Rapid Growth Of Antipsychotic Prescriptions For Children Who Are Publicly Insured Has Ceased, But Concerns Remain
Bibliographic record
Abstract
The rapid growth of antipsychotic medication use among publicly insured children in the early and mid-2000s spurred new state efforts to monitor and improve prescription behavior. A starting point for many oversight initiatives was the foster care system, where most of the children are insured publicly through Medicaid. To understand the context and the effects of these initiatives, we analyzed patterns and trends in antipsychotic treatment of Medicaid-insured children in foster care and those in Medicaid but not in foster care. We found that the trend of rapidly increasing use of antipsychotics appears to have ceased since 2008. Children in foster care treated with antipsychotic medications are now more likely than other Medicaid-insured children to receive psychosocial interventions and metabolic monitoring for the side effects of the medications. However, challenges persist in increasing safety monitoring and access to psychosocial treatment. Development of specialized managed care plans for children in foster care represents a promising policy opportunity. New national quality measures for safe and judicious antipsychotic medication use are also now available to guide improvement. Oversight policies developed for foster care appear to have potential for adaptation to the broader population of Medicaid-covered children.
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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 | 0.007 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".