Development and application of criteria for analyzing COVID-19 vaccine websites in Canada and the United States
Bibliographic record
Abstract
Background: From early in the COVID-19 pandemic, it was clear that vaccines would be necessary to stop the spread and severity of the virus. Many jurisdictions established COVID-vaccine-specific websites or web content to help inform their citizens about the vaccines. Although some criteria exist for analyzing health-related communication, there are few clear evaluative frameworks specifically for analyzing websites and even fewer specifically for vaccine information. Objective: Based on existing criteria and best practices in public health communication, my study aims to develop more comprehensive criteria for analyzing official, public-facing, public health/government websites about vaccination and then apply those criteria to select COVID-19 vaccine websites. Methods: I use content analysis methodology to develop vaccine website criteria and to evaluate websites. Using pre-existing frameworks and evaluative tools, incorporating current best practices in risk communication, and consulting with experts in the field, I developed a concise set of criteria for vaccine websites. I used these criteria to evaluate seven websites from Canada and the United States. Results: From my analysis, I identified ten criteria (functionality, accessibility, authorship, purpose, funding source, privacy policy, content quality, currency, unbiased, references) and organized them into three themes (usable, transparent, and helpful) and three tiers of website navigation. My application of these criteria against seven websites showed that individual websites scored well in a few criteria. However, there is much room for improvement, particularly in ensuring that information is unbiased, being transparent about funding sources, and ensuring that websites are accessible. Discussion and Conclusion: Fifteen recommendations are provided to support public health organizations in communicating with the public to help them make informed decisions to stay safe and healthy. These recommendations and the criteria on which they are based are intertwined and inextricable from their greater context. Consequently, these recommendations would be most effective when combined with other risk communication interventions.
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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 | 0.043 | 0.133 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.032 | 0.025 |
| Science and technology studies | 0.009 | 0.005 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| 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".