Health Service Inpatient Units for People with Intellectual Disabilities and Challenging Behaviour or Mental Health Problems
Bibliographic record
Abstract
Background As institutions for people with intellectual disabilities have been replaced with community services, health care provision has developed to provide assessment and treatment, low and medium secure units for people with challenging behaviour or mental health problems. These include both public and private sector provision. Little is known about these services. The aim of this study was to describe the characteristics of the services studied and to compare the different types of service. Methods A survey was carried out of National Health Service (NHS) and independent sector (IH) health settings providing specialist inpatient services for people with intellectual disabilities. Data were collected on setting and patient characteristics, staffing and management and care processes and outcomes. Results Services were very unevenly distributed across England. There were differences between NHS and IH settings. IH assessment and treatment units were bigger, had higher occupancy and lower staff ratio. In all three types of unit, IH units had fewer visits to patients, used more seclusion, physical restraint and had more locked areas. They had more complaints in more services from users and (for assessment and treatment units and low secure units only) from relatives. In low secure units, they had more adult protection referrals. On the other hand, IH units used fewer agency staff in assessment and treatment units (though more in medium secure units) and provided more appraisals, more visits from commissioners, more consultations with GPs and dentists and more use of whistleblowing procedures by staff. A quarter of people in NHS assessment and treatment units had finished treatment but had no plan for discharge. Conclusions Further research is required to determine the reasons for differences observed between IH and NHS units. The large number of people who have finished treatment emphasizes the importance of developing housing in the community with sufficiently skilled support. The large number of patients in low secure services raises the question whether this type of service is recreating the intellectual disability institutions which government policy intends to close.
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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 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 0.001 |
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".