A comparative review of international monitoring mechanisms for mental health legislation
Bibliographic record
Abstract
In October 2012 CQC commissioned some research from Bristol University to inform our thinking on how we monitor the use of the Mental Health Act and fulfil our responsibilities under the Optional Protocol to the Convention against Torture (OPCAT).<br/> <br/>One of our priorities for the next three years is to strengthen how we deliver our responsibilities in terms of mental health and mental capacity. We commissioned this research to understand the experiences of other countries in monitoring their mental health legislation, and enable us to move forward with the development of this function according to an international evidence base and knowledge about best practice.<br/> <br/>The research brief asked the researchers to examine aspects of the monitoring arrangements in place in a number of liberal, democratic countries comparable to England. New Zealand, Australia, Canada, Denmark, Sweden, the Netherlands, Ireland and other UK jurisdictions were chosen by the authors for the purposes of this review.<br/> <br/>Three main areas were considered:<br/> The methodology of the visits or inspections<br/> The process for feeding back the information from visits and how this is integrated with the complaints process<br/> How organisations evaluate their effectiveness and the impact of monitoring.<br/> <br/>Some of the findings from the research that have been particularly helpful include:<br/> The confirmation that there is nowhere else in the world that has a ‘ready to use’ product that is directly comparable or relevant to our legislative framework and monitoring responsibilities.<br/> The importance of visiting to understanding patients’ experience from their own perspective.<br/> The support for a collaborative approach with providers wherever possible.<br/> The different methods used for gathering the views and experiences of relatives and carers.<br/> <br/>The research findings are in two parts. The first part discusses relevant themes emerging from the study while the second proposes a number of recommendations.<br/>
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".