Psychiatric Disorders in Children and Adolescents Attending Pediatric Out Patient Departments of Tertiary Hospitals
Bibliographic record
Abstract
OBJECTIVES: Psychiatric disorders are increasingly recognized among children and adolescents in Bangladesh. Psychiatric disorders are more common in children with chronic and acute pediatric disorders. Our study was designed to determine the psychiatric disorders among children and adolescents attending pediatric outpatient departments of tertiary care hospitals. METHODS: This cross-sectional study was carried out from July 2012 to February 2013 in pediatric outpatient departments of three prime tertiary level hospitals of Dhaka, Bangladesh. A purposive sampling technique was used. A total of 240 male and female children aged 5 to 16 years old were included in the study. We used a semi-structured questionnaire to obtain sociodemographic and other relevant clinical information about the children and their families from their parents or caregivers and a validated parent version of the Bangla Development and Well-Being Assessment (DAWBA) for measuring psychopathology. RESULTS: The mean age of the children was 9.0± 2.6 years. The majority (71%) of children were in the 5-10 year age group. The male/female ratio was 1.2:1. Among the respondents, 18% were found to have a psychiatric disorder. Behavioral disorders, emotional disorders, and developmental disorders were found in 9.0%, 15.0% and 0.4% respectively. Hyperkinetic disorder was the single most frequent (5.0%) psychiatric disorder. CONCLUSIONS: A significant number of children were found to have psychiatric disorders. Our study indicates the importance of identification and subsequent management of psychiatric conditions among the pediatric population.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.003 | 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".