Assessment of Risk for Seclusion Among Forensic Inpatients: Validation and Modification of the Risk of Administrative Segregation Tool (RAST)
Bibliographic record
Abstract
Seclusion is used in psychiatric care to protect patients and staff or to manage aggression but may have adverse effects. The ability to identify at-risk patients could help reduce seclusion. This study tested the Risk of Administrative Segregation Tool's (RAST) ability to predict any seclusions among 229 male forensic inpatients followed for up to 1 year of hospitalization, and days spent secluded, controlling for length of stay. RAST scores were lower than in correctional samples. The RAST did not predict seclusions in Year 1, but modification of three items to fit the forensic population (RAST-F) offered a small improvement. Among 62 patients hospitalized for more than 1 year, the RAST significantly predicted seclusions in Year 2, and the modifications improved prediction. The present modest findings support the RAST's potential to help identify patients most in need of clinical efforts to avert seclusion. Replication in larger samples, including female patients, is needed.
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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 | 0.002 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".