Understanding the interplay of factors informing vaccination behavior in three Canadian provinces
Bibliographic record
Abstract
Arguably, the two most critical components in any response to a pandemic are effective risk communication and the rapid development of a vaccine. Despite the roll-out of a publicly-funded H1N1 vaccine program across the country, less than half of all Canadians were vaccinated during the 2009-10 pandemic. Using focus group data, this study examined vaccinating behaviors, the impact of public health messaging, and the public's attitudes toward H1N1 and the H1N1 vaccine in three Canadian provinces. Drawing on vaccine risk communication literature, a framework was devised to identify and analyze the factors related to vaccine uptake and vaccine refusal. The most predictive factor for H1N1 vaccine uptake was a prior history of vaccinating against seasonal influenza. Other important factors included barriers to immunizing (access issues) and an individual's perception of serious risk from contracting H1N1. Although critical gaps in the public's understanding of influenza infections were identified, together with misinformation about vaccination effectiveness and safety, these factors were less frequently reported to be the core factors influencing an individual's decision to vaccinate.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 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".