Prevalence of Non-suicidal Self-Injury in the General Population in Iran: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Context: Non-suicidal self-injury (NSSI) is an intentional behavior without suicidal intent, recently recognized as an independent diagnostic entity in the diagnostic and statistical manual of mental disorders, fifth edition (DSM-5). Various studies indicate that NSSI is prevalent worldwide, but there are no reports on its prevalence among the Iranian population. Evidence Acquisition: A systematic review of the literature was conducted using databases such as Scopus, PubMed/Medline, Web of Science, IMBIS, Google Scholar, Cochrane, and PsycInfo. Domestic databases, including Iran Medex, Mogiran, and SID, were also utilized. All available data until the end of June 2024 were reviewed. Data extraction was performed by two researchers, and study quality was assessed using the Newcastle-Ottawa Scale (NOS). Studies on NSSI in the general population of Iran that reported prevalence rates and accurate sample sizes were included. The heterogeneity of the studies was evaluated using the Cochran test and I2 statistics. Additionally, a meta-regression analysis was conducted based on the year of study. Results: A total of 2,180 articles were reviewed, with 17 articles ultimately selected and included in the study. The random-effects model estimated the pooled prevalence of NSSI in the general population at 16.51% (95% CI, 13.59 - 19.43). The prevalence of NSSI in women and men was 19.27% (95% CI, 13.31 - 25.24) and 14.74% (95% CI, 10.53 - 18.94), respectively. A correlation was found between the number of years since the study was conducted and the prevalence of NSSI (Reg Coef = 0.01, 95% CI: 0.003 to 0.020, P = 0.011); newer studies reported higher prevalence rates. Conclusions: The present study revealed a high prevalence of NSSI in the general population, particularly in certain provinces and among women, indicating the need for specific prevention and treatment programs.
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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 | 0.026 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.033 |
| Bibliometrics | 0.010 | 0.009 |
| 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".