The Impact of COVID-19 on the Mental Health of College Students
Bibliographic record
Abstract
COVID-19 changed the world in the span of a few months. Schools and other businesses had to close and move to an online format to decrease physical interaction and stop the spread of the virus. Many people went without seeing close friends and loved ones due to the quarantine or lost someone close to them due to the virus. Students enrolled in college were sent home abruptly and could not return in person to school for the remainder of the year. Even when they were allowed to return to school, new guidelines and how classroom content was delivered were put into effect. Quarantine and the pandemic caused the mental health of many students to decline. COVID-19 also caused an increase in other stressors, such as financial stability, lack of food, or housing options. The purpose of this study is to research how COVID-19 impacted the mental health of college students. This cross-sectional survey study used a convenience sampling of 15 college students within a specific department at a private university in West Texas. A hierarchical regression analysis shows that the increase in stressors, such as housing and financial stability, was a statistically significant factor in mental health after COVID-19. Based on the findings, universities and communities will need to expand mental health resources, as well as services that provide other needs such as food and financial help. More studies with larger sample sizes on this issue would be beneficial in order to better understand the impact COVID-19 had on the mental health of college students.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.000 | 0.002 |
| 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".