The HCR–20 and post‐discharge outcome in male patients discharged from medium security in the UK
Bibliographic record
Abstract
Abstract The Historical Clinical Risk–20 (HCR–20) [Webster et al., 1997a,b] is a structured clinical judgement 20–item risk assessment scale, which was developed for use in Canadian forensic facilities. Data on the HCR–20 in UK samples is limited. We examined the predictive validity of the HCR–20 in relation to post‐discharge outcomes in male medium secure patients who had a history of violent offending. HCR–20 scores (omitting the psychopathy item) at the point of discharge were rated in 70 violent patients released into the community using case file data. Relationships between post‐discharge outcomes (reconviction, readmission, self/collateral reports of violence) over a minimum two‐year follow‐up period were rated blind to the HCR–20 score and examined using a variety of risk prediction statistics. HCR–20 score did not predict reconviction, but was a significant predictor of readmission and self/collateral reports of violence. Subscale analyses indicated that all subscales had predictive validity. High scores on the Historical items were better at predicting who would have poor outcome. High scores on the Clinical and Risk scales were better at predicting how long an individual remained at liberty in the community following discharge. The findings suggest that the HCR–20, without the psychopathy rating, is a reasonably robust predictor of self‐reported violence and readmission in medium secure services. Our finding that it did not predict violent recidivism reflects the utility of this measure in the management of risk in violent patients discharged to the community. Aggr. Behav. 30:469–483, 2004. © 2004 Wiley‐Liss, Inc.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".