Prevalence of psychiatric disorders in Iran: A systematic review
Bibliographic record
Abstract
Objective: Taking the diversity of the methodologies applied in prevalence studies of psychiatric disorders in Iran and their heterogeneous results into consideration, there seems to be need for a systematic review in order to compile the findings and seek appropriate recommendations for future studies. This study aims at systematically identifying studies conducted in Iran describing the prevalence of psychiatric disorders in general population, and to summarize the findings of these studies. Method: To identify the relevant studies, several databases including Pubmed Medline, ISI Web of Science, PsychINFO, CINAHL, EMBASE, Irandoc, IranPsych, IranMedex, Scientific Information Database as well as reference lists of the accessed documents, unpublished reports, conference proceedings and dissertations were searched. In the next step, the original studies which contained an estimation of prevalence of 'any psychiatric disorder' (overall prevalence) among a sample of general population in the country were selected. This was followed by data extraction, presentation of the results, quality assessment and quantitative pooling of estimated rates of prevalence of psychiatric disorders. Results: A total number of 35 studies were included. Estimations provided for prevalence rates in different groups illustrate diversity and heterogeneity; the rates varied in the range of 1.9-58.8%. Most of the studies had assessed the point prevalence of the disorders conducted using screening instruments. The median point prevalence has been reported to be 28.70% in screening studies, and 18.60% in studies using diagnostic interviews. Pooled estimates obtained through meta-analysis for screening and diagnostic studies were 29.1% and 21.9%,respectively. The results of the studies which have used diagnostic interviews as their data collection tool showed less heterogeneity than the ones using screening instruments. In quality assessment of the studies, only one third proved to be of high quality. Conclusion: Even though the pooled rates for prevalence of psychiatric disorders are comparable to the rates in many other countries, the most important finding of this study is the diversity of the prevalence rates among different communities in Iran. This diversity does not seem to be attributed solely to the different time frames and geographical locations of the studies. It might also have resulted from differences in methodologies (e.g., using different tools), study procedures and study quality.
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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 | 0.011 | 0.029 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.008 |
| Bibliometrics | 0.018 | 0.015 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 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".