism to explain how social interactions and connections shape our perceptions of reality. Additionally, it draws on political culture theory to analyze how political beliefs influence various facets of our lives, including responses to public health crises. The thesis concludes by providing critical data and results that can assist government officials, epidemiologists and policymakers to bridge social divides and develop strategies to manage future pandemics better.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".