Factors Associated with Restraint Application in Children and Adolescents with Intellectual and Developmental Disabilities Displaying Severe Challenging Behaviour
Bibliographic record
Abstract
Emergency physical restraints (PR) are widely used, with prevalence rates ranging from 11% to 78% across different service sectors (Fitton & Jones, 2020; Ye et al., 2019). Behaviour analysts may recommend PR when severe challenging behaviour (CB) poses significant safety risks (e.g., intense aggression causing severe tissue damage; Foxx & Meindl, 2007; Vollmer, 2011). Most existing PR literature featuring child and adolescent participants focuses on variables that behaviour-analytic interventions cannot manipulate (e.g., gender, diagnosis). Additionally, the literature predominantly features inpatient clinical populations with psychiatric disorders despite the prevalence of restraint application among individuals with intellectual and developmental disabilities. The current study applied a multi-level analysis using retrospective outpatient data (N =12) from children and adolescents with intellectual and developmental disability who required emergency PRs. The study aimed to a) examine participant and restraint application characteristics and trends, and b) determine if CB severity at intake, as reported by the partnering agency's severity tool, predicted the latency to restraint application. According to the descriptive analysis most participants were experiencing polypharmacy (i.e., concurrent psychotropic medication use), received moderate to high scores on the partnering agency’s CB severity assessment tool, and primarily exhibited tangible or multiply controlled behavioural functions. Regarding restraint characteristics, the average restraint rate was 0.091 per hr, with most participants being described as calm during restraints. Typically, more than two staff members applied PR. Regression results indicated that the median CB severity score significantly predicted latency to the first restraint applications. Clinical implications 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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".