Assessment and treatment units for people with intellectual disabilities and challenging behaviour in England: an exploratory survey
Bibliographic record
Abstract
BACKGROUND: Evaluative studies have shown that special units for people with intellectual disabilities (ID) who have challenging behaviour have advantages and disadvantages. There has been no survey of their number or characteristics for nearly 20 years. METHODS: A questionnaire was sent to all National Health Service trusts that had ID inpatient beds, and all private or voluntary healthcare establishments providing services for people with mental health problems or ID. This asked for information about the unit, its residents and the views of the unit manager. RESULTS: Forty-four agencies confirmed that they provided assessment and treatment units, of which 38 returned questionnaires. These units served 333 people, of whom 75% had mild or moderate ID. A quarter had been there for more than 2 years. Forty per cent of residents had a discharge plan, and 20% had this and the type of placement considered ideal for them in their home area. The main strengths of the units were identified as the knowledge and experience of the staff and having sufficient staff; the main problems as inappropriate admissions, bed-blocking and the relationship with other services; difficulties with recruiting and retaining staff; the location and environment of the unit; and the mix of residents. CONCLUSIONS: There has been an increasing rate of provision of special units, which now predominantly serve people with moderate or mild ID. This model of service provision is becoming more widespread, but the potential problems identified 20 years ago are still present. Areas are identified for further research.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 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".