Prevalence and risk factors of self‐reported psychotic experiences among high school and college students: A systematic review, meta‐analysis, and meta‐regression
Bibliographic record
Abstract
Abstract Background Adolescents are at high risk of incident psychopathology. Fleeting psychotic experiences (PEs) that emerge in young people in response to stress may be warning signs that are missed by research that fails to study stressed populations, such as late high school and college/university students. Our aim in this systematic review was to conduct a meta‐analysis that estimates prevalence rates of PEs in students, and to assess whether these rates differ by gender, age, culture, and COVID‐19 exposure. Method We searched nine electronic databases, from their inception until January 31, 2022 for relevant studies. We pooled the estimates using the DerSimonian–Laird technique and random‐effects meta‐analysis. Our main outcome was the prevalence of self‐reported PEs in high school and college/university students. We subsequently analyzed our data by age, gender, population, country, culture, evaluation tool, and COVID‐19 exposure. Results Out of 486 studies retrieved, a total of 59 independent studies met inclusion criteria reporting 210′ 024 students from 21 different countries. Nearly one in four students (23.31%; 95% CI 18.41%–29.05%), reported having experienced PEs (heterogeneity [Q = 22,698.23 (62), p = 0.001] τ2 = 1.4418 [1.0415‐2.1391], τ = 1.2007 [1.0205‐1.4626], I2 = 99.7%, H = 19.13 [18.59‐19.69]). The 95% prediction intervals were 04.01%–68.85%. Subgroup analyses showed that the pooled prevalence differed significantly by population, culture, and COVID‐19 exposure. Conclusion This meta‐analysis revealed high prevalence rates of self‐reported PEs among teen and young adult students, which may have significance for mental health screening in school settings. An important realization is that PEs may have very different mental health meaning in different cultures.
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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 | 0.013 | 0.035 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.019 | 0.044 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".