Predictors and impact of trust on vaccine decisions in parents of 2-year-old children in Canada: findings from the 2017 Childhood National Immunization Coverage Survey (cNICS)
Bibliographic record
Abstract
Trust is known to be an important factor in vaccine decisions for parents of young children, but there has been a lack of Canadian data measuring the determinants and impact of trust. Using data from the 2017 Canadian Childhood National Immunization Coverage Survey (cNICS), this study analyzed the relationships between sources that parents trust for vaccine information and demographics, parental knowledge, attitudes, and beliefs (KAB) and vaccine decisions (refusal, delay or reluctance) in parents of 2-year-old children who had accepted at least one vaccine for their child (n = 6125). The findings show that 83% of parents trust doctors for vaccine information; 70-80% trust pharmacists, PMH, nurses and HC/PHAC; 34% trust family and 23% trust friends and CAM HCPs. However, parents found to have poor or moderate KAB were less likely to trust doctors, nurses, pharmacists, PMH and HC/PHAC. Parents were also less likely to trust the PMH or HC/PHAC if they had high school education or less or trade/college education, or were widowed, separated, or divorced. Parents who had never been reluctant to vaccinate their 2-year-old child were over 2 times more likely to trust doctors, nurses, pharmacists, PMH and HC/PHAC while parents who trusted family and friends were less likely to delay or refuse vaccines. There was also significant regional variation within Canada, with parents from Quebec most likely to trust doctors, nurses, pharmacists, friends, PMH and HC/PHAC. Parents from the Territories were less likely to trust doctors, nurses and pharmacists, but more likely to trust family. Parents were less likely to trust doctors if they were from the Prairies, and pharmacists if they were from BC, and parents from the Prairies and BC were less likely to trust HC/PHAC. Parents from Ontario were less likely to trust family or friends, but more likely to trust the PMH. Tailored vaccine campaigns are needed to account for educational, marital, and regional differences across Canada to improve vaccine uptake.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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) | 0.001 | 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".