Determinants of vaccine refusal, delay and reluctance in parents of 2-year-old children in Canada: Findings from the 2017 Childhood National Immunization Coverage Survey (cNICS)
Bibliographic record
Abstract
Vaccine hesitancy is a barrier to improving childhood vaccination rates in Canada, but the scope of this problem is unclear due to inconsistent measurement of vaccine uptake indicators. Using 2017 data from a Canadian national vaccine coverage survey, this study analyzed the impact of demographics and parental knowledge, attitudes and beliefs (KAB) on vaccine decisions (refusal, delay and reluctance) in parents of 2-year-old children who had received at least one vaccine. The findings show that 16.8% had refused a vaccine, specifically influenza (73%), rotavirus (13%) and varicella (9%); female parents or those from Quebec or the Territories more likely to refuse. 12.8% were reluctant to accept a vaccine, usually influenza (34%), MMR (21%) and varicella (19%), but eventually accepted them upon advice from a health care provider. 13.1% had delayed a vaccine, usually because their child had health issues (54%) or was too young (18.6%) and was predicted by five or six person households. Recent immigration to Canada decreased likelihood of refusal, delay, or reluctance; however, after 10 years in Canada, these parents were as likely to refuse or be reluctant as parents born in Canada. Poor KAB increased likelihood of refusal and delay by 5 times, and reluctance by 15 times, while moderate KAB increased likelihood of refusal (OR 1.6), delay (OR 2.3) and reluctance (OR 3.6). Future research into vaccine decisions by female and/or single parents, and predictors of vaccine KAB would provide valuable information and help protect our children from vaccine preventable diseases.
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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 | 0.001 | 0.001 |
| 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) | 0.001 | 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".