Determinants of influenza vaccination among a large adult population in Quebec
Bibliographic record
Abstract
OBJECTIVES: Very low uptake has been noted for influenza vaccination in the province of Quebec. This study aimed to identify the determinants of influenza vaccination among a large regional population. METHODS: A telephone survey was administered to a random digit sample in the Eastern Townships region (Quebec, Canada). Respondents were asked questions on several health topics such as perceived knowledge and beliefs about influenza immunization, medical consultations, perceived health status and life habits. Significant variables in the univariate analysis were introduced into a multivariate logistic regression model to determine independent factors for having received the influenza vaccine (aOR and 95% CI) among adults aged ≥60 years and younger adults with ≥1 chronic condition. RESULTS: A total of 4,620 interviews were analyzed. Among the target groups, 55.4% of adults aged ≥60 and 32.2% of adults aged 18-59 with at least one chronic disease had received the influenza vaccine during the 2013-2014 season. Several determinants were significantly associated with influenza vaccination in both groups such as having received a recommendation from a healthcare professional. Among adults aged ≥60, not having consulted a chiropractor over the last 12 months (aOR = 2.37; 1.09-5.19), non-smokers (aOR = 1.78; 1.22-2.59) and self-perceived poor health status (aOR = 1.45; 1.01-2.06) were significantly linked to flu vaccination. In the younger group, influenza vaccination was independently associated to low alcohol consumption (aOR = 2.14; 1.13-4.05) and being overweight (aOR = 1.63; 1.12-2.38). CONCLUSIONS: Many determinants influence the decision to get vaccinated against influenza. Specific messages should be tailored for high-risk groups to effectively increase influenza vaccine coverage.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".