Perceived Stress, Belief in Conspiracy Theories and Anti-vaccination Attitudes in Canadian Sample
Bibliographic record
Abstract
Background: The use of COVID-19 vaccinations to prevent illness has been accepted by approximately 2/3rds of the general Canadian public at the completion of this study (August 2021). Although vaccines are widely accessible in North America, there remains a portion of Canadians who are vaccine hesitant. We hypothesized that a greater tendency to subscribe to conspiracy beliefs in general would be predictive of an anti-vaccination attitude in respect to the COVID-19 vaccine. We also hypothesized the higher levels of perceived stress would be related to a greater tendency to adopt conspiracy-based beliefs. Methods: An online survey was used, and the primary investigator recruited 51 participants from the Vancouver, British Columbia region. The survey consisted of 25 Likert-scale questions and was divided into three sections: vaccination attitude, perceived stress, and generic conspiracy beliefs. Results: The analysis revealed a significant negative regression coefficient between conspiratorial thinking and vaccine attitude (b = -0.465, p < .001) in our participants. All the remaining coefficients were non-significant (p > 0.603). Discussion: The results confirmed the hypothesis of a correlation between vaccination attitudes and general conspiracy beliefs. The secondary hypothesis of a correlation between vaccination attitudes and perceived stress was not supported. The primary investigator explored the role scientific uncertainty plays towards trust and conspiratorial thinking.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| 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) | 0.004 | 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".