Epidemiological Survey on the Status of Obsessive-compulsive Disorder among School-age Childrenin Iran
Bibliographic record
Abstract
Background and purpose: The age of the onset of obsessive-compulsive disorder (OCD) and its manifestation is now identified at childhood. In the present study, we aimed to epidemiologically assess OCD and its main determinants in school-age children. Materials and Methods: This cross-sectional study was performed on 600 consecutive schoolage children (7-12 years old) in primary schools at Kermanshah, Iran in May 2012. OCD status was assessed using the Maudsley Obsessive Compulsive Inventory. Results: In this study, the prevalence of OCD in children were 12.2% (73 person). Among OCDs, 32.0% were male and 68.0% were female with a significant difference (P < 0.001). Among different subscales of OCD, checking was more prevalent in girls than in boys (74.0% vs. 65.0%, P < 0.001), whereas doubting was more revealed in boys than in girls (56.0% vs. 34.0%, P < 0.001). In this regard, no differences were found between boys and girls in the rate of washing subscale (43.0% vs. 40.0%, P = 0.456) and slowness domain (30.0% vs. 34.0%, P = 0.294). Totally, mean MOCI score was significantly a higher in girls than in boys in different grades of education. OCD was influenced by father’s occupation status, parents’ education level, order of birth, dependence to mothers and previous history of OCD in fathers. Conclusion: The prevalence of OCD was a higher than other areas in the country and other populations. Because of the existence of OCD potential determinants including socioeconomic level and familial tendency, the high prevalence of the disease must be considered in correlation with these factors.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".