Factors Influencing Depression and Mental Distress Related to COVID-19 Among University Students in China: Online Cross-sectional Mediation Study
Bibliographic record
Abstract
BACKGROUND: The COVID-19 epidemic may elevate mental distress and depressive symptoms in various populations in China. OBJECTIVE: This study investigates the levels of depression and mental distress due to COVID-19, and the associations between cognitive, behavioral, and psychosocial factors, and depression and mental distress due to COVID-19 among university students in China. METHODS: A large-scale online cross-sectional study (16 cities in 13 provinces) was conducted among university students from February 1 to 10, 2020, in China; 23,863 valid questionnaires were returned. The Patient Health Questionnaire-9 was used to assess depression. Structural equation modeling was performed to test mediation and suppression effects. RESULTS: Of the 23,863 participants, 47.1% (n=11,235) reported high or very high levels of one or more types of mental distress due to COVID-19; 39.1% (n=9326) showed mild to severe depression. Mental distress due to COVID-19 was positively associated with depression. All but one factor (perceived infection risks, perceived chance of controlling the epidemic, staying at home, contacted people from Wuhan, and perceived discrimination) were significantly associated with mental distress due to COVID-19 and depression. Mental distress due to COVID-19 partially mediated and suppressed the associations between some of the studied factors and depression (effect size of 6.0%-79.5%). CONCLUSIONS: Both mental distress due to COVID-19 and depression were prevalent among university students in China; the former may have increased the prevalence of the latter. The studied cognitive, behavioral, and psychosocial factors related to COVID-19 may directly or indirectly (via mental distress due to COVID-19) affect depression. Interventions to modify such factors may reduce mental distress and depressive symptoms during the COVID-19 epidemic.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".