Canadian university students’ perceptions of COVID-19 severity, susceptibility, and health behaviours during the early pandemic period
Bibliographic record
Abstract
We surveyed university students to assess their demographic factors, perceived severity, personal susceptibility, and the adoption of health behaviours in relation to COVID-19. Ethics approval was obtained from the University of Toronto’s Research Ethics Board (#39169). Responses were collected between March 20 and April 17, 2020, capturing the first month of government-mandated social distancing in Ontario, Canada. We distributed the online survey to the University of Toronto student population, yielding a total convenience sample of 592 participants. We summarised the results and conducted Mann-Whitney U and Kruskal-Wallis tests to explore relationships between demographic data and perceived severity of COVID-19. Pearson’s Chi-square tests were used to explore the relationship between demographic variables and perceived susceptibility, with phi being used to explore the strength of the association. A value of p < 0.05 was used to determine significance. The majority of participants (60.1%) judged COVID-19 to be Very Severe; there was a significant relationship between being female and the adoption of new health behaviours. 57.4% indicated they felt susceptible to COVID-19, while 40.9% did not. Feeling susceptible was associated with studying a healthcare field or being personally affected by COVID-19. Individuals who stated they were not susceptible to COVID-19 declared mitigating factors such as new health behaviours to be a major driver in their perception. University students believe COVID-19 is a severe disease and have adopted new and increased health behaviours to mitigate the spread. While this study demonstrates differing health behaviour adoption rates based upon demographic factors, overall this research finds young adults supportive and accepting of government policy as a protective and susceptibility-mitigating measure.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".