Parent and family characteristics associated with reported pediatric influenza vaccination in a sample of Canadian digital vaccination platform users. An exploratory, cross-sectional study in the 2018-2019 influenza season
Bibliographic record
Abstract
Seasonal vaccination remains one of the best interventions to prevent morbidity and mortality from influenza in children. Understanding the characteristics of parents who vaccinate their children can inform communication strategies to encourage immunization. Using a cross-sectional study, we described parental characteristics of people who reported vaccinating their children against influenza during 2018/2019 in a cohort of Canadian digital immunization record users. Data was collected from a free, Pan-Canadian digital vaccination tool, CANImmunize. Eligible accounts contained at least one parental and one "child/dependent" record. Each parental characteristic (gender, age, family size, etc) was tested for association with pediatric influenza vaccination, and a multivariate logistic regression model was fit. A total of 6,801 CANImmunize accounts met inclusion criteria. After collapsing the dataset, the final sample contained 11,381 unique dyads. Influenza vaccination was reported for 32.3% of the children and 42.0% of the parents. In the multivariate logistic regression analysis, parents receiving the seasonal influenza vaccine were most strongly associated with reporting pediatric influenza vaccination (OR 17.05, 95% CI 15.08, 19.28). Having a larger family size and fewer transactions during the study period was associated with not reporting pediatric influenza vaccination. While there are several limitations to this large-scale study, these results can help inform future research in the area. Digital technologies may provide a unique and valuable source of vaccine coverage data and to explore associations between individual characteristics and immunization behavior. Policy makers considering digital messaging may want to tailor their efforts based on parental characteristics to further improve pediatric seasonal influenza 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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".