The Impact of COVID-19 Psychological Distress on Students' Academic Challenges in University
Bibliographic record
Abstract
The COVID-19 pandemic has introduced significant disruptions in the learning environment for many post-secondary students with many shifting entirely to remote online learning, which can compound existing academic challenges. While emerging evidence has suggested that COVID-19 impacts students’ well-being and stress, little is known about how the pandemic has affected students academically. This study investigates how different types of academic challenges mediate the relationship between students’ COVID-19 psychological distress and their academic performance. Participants (n=496) completed an online survey that measured COVID-19 psychological distress, self-reported grade point average (GPA), and academic challenges. Mediational analyses estimating indirect pathways were conducted using structural equation modelling on Mplus. Our results showed that all challenges increased along with COVID-19 distress, but specific challenges had a significant relationship with the expected GPA. We found that out of the five academic challenge areas, metacognitive, motivational, and social and emotional challenges emerged as the salient challenge areas that fully mediated the relationship between COVID-19 distress and GPA. Contrary to our prediction, while more significant COVID-19 distress predicted more social and emotional challenges, these challenges were associated to higher GPA. Future research is invited to help students manage and cope with their academic challenges.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".