Understanding the interface between the Mental Capacity Act’s Deprivation of Liberty Safeguards (MCA-DoLS) and the Mental Health Act (MHA)
Bibliographic record
Abstract
The background to the Mental Capacity Act's Deprivation of Liberty Safeguards (the MCA-DoLS) regulations, which were introduced in 2009, lies in the European Convention on Human Rights.Their aim is to protect adults with a mental disorder who lack capacity to make decisions about arrangements for their care and treatment in psychiatric or general ('acute') hospitals and care homes and may be at risk of having limits that go beyond mere restriction or restraint placed on their freedom of movement.According to the regulations that set out the MCA-DoLS procedure, a deprivation of liberty, which must always be in the 'best interests' of the person to whom it applies, can only be authorised by an independent body, the Supervisory Body, following an application by a Managing Authority on behalf of a clinical team and the completion of six assessments.In contrast with care homes, in settings such as psychiatric hospitals where the Mental Health Act (MHA) can also be used, a decision may need to be made between the two statutory frameworks for civil detention.In order to provide recommendations for policy and practice, we set out to examine, first, how practitioners make decisions between the MCA-DoLS and the MHA, and, secondly, the characteristics of, and outcomes for, men and women for whom applications for, and authorisations of, a deprivation of liberty are made.The study, which was carried out from November 2010 to November 2011 used information from a number of sources.Three Supervisory Bodies, covering an ethnically diverse population of 1.7 million across metropolitan, urban, and rural areas provided anonymised completed copies of the Department of Health's standard application and key assessment Forms.We also analysed the text of other standard Forms; carried out semi-structured interviews with individuals with key roles in the application, assessment and/or authorisation process; presented brief clinical vignettes to psychiatrists and others; attended events and meetings with practitioners; and held discussions with representatives of the three Supervisory Bodies to confirm issues relating to emerging themes.In addition, data relating to the MCA-DoLS from the Health and Social Care Information Centre were collected and compared with information about the use of the MHA in order to examine and compare the characteristics and experiences of men and women subject to the two different legal frameworks.While concerns were expressed by practitioners regarding, for example, the Code of Practice and the status of guidance that is occasionally issued by the Department of We are grateful to many people: in particular, the representatives of the three Supervisory Bodies: Ms Emma Ekwegh, Mr Stephen Vickers and Mr Joseph Yow, and their colleagues,
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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 | 0.030 | 0.068 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.005 | 0.039 |
| Scholarly communication | 0.014 | 0.020 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.005 | 0.010 |
| Insufficient payload (model declined to judge) | 0.004 | 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".