Predicting Seclusion in a Medium Secure Forensic Inpatient Setting
Bibliographic record
Abstract
The current study investigated the predictive validity of several popular risk-related assessment instruments with respect to seclusion. The Hare Psychopathy Checklist-Revised (PCL-R; Hare, 2003), Version 2 of the HCR-20 (HCR-20; Webster, Douglas, Eaves, & Hart, 1997), the Violence Risk Appraisal Guide (VRAG; Harris, Rice, & Quinsey, 1998), and the Level of Service Inventory-Revised (LSI-R; Andrews & Bonta, 1995) were coded from institutional files for a sample of 130 patients from a medium-secure forensic inpatient unit. Seclusion was indexed in terms of total number of seclusions during a period of two years and total time spent in seclusion. ROC analyses indicated that all instruments had small to moderate and significant predictive validity with respect to frequency of seclusion, but were less strongly predictive of duration of seclusion. Overall, Factor 2 of the PCL-R was the best predictor of seclusion. Implications for practice and the necessity for future and local research are discussed.
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 | 0.001 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| 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".