Excellence in forensic psychiatry services: international survey of qualities and correlates
Bibliographic record
Abstract
BACKGROUND: Excellence is that quality that drives continuously improving outcomes for patients. Excellence must be measurable. We set out to measure excellence in forensic mental health services according to four levels of organisation and complexity (basic, standard, progressive and excellent) across seven domains: values and rights; clinical organisation; consistency; timescale; specialisation; routine outcome measures; research and development. AIMS: To validate the psychometric properties of a measurement scale to test which objective features of forensic services might relate to excellence: for example, university linkages, service size and integrated patient pathways across levels of therapeutic security. METHOD: A survey instrument was devised by a modified Delphi process. Forensic leads, either clinical or academic, in 48 forensic services across 5 jurisdictions completed the questionnaire. RESULTS: Regression analysis found that the number of security levels, linked patient pathways, number of in-patient teams and joint university appointments predicted total excellence score. CONCLUSIONS: Larger services organised according to stratified therapeutic security and with strong university and research links scored higher on this measure of excellence. A weakness is that these were self-ratings. Reliability could be improved with peer review and with objective measures such as quality and quantity of research output. For the future, studies are needed of the determinants of other objective measures of better outcomes for patients, including shorter lengths of stay, reduced recidivism and readmission, and improved physical and mental health and quality of life.
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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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 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".