Prevalence of mental disorder symptoms among university students: An umbrella review
Bibliographic record
Abstract
This umbrella review synthesizes data on the prevalence of mental disorder symptoms among university students worldwide. A systematic search of seven databases (inception-July 23, 2023) followed PRISMA guidelines. We included meta-analyses assessing the prevalence of mental disorder symptoms, evaluating methodological quality with AMSTAR-2. A random-effects meta-analysis was conducted, along with meta-regression and subgroup analyses for moderators (percentage of females, publication date, healthcare-related degrees, COVID-19 pandemic). We included 1,655 primary studies from 62 meta-analyses, encompassing 8,706,185 participants. AMSTAR-2 ratings classified 35 % of meta-analyses as low quality and 65 % as critically low. Pooled prevalence estimates were: depression-mild (35.41 %, CI=33.9-36.93) and severe (13.42 %, CI=8.03-19.92; k=952; n=2,108,813); anxiety-mild (40.21 %, CI=37.39-43.07) and severe (16.79 %, CI=7.21-29.29; k=433; n=1,579,780); sleep disorders (41.09 %, CI=35.7-46.58); eating disorders (17.94 %, CI=15.79-20.20); gambling disorder (6.59 %, CI=5.52-7.75); post-traumatic stress disorder (25.13 %, CI=20.55-30.02); stress (36.34 %, CI=29.36-43.62); and suicide-related outcomes (ideation past 12 months: 10.76 %, CI=9.53-12.06; lifetime ideation: 20.33 %, CI=16.15-24.86; suicide attempt past 12 months: 1.37 %, CI=0.67-2.29; lifetime attempt: 3.44 %, CI=2.48-4.54). Meta-regression analyses identified statistically significant moderators of prevalence such as healthcare academic degrees and the pandemic in the case of depression and studies with more females in the case of sleep disorders. This is the most comprehensive synthesis on the prevalence of mental disorder symptoms in university students, providing crucial insights for clinicians, policymakers, and stakeholders.
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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 | 0.017 | 0.062 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.010 | 0.011 |
| Bibliometrics | 0.021 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".