Determinants of non-vaccination against seasonal influenza.
Bibliographic record
Abstract
BACKGROUND: In Canada, vaccine coverage for seasonal influenza remains below targets. Few studies have sought to determine the sociodemographic factors associated with non-vaccination using a Canada-wide survey. This study aims to identify the determinants of, and the reasons for, non-vaccination. DATA AND METHODS: Data from the 2013/2014 Canadian Community Health Survey (CCHS) were used. Respondents were divided into three groups: adults aged 18 to 64 years with a chronic medical condition (CMC), adults in the same age group with no CMC, and adults aged 65 years and older. Logistic regressions were used to measure the association between sociodemographic factors and non-vaccination. RESULTS: Among adults aged 65 years and older, the proportion of non-vaccinated persons was 36.2%. This proportion was higher among adults aged 18 to 64 years with a CMC and those with no CMC (62.2% and 77.8%, respectively). Factors independently associated with non-vaccination in all groups included being young, having a lower level of education, and not having a family doctor. Among adults aged 65 years and older and 18 to 64 years with a CMC, excellent self-perceived health was also associated with non-vaccination. The belief that the vaccine is not necessary was the most common reason for non-vaccination. DISCUSSION: Too few Canadians get the influenza vaccine. The main reasons for not getting vaccinated have more to do with personal decision than barriers to access. This illustrates the ongoing need to inform the public about the importance of the vaccine and the risks associated with influenza.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".