Reasons for not having received influenza vaccination and its predictors in Canadians
Bibliographic record
Abstract
Yue Chen,1 Jun Wu,2 Qi-long Yi,1 Julie Laroche,3 Thomas Wong21Department of Epidemiology and Community Medicine, Faculty of Medicine, University of Ottawa, 2Professional Guidelines and Public Health Practice Division, Centre for Communicable Diseases and Infection Control, Public Health Agency of Canada, 3Immunization Assessment and Information, Centre for Immunization and Respiratory Infectious Diseases, Public Health Agency of Canada, Ottawa, Ontario, CanadaBackground: Influenza vaccination is the most effective way to prevent influenza. However, only about one-third of Canadians receive an annual seasonal influenza vaccination.Methods: The reasons for not having received influenza vaccination were examined among 131,061 Canadians ≥ 12 years of age who participated in a national survey in 2007–2008. Among them, 127,297 subjects responded to the questions concerning their flu shot history and were grouped into three categories: never (n = 51,767), 1+ year ago (n = 29,310), last year (n = 46,220). Subjects who reported not having had a flu shot during the past year were asked the reasons for not having it. The log binomial regression model was used to estimate prevalence ratios (PRs) and 95% confidence intervals (95% CIs) for the associations of various reasons for not having received influenza vaccination and their predictors.Results: When weighted to the Canadian population, 44.0% had never previously received influenza vaccine and 24.5% had received the vaccine > 12 months ago. The most common reasons for not having received influenza vaccination in the past 12 months were “Respondent did not think it necessary” (71.3%) and “Have not gotten around to it” (17.6%). Log binomial regression analysis shows that females were less likely to report these two reasons compared to males with PRs of 0.98 (0.97, 0.99) and 0.84 (0.81, 0.87), respectively. Younger participants were more likely to report, “Have not gotten around to it.” For those who had an influenza vaccination previously, the primary reason for not having an influenza vaccination in the last year was “Have not gotten around to it.”Conclusions: More than two-thirds of Canadians 12+ years of age did not receive an influenza vaccination in the past year, and “Respondent did not think it necessary” and “Have not gotten around to it” were the main reasons.Keywords: Canada, flu shot, human, influenza, survey, vaccination
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".