Prevalence of Mental Disorders among High-School Students in Iran: A Systematic Review
Bibliographic record
Abstract
Objective: The aim of the present study is to perform a systematic review of studies that investigated the prevalence of any kind of mental disorders in high school students in Iran. Method: A broad search was conducted in MEDLINE/PubMed, ISI web of Science, PsychINFO, CINAHL, EMBASE, and three Iranian databases, including IranPsych, IranMedex, and Scientific Information Database (SID). To cover studies not published, we did a hand searching of all theses, reports and congresses' abstract booklets which were available in IranPsych. Then, we included the original studies which reported the prevalence of any kind of mental disorders in high school students; data extraction was performed with two researchers for each document. Results: Sixteen studies were finally included, representing 19 estimates of mental disorders in high school students, using diagnostic or screening instruments. The prevalence rate of any mental disorder reported by two studies using diagnostic instruments was equal to 16.6% and 4.34%. The median of prevalence rates of mental disorders reported in studies using screening instruments was 34.4%. There was a significant heterogeneity between the studies. Conclusion: Prevalence rates of mental disorders were reported in a wide range in high school students of Iran. More studies with improved quality are needed in this field.
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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 | 0.008 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.004 |
| Bibliometrics | 0.013 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".