Trends in influenza vaccine coverage and vaccine hesitancy in Canada, 2006/07 to 2013/14: results from cross-sectional survey data
Bibliographic record
Abstract
BACKGROUND: Past studies have reported influenza vaccination coverage below national targets, but up-to-date estimates are needed to understand trends and to identify areas for intervention. The objective of this study was to describe recent trends in influenza vaccination in Canada, timing of uptake and reasons for not receiving the vaccine. METHODS: We pooled data from the 2007 to 2014 cycles of the Canadian Community Health Survey. Using bootstrapped survey weights, we examined influenza vaccine coverage by various groups, including by age and by presence of chronic medical conditions. RESULTS: The overall sample included 481 526 respondents. Across all survey cycles combined, 29% of respondents reported receiving seasonal influenza vaccination in the past 12 months. Coverage levels were fairly consistent during the study period, but varied by province or territory. Vaccination coverage decreased over time among those aged 65 years and older. Among those who received a vaccination, it was most common to do so in October or November. Among those not vaccinated, the most frequently cited reason was believing it was unnecessary. INTERPRETATION: Influenza vaccination coverage continues to fall below national targets, with substantial declines seen among those aged 65 years and older, a group for which vaccination is particularly important. More intensive efforts are needed to improve coverage in Canada, particularly for high-risk groups.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".