Adverse childhood experiences in forensic psychiatric patients: Prevalence and correlates from two independent Canadian samples
Bibliographic record
Abstract
There is a lack of research examining Adverse Childhood Experiences (ACEs) in forensic psychiatric patients despite research consistently demonstrating a relationship between ACEs and later life mental and physical health issues and likelihood of incarceration. The current study sought to examine the relationships between ACEs and indicators of health and offending among forensic psychiatric patients. Medicolegal files were reviewed and coded for 313 patients from two independent high and low/medium security Canadian forensic psychiatric programs. Findings from both samples revealed higher than previously reported average and cumulative rates of childhood adversity compared to the general population, and rates which were comparable to other forensic samples. Both samples revealed positive relationships between ACEs and both mental health and offending history, while differential patterns between samples emerged for physical health outcomes. Results are interpreted via comparisons between the samples, as well as in reference to previous forensic, correctional, clinical, and community research findings. Although it is clear from these results that ACEs are a significant factor in the forensic psychiatric population, more research is needed to better understand the interactions among, and mechanisms through which, ACEs contribute to offending onset and risk, treatment response, and both mental and physical health outcomes.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.006 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.002 | 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".