Infodemiology of public sentiment toward the measles vaccine in Canada: A google trends and health belief model–based analysis, 2025
Bibliographic record
Abstract
Measles remains a public health threat in Canada despite vaccine availability, making effective risk communication essential to deal with vaccine hesitancy. This study, conducted from July through September 2025, analyzed Google Trends data from January 1 to August 31, 2025, to assess public interest in the measles vaccine in Canada and identify regional and thematic patterns in vaccine-related information seeking. Ninety-two queries were analyzed for relative search volumes (RSV) and classified by sentiment and Extended Health Belief Model (EHBM) constructs. RSV values were normalized and aggregated by province. A national peak in searches occurred on March 19. Alberta showed consistently high engagement across sentiment categories, while Manitoba and Saskatchewan exhibited more negative sentiment, suggesting areas for targeted intervention. EHBM analysis showed most queries related to cues to action, followed by perceived barriers, highlighting the need to address motivational and barrier-related concerns. Findings highlight regional differences, reinforcing importance of tailored communication.
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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 | 0.001 |
| Bibliometrics | 0.004 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".