The <scp>COVID</scp> ‐19 Pandemic's Impact on Credibility of Health Sources Among Undergraduate Students
Bibliographic record
Abstract
ABSTRACT The following poster reports the preliminary results of a comparison between a 2017 survey on health information and the same survey administered in 2023. The primary research question is: How did the COVID‐19 pandemic impact undergraduate students' judgement of credibility in health information sources? Recent research has shown that student health information seeking has changed around the COVID‐19 pandemic. However, the research has not noted whether the pandemic has had a lasting impact on credibility of sources during health information seeking at the presumptive tail end of the pandemic in 2023. The original study in 2017 surveyed the undergraduate population of McGill University. The same survey was readministered in 2023, with COVID‐19 specific questions added. The preliminary analysis suggests that the COVID‐19 pandemic impacted students' judgement of credibility in health information sources. There were negative changes in the perceived credibility of family/friends, well‐known websites, wiki, blogs/forums, and social media for both everyday life health and COVID‐19 information from 2017 to 2023. Conversely, government/university, scholarly books/journals, and TV/radio all saw increases in perceived credibility for both everyday life health and COVID‐19 information.
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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 | 0.013 | 0.064 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".