Women Are at Greater Risk of OCD Than Men
Bibliographic record
Abstract
OBJECTIVE: To estimate the worldwide prevalence of obsessive-compulsive disorder (OCD), examine whether women are at greater risk than men, and explore other potential moderators of OCD prevalence to explain variability in community-based epidemiologic studies. DATA SOURCES: An electronic search of PsycINFO and PubMed was conducted until January 2017, without date or language restrictions, using the keywords OCD, epidemiology, and prevalence. The search was supplemented by articles referenced in the obtained sources and relevant reviews. STUDY SELECTION: Studies were included if they reported current, period, and/or lifetime OCD prevalence (diagnosed according to an interview based on DSM or ICD criteria) in representative community samples of adults aged 18 years or older. A total of 4,045 studies were retrieved, with 34 studies ultimately included. DATA EXTRACTION: OCD prevalence was extracted from each study alongside 9 moderators: gender, year, response rate, region, economic status, diagnostic criteria, diagnostic interview, interviewer, and age. RESULTS: The overall aggregate current, period, and lifetime OCD prevalence estimates were 1.1%, 0.8%, and 1.3%, respectively. In a typical sample, women were 1.6 times more likely to experience OCD compared to men, with lifetime prevalence rates of 1.5% in women and 1.0% in men. There was also a trend toward younger adults' being more likely to experience OCD in their lifetime than older adults. All findings demonstrated moderate heterogeneity. CONCLUSIONS: Women are typically at greater risk of experiencing OCD in their lifetime than men.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".