Variations in behaviour function in individuals with intellectual disability and psychotropic medication
Bibliographic record
Abstract
Psychopharmacological and behavioural interventions are used to treat challenging behaviours (e.g., self-injury, aggression, stereotypy, bizarre vocalizations) in individuals with intellectual disability (ID), often in combination. However, little is known about the behavioural mechanisms underlying psychopharmacological treatment. Establishing a better understanding of these mechanisms could contribute to improving treatment efficacy. For this study, I conducted repeated functional analyses using single-subject experimental designs to assess the impact of naturally varying dosages of psychotropic medications on behaviour function. Four individuals with ID who engaged in challenging behaviour and were undergoing psychotropic medication changes participated. Medication impact across two topographies for one participant, and three topographies for another participant were assessed, for a total of seven cases. For Analysis 1, I calculated standardized mean differences between baseline and final drug administration phases to estimate the overall effect of medication. I used this information to examine whether response rate following drug administration was related to response rate during baseline, referred to as rate-dependency. Rate-dependency was not observed. Analysis 2 explored the relation between psychotropic medications and behaviour function identified through functional analyses. Challenging behaviour was the dependent variable, while functional analysis conditions and psychotropic medication level served as independent variables. The latter was a quasi-experimental variable given participants’ psychiatric team prescribed changes independent of the researchers. Behaviour function correspondence, defined as no function change after a medication manipulation, was observed across 14 of the 21 medication manipulations (67%).
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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".