Perspective On Excellence in Forensic Mental Health Services: What We Can Learn From Oncology and Other Medical Services
Bibliographic record
Abstract
We propose that excellence in forensic and other mental health services can be recognized by the abilities necessary to conduct randomized controlled trials (RCTs) and equivalent forms of rigorous quantitative research to continuously improve the outcomes of treatment as usual (TAU). Forensic mental health services (FMHSs) are growing, are high cost, and increasingly provide the main access route to more intensive, organized, and sustained pathways through care and treatment. A patient newly diagnosed with a cancer can expect to be enrolled in RCTs comparing innovations with the current best TAU. The same should be provided for patients newly diagnosed with severe mental illnesses and particularly those detained and at risk of prolonged periods in a secure hospital. We describe FMHSs in four levels 1 to 4, basic to excellent, according to seven domains: values or qualities, clinical organization, consistency, timescale, specialization, routine outcome measures, and research. Excellence is not elitism. Not all centers need to achieve excellence, though all should be of high quality. Services can provide each population with a network of centers with access to one center of excellence. Excellence is the standard needed to drive the virtuous circle of research and development that is necessary for teaching, training, and the pursuit of new knowledge and better outcomes. Substantial advances in treatment of severe mental disorders require a drive at a national and international level to create services that meet these standards of excellence and are focused, active, and productive to drive better functional outcomes for service users.
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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 | 0.052 | 0.081 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.006 | 0.030 |
| Scholarly communication | 0.014 | 0.044 |
| Open science | 0.005 | 0.013 |
| Research integrity | 0.026 | 0.046 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".