Prevalence of depression among Chinese university students: a systematic review and meta-analysis
Bibliographic record
Abstract
Estimates of the depression prevalence among Chinese university students vary considerably across studies. This systematic review and meta-analysis aimed to comprehensively analyze the depression prevalence among Chinese university students. We searched four electronic databases with the search terms of depression, China, university student, and questionnaire. Studies reporting depression among Chinese university students were included in the analysis. Two reviewers independently extracted the data and assessed the qualities of the studies. The package of "meta" in R Foundation for Statistical Computing was used to calculate an overall proportion in a random-effects model with 95% confidence intervals. Subgroup analysis was conducted to analyze the influencing factors on the depression prevalence. Any conflict in the data analysis was discussed by all the reviewers. A total of 113 studies were included in the meta-analysis. The overall prevalence of depression among Chinese university students was shown to be 28.4% (n = 185,787), with 95%CI from 25.7 to 31.2%. The overall depression prevalence among Chinese university students was still relatively high. More efforts need to be done to provide better mental healthcare to university students in China.
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 | 0.011 | 0.025 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.014 | 0.022 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".