Fear of COVID-19 Effects on University Student’s Health Behaviours: The FRESH Study
Bibliographic record
Abstract
Background: The imposed stay-at-home restrictions with the coronavirus (COVID-19) pandemic disrupted daily routines and impacted some population groups more than others. One such group included university students who were forced to adapt to online classes reducing face to face interactions. The uncertainty associated with this change may have created a sense of anxiety and fear towards the virus. Objective: The objective of this study was to measure the level of fear associated with COVID-19 in Canadian undergrad university students and the influence fear may have on their health-related behaviours during an ongoing pandemic. Methods: Students enrolled in four large universities in the province of Alberta were invited to complete an online Fear of COVID-19 Scale (FCV-19S) from January 4th to February 15th, 2021. Results: Undergraduate students (n=680; 73.4% female) with a mean age of 23 (SD 5) years had an average FCV-19 score of 17.1 (SD 6.1) out of 35. Many students (60%) categorized their fear in the moderate range and females were more likely to have a higher FCV-19 score (p<.05) than males. Increases in alcohol and substance use, sleeping and eating behaviours were significantly associated (p<.05) with higher FCV-19 scores (p=.047; p=.000; p=.000). Most student’s smoking behaviour (80.5%) remained the same, however physical activity decreased. Conclusion: In summary, students showed a moderate level of fear associated with COVID-19 during the pandemic, and those with greater levels of fear reported more negative heath behaviour choices. These results highlight that continued restrictions during the COVID-19 pandemic continue to negatively impact student’s health behaviours.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".