Bibliometric analysis of the impacts of COVID-19 on the mental health of college students
Bibliographic record
Abstract
The current narrative review was planned to summarise research on the effects of coronavirus disease-2019 on the mental health of college students. A total of 1,695 studies from the Web of Science Core Collections database were accessed using VOSviewer software. China and the United States jointly contributed almost half of the overall publications, while the United States and the United Kingdom demonstrated the strongest collaborative network, and the University of Toronto was the research institution with the highest number of publications; 34(2%) papers and 2,330 citations. The current hotspots could be categorised into four areas, with stress, anxiety and depression being the most prominent. Those effects varied based on their academic major, gender, learning status and social support. The findings underscored the immediate need to cultivate international collaboration and interdisciplinary authorship, alongside the implementation of tailored preventive measures.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.015 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.012 | 0.065 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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; both teacher heads agree on what is shown here.
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".