The prevalence of psychiatric disorders among students aged 6~ 16 years old in central Hunan, China
Bibliographic record
Abstract
BACKGROUND: Though several epidemiological surveys of psychiatric disorders have been carried out in China, only a few of them are concerned about the prevalence of psychiatric disorders in central Hunan and reveal the distribution of common psychiatric disorders and their comorbidities. METHODS: Achenbach's Child Behavior Checklist (CBCL), the Mini International Neuropsychiatric Interview for Children and Adolescents (MINI-KID), and Diagnostic and Statistical Manual of Mental Disorders, 4th Edition (DSM-IV) were administered to a stratified sample of 17,071 participants aged 6 to 16 years old from two cities in the central part of Hunan province. Twelve-month prevalence rates were calculated. RESULTS: Twelve-month prevalence of the population was 9.74%. The most common psychiatric disorders were attention deficit hyperactivity disorder (ADHD) (4.96%), oppositional defiant disorder (ODD) (2.98%) and generalized anxiety disorder (GAD) (1.77%). Of those with a 12-month prevalence diagnosis, 34.6% had one or more comorbid psychiatric disorders. Most notably, ADHD had comorbidity rates of 25.15% with ODD, 18.18% with CD, 6.38% with GAD, and 3.66% with MDD. CONCLUSIONS: Psychiatric disorders are common in Chinese children and adolescents. Being the most prevalent mental disorder, ADHD requires continued focus and support in awareness and education.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".