A Cross-sectional Analysis of the Impact of COVID-19 Related Stressors on Canadian University Students’ Mental Health and Wellbeing
Bibliographic record
Abstract
Abstract BackgroundNational, cross-sectional data suggests that over one third of young, post-secondary aged adults are endorsing moderate to severe levels of anxiety and depression in the wake of the COVID-19 pandemic. The purpose of this study was to evaluate the impact of pandemic-related stressors on university students’ levels of psychological distress, using a large sample of students attending schools across Canada.MethodsThis study analyzed the first data time point of a cross-sectional, repeated measures study of university student stress completed during the 2020-2021 academic year. Participants (n= 4954) were students attending fourteen universities across Canada. Participants completed an online survey that included questions about demographics, stress, COVID-19 diagnosis history, psychological distress, history of mental illness, and resilience.ResultsParticipants reported the highest mean severity of stress ratings for the following COVID-19 related stressors: the pandemic’s effect on academics (X̅= 2.76, SD= 1.06) and uncertainty around how long the pandemic will last (X̅= 2.68, SD= 0.95). Average psychological distress score was high in this sample (X̅= 28.67, SD= 9.53) with nearly half the sample scoring in the ‘severe distress’ category. Modified log poisson regression models were stratified by high/low resiliency. Within the low resilience stratum, the strongest associations were observed for: COVID-19’s effect on academics (RR= 1.14 [95% CI 1.08, 1.20]); difficulties accessing health care during the pandemic (RR= 1.12 [95% CI 1.07, 1.18]); and loneliness as a result of quarantine/self-isolation (R= 1.11 [95% CI 1.06, 1.16]). Within the high resilience stratum, the strongest associations were observed for: COVID-19’s effect on academics (RR= 1.32 [95% CI 1.25, 1.41]); difficulties accessing healthcare during the pandemic (RR= 1.25 [95% CI 1.18, 1.33])); and uncertainty around how long the pandemic will last (RR= 1.24 [95% CI 1.16, 1.32]). Notably, larger effects were observed for those in the high resilience stratum, contrary to expectations.ConclusionsThe results of this study suggest that students’ psychological distress has been significantly impacted by the COVID-19 pandemic. In order to effectively support students’ wellbeing throughout the pandemic and beyond, improved understanding of stress related to COVID-19 is needed. Implications of this study and areas for future research are discussed.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.004 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".