The COVID-19 Pandemic, Undergraduate Students’ Well-Being and Their Coping Strategies: A Scoping Review
Bibliographic record
Abstract
The COVID-19 pandemic has had a profound impact on the education sector worldwide, and on undergraduate students in particular. This Scoping Review seeks to unearth research examining the psychosocial impact of the pandemic on undergraduate students in the regions of North America, Latin America, and the Caribbean. Additionally, this review explores students’ coping mechanisms as a means to guide researchers in conducting informed investigations and to allow institutions in making meaningful decisions to combat the effects of the pandemic and future disruptions. Despite the wealth of research on COVID-19, findings from this review show a notable scarcity of literature specifically focused on undergraduate students. Findings reveal a consensus among studies regarding increased stress, anxiety, and depression among undergraduate students. Coping strategies employed by students highlight possible challenges such as overuse of social media and substance use, but they also shed light on potential interventions including physical activities, emotional strategies, and social supports. Future research should focus on filling the gaps in the existing literature and assessing the efficacy of targeted interventions. By gaining a deeper understanding of undergraduate student experiences and identifying effective support mechanisms, we can enhance the overall well-being and academic success of students.
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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 | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 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".