COVID-related stressors, psychological distress and social support in Atlantic Canadian University students
Bibliographic record
Abstract
Abstract This study explores the impact of the COVID-19 pandemic on Memorial University of Newfoundland (MUN) undergraduate and graduate students. Using the National College Health Assessment (third revision) (NCHA-III) survey, the relationship between COVID-related stressors and mental health was assessed after controlling for demographic, economic, and academic variables, and reported mental illness. A hierarchical regression revealed that psychological distress was higher for students who were undergraduate, female, of lower family income, with a pre-existing anxiety or depressive disorder. Psychological distress was also predicted by direct COVID stressors (e.g., fear of infection), as well as indirect stressors, including worries about employment and tuition, professor/instructor support, campus efforts to ensure safety, and discrimination/hostility due to race/ethnicity. Chi Square tests subsequently revealed that graduate students were more likely to be concerned about the threat COVID-19 posed to loved ones, separation from family/friends, and pandemic duration, while undergraduates were more likely concerned about returning to school, tuition, employment, and the legitimacy of their degree. Undergraduates were also more likely to have witnessed discrimination/hostility, and less likely to report professor/instructor support. Finally, independent t tests revealed that undergraduate students were significantly lower in overall social support, as well as for particular subdomains including ‘guidance’, ‘social integration’, and ‘reassurance of worth’. Interpretation of the findings and implications are considered.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.006 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 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".