Determinants of non-vaccination against seasonal influenza in Canadian adults: findings from the 2015–2016 Influenza Immunization Coverage Survey
Bibliographic record
Abstract
OBJECTIVES: The study objectives were to (1) identify determinants of non-vaccination against seasonal influenza in Canadian adults and (2) examine self-reported reasons for non-vaccination. METHODS: The data source was the 2015-2016 Influenza Immunization Coverage Survey, a national telephone survey of Canadian adults. Participants (n = 1950) were divided into three groups: adults aged 18-64 years with (n = 408) and without (n = 1028) chronic medical conditions (CMC) and adults ≥ 65 years (n = 514). Logistic regression was used to measure associations between sociodemographic factors and non-vaccination for the 2015-2016 influenza season. Weighted proportions were calculated to determine the main self-reported reasons for not receiving the influenza vaccine. RESULTS: Younger age was found to be associated with non-vaccination across all groups. In adults ≥ 65 years, elementary- or secondary- vs. university-level education (aOR 1.87, 95% CI 1.14-3.06) was also significantly associated with non-vaccination. Significant variation in vaccine uptake was found for several sociodemographic factors in adults aged 18-64 without CMC. Low perceived susceptibility or severity of influenza and lack of belief in the vaccine's effectiveness were the most commonly reported reasons for not receiving the vaccine. CONCLUSION: In general, our results were consistent with findings from other Canadian and American studies on seasonal influenza vaccine uptake. Belief that the influenza vaccine is not needed was common, even among those at increased risk of influenza-related complications. Additional research is needed to better understand how sociodemographic factors such as income and education may influence uptake and to raise awareness of potential complications from influenza infection in high-risk adults.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.002 | 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) | 0.002 | 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".