Association of Excessive <i> WeChat</i> Use with Mental Disorders: A Representative Nationwide Study in China
Bibliographic record
Abstract
Objectives: We examined associations between excessive WeChat use and mental disorders at the individual and contextual level. Methods: We conducted a representative nationwide survey sampling process of 11,283 medical students from 30 universities in China. Mental health status was measured by the Chinese Health Questionnaire. Both unadjusted and adjusted methods were considered in the analyses. Results: High frequency and long-time use prevalence was 19.1% and 31.2% respectively among WeChat users. The multilevel logistic regression model found that individual-level high frequency (OR = 1.26) and long-time use (OR = 1.24) were significantly associated with mental health disorders. University-level excessive WeChat use also was associated with the mental disorders (OR = 1.33 [high frequency use]; OR = 1.17 [long-time use]). Structural equation analysis showed that individual- and university-level high frequency and individual-level and university-level long-time WeChat use have a direct influence on poor mental health. The above variables, except individual-level long-time use, have an indirect influence on poor mental health through mental stress. Conclusions: This study provides new evidence that excessive WeChat use is associated with mental disorders. These findings underscore the importance of alerting people to the possible health risks of excessive social media use.
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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 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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; 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".