Studying Under Stress: The Effect of COVID-19 Psychological Distress on Academic Challenges and Performance of Post-Secondary Students
Bibliographic record
Abstract
The COVID-19 pandemic introduced significant disruptions in the learning environment for many post-secondary students. While emerging evidence suggest mental health has declined during the pandemic, little is known about how the pandemic has affected students academically. This study investigates how COVID-19 psychological distress impacts academic performance among a Canadian sample of post-secondary students (n = 496). Path analysis findings suggest that greater levels of COVID-19 distress was associated with lower self-reported predicted GPA. Metacognitive, motivational, and social and emotional challenges emerged as the most salient challenge areas that fully mediated the relationship between COVID-19 psychological distress and self-reported predicted GPA. Specifically, COVID-19 distress predicted greater levels of metacognitive and motivational challenges which, in turn, predicted lower self-reported GPA. Similarly, greater levels of COVID-19 distress predicted more social and emotional challenges but these challenges were associated to higher perceived GPA. Findings warrant future research to help students manage and cope with academic challenges that may be exacerbated under stressful conditions.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".