Risk stratification and the care pathway
Bibliographic record
Abstract
OBJECTIVES: It was hypothesised that patients admitted to forensic mental health facilities are stratified along the pathway through care according to levels of need. Level of risk and psychopathology should vary with different levels of security. METHOD: Seventy-five men in a forensic hospital were interviewed by three trained clinicians using the HCR-20 (Historical Clinical Risk Assessment) - clinical and risk items, The Health of the Nation Scales - Secure (HoNOS-SECURE), PANSS (Positive and Negative Syndrome Scale), GAF (Global Assessment of Functioning) and the CANFOR (Camberwell Assessment of need Forensic Version). RESULTS: The mean scores on a variety of clinical measures were higher in admission/high security areas and progressively lower in rehabilitation and pre-discharge areas. As patients moved through the pathways of care, they improved in a number of areas including psychiatric morbidity, risk, function, unmet needs. The following results stratified significantly; the HCR-20 summated clinical and risk (F = 9.2, df = 5, p < 0.001), the HoNOS secure (F = 18.2, df = 5, p < 0.001), PANSS (positive, general and total), GAF, staff and user unmet needs on the CANFOR. CONCLUSIONS: The data indicate that the theoretical organisation of the units of the hospital into high, medium and low security units to form a coherent pathway through care is reflected in practice. This is a transparent route out of secure care in which restrictions are proportionate to risk and supports proportionate to need. It is unclear whether alternative models, consisting of a series of generic unstratified units for admission and discharge, all at the same level of therapeutic security, allow for the provision of treatment programmes and relational interventions appropriate to the patient's stage of recovery and rehabilitation.
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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 | 0.001 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".