Medicine in the Classroom: A Review of Psychiatric Medications for Students with Emotional or Behavioral Disorders.
Bibliographic record
Abstract
There are many ways to help children with emotional and behavioral disorders (EBD) succeed in school. Functional behavioral assessments guide educators in identifying areas of individual need (Kern & Gresham, 2004). Behavior intervention plans provide supports to help students adjust their actions to fit school norms (e.g., Stahr, Cushing, Lane, & Fox, 2006). Inclusive philosophy and practices continue to break the cycle of restricted and segregated placements (Turnbull, Turnbull, & Wehmeyer, 2006), whereas schoolwide intervention models have brought benefit not only to a few targeted students, but to entire schools (e.g., Lewis, Powers, Kelk, & Newcomer, 2002). Perhaps most notable is the spread of pharmacological treatment for behavioral disorders. Once rare, it is now commonplace to find that students with EBD are taking psychiatric medication (Konopasek & Forness, 2004). It has been more than 10 years since Forness, Sweeney, and Toy (1996) published their introduction to psychopharmacology in Beyond Behavior, and the intervening years have seen the use of psychiatric medication in children increase dramatically. Today, nearly a quarter of all students seen by school psychologists take some form of psychiatric medication (Carlson, Demaray, & Hunter-Oehmke, 2006); in EBD settings, as many as 65% of students take at least one, and sometimes several, such prescriptions (Hall, Bowman, Ley, & Frankenberger, 2006). Research has supported the effectiveness of medication in treating a growing number of emotional and behavioral difficulties, especially when offered together with behavioral intervention (e.g., Forness, Freeman, & Paparella, 2006; Weisz & Jensen, 1999). Advances in medical science have made it possible for physicians to prescribe medications with fewer doses per day and milder side effects, and to choose from an ever-widening range of available formulations (Wilens, 2004). Medication has truly emerged from the shadows to become an important aspect of multimodal treatment for children with emotional or behavioral needs. This article provides an overview of the medications that physicians use to treat children and youth with EBD, medications that students and their families encounter every day. Understanding the effects of these medications can enhance the ability of educators to work with these challenging youngsters.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".