Clinical Subgroups of Individuals Receiving Care in a Forensic Hospital: A 20-Year Comparison and Treatment Need Implications
Bibliographic record
Abstract
This study examines whether clinical need subgroups in forensic care have evolved over 20 years and explores differences in adverse childhood experiences (ACEs) and assaultive behaviors across groups. This retrospective observational study used data from a hospital for men; Sample 1 (S1, N = 97) collected in 1990, and Sample 2 (S2, N = 176) in 2009–2012. A data-driven multiple correspondence analysis and a cluster analysis was conducted on S1 based on clinical needs, then applied to S2. ACEs and assault proportions within each cluster were compared using chi-square tests. Clusters identified: minimal needs (S1 = 23%, S2 = 20%); psychotic disorders (S1 = 19%, S2 = 17%); personality disorders (S1 = 21%, S2 = 22%); complex needs (S1 = 37%, S2 = 41%). Participants reporting ACEs ( p = .004) and assault ( p < .001) differed between clusters. The third cluster reported the highest ACEs (84.6%) and assault (61.5%).
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 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".