Age onset of offending and serious mental illness among forensic psychiatric patients: A latent profile analysis
Bibliographic record
Abstract
BACKGROUND: Developmental typologies regarding age of onset of violence and offending have not routinely taken account of the role of serious mental illness (SMI), and whether age of onset of offending in relation to onset of illness impacts on the manifestation of offending over the life course. AIMS: To test whether forensic psychiatric patients can be classified according to age of onset of SMI and offending, and, if so, whether subtypes differ by sex. METHODS: Details of all 511 patients enrolled into a large forensic mental health service in Ontario, Canada, in 2011 or 2012 were collected from records. RESULTS: A latent profile analysis supported a 2-class solution in both men and women. External validation of the classes demonstrated that those with a younger age onset of serious mental illness and offending were characterised by higher levels of static risk factors and criminogenic need than those whose involvement in both mental health and criminal justice systems was delayed to later life. CONCLUSIONS: Our findings present a new perspective on life course trajectories of offenders with SMI. While analyses identified just two distinct age-of-onset groups, in both the illness preceded the offending. The fact that our sample was entirely drawn from those hospitalised may have introduced a selection bias for those whose illness precedes offending, but findings underscore the complexity and level of need among those with a younger age of onset. Copyright © 2018 John Wiley & Sons, Ltd.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".