Implication of COVID-19 on Post-Secondary Students’ Mental Health: A Review
Bibliographic record
Abstract
Introduction: Nearing two nears into the current pandemic, COVID-19 is recognized worldwide for its devastating physical effects, with mandatory restrictions implemented to prevent the transmission of SARS-CoV-2. However, the world is only beginning to understand the pandemic’s mental and social side effects. As such, current research on consequential mental health from COVID-19 is still novel, and there is much more to be learned concerning the long-term psychological effects and damage from the pandemic. Discussion: The combination of online learning and social isolation due to COVID-19 has affected post-secondary students across North America as it relates to their overall well-being and mental health. Researchers have aimed to examine the psychological impact on students’ mental health, primarily through cross-sectional studies and self-reported surveys. Conclusion: Studies have determined that COVID-19 has increased mental health symptoms such as depression, anxiety, PTSD, as well as increased feelings of isolation, loneliness, and fatigue. Furthermore, drinking and substance use, poor sleeping patterns, and screen time have risen as a result of the ongoing pandemic. Relevance: These findings call for post-secondary institutions, health care providers, and governments to prioritize the mental health of future generations while providing support and intervention programs. Future research should focus on further investigating COVID-19’s long-term effects on the mental health of post-secondary students and exploring prevention methods.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".