Correlates of restraint and seclusion for adults with intellectual disabilities in community services
Bibliographic record
Abstract
BACKGROUND: Some individuals with intellectual disabilities (IDs) exhibit aggressive behaviour directed towards themselves, others or the environment. Displaying aggressive behaviour is associated with a number of negative consequences such as the exposure to restrictive interventions. This study aims to identify personal and environmental factors related to the use of restrictive measures among persons with IDs living in the community. METHODS: Data for 81 adults with IDs were collected through a mail survey. The questionnaires acquired information on demographic variables, physical health and psychiatric diagnoses, medication, residential setting, support worker experience and prevalence of restraint and seclusion. The type and severity of aggressive behaviours were measured by the Modified Overt Aggression Scale. RESULTS: The prevalence of restrictive measures was 63.0%: 44.4% seclusion, 42.0% physical restraint and 27.2% mechanical restraint. The mode of communication, anxiolytic medication, severity of the aggressive behaviours, presence of a functional assessment on aggressive behaviours, and support workers' experience with persons with IDs were predictors of restrictive measures. CONCLUSION: The results of this study have several clinical implications for practitioners working with persons with IDs who exhibit aggressive behaviours. More research is needed to expand our understanding of the use of restrictive measures and reduce its frequency.
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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 | 0.000 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".