The Use of Psychotropic Medication in Iranian Children with Developmental Disabilities
Bibliographic record
Abstract
The use of psychotropic medication in children is increasing worldwide. Children with developmental disabilities seem to be prescribed these medications at a higher rate compared to their non-disabled peers. Little is known about prescribing in non-Western, middle-income studies. In Iran, the file records of 1133 children, aged 2 to 17 years, assessed as having autism spectrum disorder (ASD) or an intellectual disability (ID) in Tehran City and Province from 2005 to 2019 were collated, and information from parental reports of medications was extracted. Upwards of 80% of children with ASD and 56% of those with ID were prescribed a psychotropic medication with around one quarter in each group taking two or more medications. The rates were higher among male children showing difficult-to-manage behaviors such as hyperactivity, but less so for children of fathers with higher levels of education. The lack of alternative management strategies may be a significant driver for the use of psychotropic medications in Iran and other Low and Middle Income countries, despite their known side effects, and their failure to address the developmental needs of the children. Rather, multi-disciplinary, behavioral, therapeutic, and educational interventions are required, but these are not available widely in Iran, although a start has been made.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| 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".