The good, the bad, and the ugly: A qualitative evaluation of web-based COVID-19 vaccine communication in Canada
Bibliographic record
Abstract
Poor website accessibility and usability with credible website/information sources can create barriers to the equitable uptake of vaccines. Scarce research investigates how intended users interact with and perceive official COVID-19 websites. We examine how people in Canada interact with official COVID-19 vaccine websites and how they use information to inform their choices regarding COVID-19 vaccinations. Using a qualitative design and talk-aloud (also called 'think-aloud') method, we conducted interviews with 50 general population individuals residing in 3 provinces in Canada in July-December 2021, during which they navigated specific government websites and attempted to find information on various aspects of COVID-19. During the interviews, participants were given specific tasks (e.g. searching for specific information on the websites) and asked to 'think aloud' while performing them. Thematic content was used to identify positive and negative elements regarding the websites that were stated by participants as they navigated the websites. Our analysis demonstrated that participants appreciated websites that featured user-friendly and aesthetically pleasing designs, had local and updated information, offered links to reputable sources, and dispelled misconceptions. Participants also critiqued sites for using technical jargon, presenting seemingly insufficient information, and potentially having conflicts of interest. These findings underline the need for health authorities to prioritize web-based communication and understand the information needs of their audience. Ignoring user preferences raises potential risks of poor communication, such as leaving their citizens seeking information elsewhere.
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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 | 0.026 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.026 | 0.018 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| 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".