Evaluating Preference Stability Across Psychotropic Medication Changes in Persons with Intellectual and Developmental Disabilities
Bibliographic record
Abstract
Research in applied behaviour analysis evaluating psychotropic medication impact on persons with intellectual and developmental disabilities (IDD) is relatively limited (Cox & Virués-Ortega, 2016). Even though evidence supporting the efficacy of psychotropic medication in treating challenging behaviour has been described as controversial, an Ontario study reported between 39% to 56% of adults with IDD are prescribed at least one psychotropic medication (Lunksy et al., 2018). The overall prevalence of medication use within this population, combined with the lack of research showcasing efficacious outcomes, suggests that further evaluation of psychotropic medication impacts is required. Behaviour analytic researchers have hypothesized that psychotropic medications may function as motivating operations (Conine & Vollmer, 2019). Therefore, it may be important to systematically monitor clinically indicated medication changes for their effect on an individuals preference stability, as well as on stimulus class displacement. Two participants with IDD who engage in challenging behaviour and were undergoing medication changes (e.g., medication increases, decreases, addition, and removal) took part in repeated weekly preference assessments (edible-item, leisure-item, and combined-class). Analysis included a Spearman rank correlation analysis, a non-parametric partial correlation analyses, and visual analysis to these data. Results indicated that psychotropic medication changes appeared to affect non-selection, preference stability, and class displacement differentially across the two participants. Clinical implications, limitations, and future directions are discussed.
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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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".