A comparative systematic scan of COVID-19 health literacy information sources for Canadian university students
Bibliographic record
Abstract
INTRODUCTION: With the rapid spread of online coronavirus-related health information, it is important to ensure that this information is reliable and effectively communicated. This study observes the dissemination of COVID-19 health literacy information by Canadian postsecondary institutions aimed at university students as compared to provincial and federal government COVID-19 guidelines. METHODS: We conducted a systematic scan of web pages from Canadian provincial and federal governments and from selected Canadian universities to identify how health information is presented to university students. We used our previously implemented health literacy survey with Canadian postsecondary students as a sampling frame to determine which academic institutions to include. We then used specific search terms to identify relevant web pages using Google and integrated search functions on government websites, and compared the information available on pandemic measures categorized by university response strategies, sources of expertise and branding approaches. RESULTS: Our scan of Canadian government and university web pages found that universities similarly created one main page for COVID-19 updates and information and linked to public sector agencies as a main resource, and mainly differed in their provincial and local sources for obtaining information. They also differed in their strategies for communicating and displaying this information to their respective students. CONCLUSION: The universities in our sample outlined similar policies for their students, aligning with Canadian government public health recommendations and their respective provincial or regional health authorities. Maintaining the accuracy of these information sources is important to ensure student health literacy and counter misinformation about COVID-19.
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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 | 0.012 | 0.064 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.036 | 0.050 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".