Prevalence and Psychosocial Correlates of Mental Health Outcomes Among Chinese College Students During the Coronavirus Disease (COVID-19) Pandemic
Bibliographic record
Abstract
Objectives: To investigate the prevalence and risk factors for poor mental health Chinese university students during the COVID-19 pandemic. Method: Chinese nation-wide on-line cross-sectional survey on university students, collected between February 12th and 17th, 2020. Primary outcome was prevalence of clinically-relevant post traumatic stress disorders symptoms. Secondary outcomes on poor mental health included prevalence of clinically-relevant anxiety and depressive symptoms, while post traumatic growth was considered as indicator of effective coping reaction. Results: Of 2,500 invited Chinese university students, 2,038 completed the survey. Prevalence of clinically-relevant PTSD, anxiety and depressive, symptoms, and PTG were 30.8%, 15.5%, 23.3%, and 66.9% respectively. Older age, knowing people who had been isolated, more ACEs, higher level of anxious attachment, and lower level of resilience all predicted primary outcome (all p < 0.01). Conclusions: A significant proportion of young adults exhibit clinically relevant PTSD, anxious or depressive symptoms, but a larger portion of individuals showed to effectively cope with COVID-19 pandemic. Interventions promoting resilience should be provided, even remotely, to those subjects with specific risk factors to develop poor mental health during COVID-19 or other pandemics with social isolation.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".