Determinants of non-vaccination and incomplete vaccination in Canadian toddlers
Bibliographic record
Abstract
Vaccination coverage remains suboptimal in Canada and sporadic outbreaks of vaccine-preventable diseases such as measles and pertussis continue to occur. This study was undertaken to identify sociodemographic determinants of total non-vaccination (having never received any vaccine), non-vaccination for measles (0 doses) and incomplete vaccination for pertussis (< 4 doses) among 2-year-old Canadian children. Data from the 2013 Childhood National Immunization Coverage Survey (CNICS) were used. Associations between sociodemographic factors and outcomes were measured by multiple logistic regressions and adjusted odds ratios (aOR) were calculated. A total of 5,477 children were included in the analyses of total non-vaccination, and 3,899 children were included in the analysis of non-vaccination for measles and incomplete vaccination for pertussis. Overall, 2.7% of children (95% CI 2.0-3.3) had received no vaccine at all. Lower parental education, i.e., the responding parent having a high school diploma, trade certificate or less (compared with university graduation) was associated with total non-vaccination (aOR 1.99, 95% CI 1.02-3.91). Non-vaccination for measles was more frequent among children of single parent families (aOR 1.63, 95% CI 1.01-2.61) and those of parents with lower education (aOR 1.86, 95% CI 1.26-2.76). The odds of incomplete vaccination for pertussis was greater among children born outside Canada (aOR 3.10, 95% CI 1.73-5.58), of parents with lower education (aOR 1.92, 95% CI 1.41-2.62), and those whose household income was between $40,000 and $59,999 (aOR 1.47; 95% CI 1.04-2.07) or lower than $40,000 (aOR 1.58, 95% CI 1.13-2.22). Significant regional variation was also found for all outcomes. In conclusion, despite universal access to free childhood vaccines in Canada, regional variation and socioeconomic inequalities in vaccine uptake were still observed. Further analyses are warranted to identify barriers contributing to these variations.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 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) | 0.002 | 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".