Level of Knowledge in the COVID-19 Pandemic: A Cross-Sectional Survey of Canadian Medical Students
Bibliographic record
Abstract
Abstract Background During health crises medical education is often derailed as was the case during the current COVID-19 pandemic. Medical trainees face the daunting task of having to gather, filter and synthesize new information about the evolving situation often without the standardized resources they are used to. Methods We surveyed Canadian medical students, in the hardest hit province of Quebec, on how they were acquiring knowledge as well as what they knew of the pandemic. Google Forms was used, with the survey being distributed to each medical school in Quebec (McGill, ULaval, Udem) both through email and through social media pages for each class year. Two analyses, Mann-Whitney and ANOVA tests, were performed for year of study and degree obtained. Results We received responses from 111 medical students from three universities, which represents 5% of the students invited to complete the survey. Students reported using mass media most frequently (83%) and also had a high rate of use of social media (to gather information about the pandemic. They rated these resources low in terms of their trustworthiness despite the high rates of use (average 2.91 and 2.03 of 5 respectively). Medical students also endorsed using more formal resources like public health information, scientific journals and faculty-provided information that they trusted more, however, they accessed these resources at lower rates. Of note, medical students had correct answered 60% of COVID-19 prevention strategies, 73% clinical correct answers, 90% epidemiological correct answers. Additionally, students who were training in the larger city of Montreal, where the worst of the outbreak was focused, tended to significantly perform better (p Conclusion These finding indicate a wide use of information resources intended for public consumption rather than more rigorous and trustworthy sources. Furthermore, there seems to be a knowledge gap amongst medical students responding to this survey that suggests an opportunity to improve the delivery of educational content during this rapidly evolving pandemic.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".