Monitoring Psychotropic Medication Influence on Disruptive Behaviour in Persons with Intellectual and Developmental Disabilities
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Bibliographic record
Abstract
Demographic research suggests that up to 50% of persons with intellectual and developmental \ndisabilities engage in disruptive behaviour (Sheehan et al., 2015). Psychopharmacological \ninterventions are an oft applied treatment approach. Unfortunately, efficacy research on this topic \nis relatively limited, especially applied behavioural pharmacology research aimed at monitoring \nand evaluating the behavioural effects of psychotropic medication in this clinical population \n(Khokhar et al., 2023). Behaviour analysts often conduct a functional analysis to uncover \nbehaviour function as this assessment approach permits the systematic examination of the \nrelationship between disruptive behaviour and environmental events. Theory around how \npsychotropic medications may be affecting behaviour suggests that functional analyses may \nfacilitate revealing drug-behaviour interactions. Thus, the proposed study explored the \nbehavioural effects of clinically-indicated psychotropic medication changes in 10 adults with \nintellectual and developmental disabilities who engaged in disruptive behaviour and were \nprescribed psychotropic medication. Repeat functional analyses were conducted across regular \nand PRN phases to monitor changes in behaviour function and rate. Evidently, 47.82% of the \nregular medication phase comparisons were associated with function stability, while 36.36% of \nthe PRN phase comparisons were associated with function stability. An odds ratio coefficient of \n0.62 (95% CI: 0.14-2.73) indicated function changes across PRN medication phases were more \nlikely. Effect sizes were generated to examine the magnitude of change in disruptive behaviour. \nMean absolute effect size results for the PRN and regular medication phases (0.32 and 0.67, \nrespectively) suggest there may be a noteworthy difference across the two conditions. Clinical \nimplications, study strengths and limitations are discussed.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it