A review of Canadian online resources providing information on COVID-19 vaccination for caregivers of children aged 5–11 years
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Vaccination is one of the most searched health topics online, yet the quality of resources varies considerably. This study evaluated the quality of Canadian COVID-19 vaccines online resources for caregivers of 5-11-year-old children. METHODS: We reviewed Canadian public-facing websites from academic pediatric hospitals, governments, professional organizations, and public health authorities until April 22, 2022. Inclusion criteria included English/French resources targeting caregivers of 5-11-year-olds, presented as webpages, FAQs, posters/infographics, and/or videos. Reliability, readability, and understandability/actionability were appraised using the JAMA Benchmark, Flesch-Kincaid Grade Level, and Patient Education Material Assessment Tool for Printable/Audiovisual materials, respectively. We used a content checklist to assess key vaccine topics (e.g., effectiveness and safety). Descriptive statistics included Fisher's exact and ANOVA tests. RESULTS: Of 1046 websites screened, 43 primary webpage clusters and 141 secondary webpages were analyzed. Twenty (46.5 %), 9 (20.9 %), 7 (16.3 %), and 7 (16.3 %) primary webpage clusters belonged to government, academic pediatric hospitals, professional organizations, and public health authorities, respectively. The mean JAMA Benchmark score was 3.47 ± 0.55 (out of 43). Of 43 clusters, only five (11.6 %) scored at or below a US 6th-grade education level. While 42/43 (97.7 %) primary clusters including printable materials were understandable (PEMAT-P > 70 %), only 7/43 (16.3 %) were considered actionable. The mean content score was 12.65 ± 3.60 (out of 20) among the 43 primary clusters. No differences in quality were seen across organization types, except for actionability (p = 0.016). CONCLUSIONS: Although most Canadian webpages on COVID-19 vaccines received high scores in understandability, areas requiring improvement in actionability, readability, and content were identified.
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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 | 0.009 | 0.051 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.024 | 0.032 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".