Adverse Childhood Experiences and Offending as a Function of Acquired Brain Injury Among Men in a High Secure Forensic Psychiatric Hospital
Bibliographic record
Abstract
BACKGROUND: Acquired brain injury (ABI) is a serious problem that disproportionately affects individuals in correctional services, but relatively little is known about ABI risks and correlates in forensic psychiatric services. METHODS: means cluster analysis was employed to assess risk factors by which men with ABI could be identified and multivariate general linear models were used to identify ABI-related differences in offending history and in-hospital aggression. RESULTS: One-fifth of the men had a documented ABI indicator. Based on our cluster analysis, ABI was more likely to be identified by greater adverse childhood experiences (ACEs), more health problems from pregnancy to childhood, and lower socioeconomic status, suggesting that ABI within the forensic context is associated with greater developmental disadvantage. Men with ABI had more serious pre-admission offences, but not more serious admission offences or in-hospital aggression. Men with ABI were more likely than those without to have higher scores on the Violence Risk Appraisal Guide or to be diagnosed with mood and personality disorders, and less likely to have a schizophrenia diagnosis, suggesting an association between ABI and general mental health pathologies but not with psychotic illness. CONCLUSIONS: The disadvantage of ABI among men in forensic psychiatric hospitals is most likely evinced in antisocial behaviour rather than serious mental illness. Given that ACEs are likely to precede or co-occur with ABI, strategies that mitigate ACEs hold promise for ABI prevention.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".