Violent reoffending in people released from prison: psychiatric epidemiology, risk assessment and psychological interventions
Bibliographic record
Abstract
Violence was identified as a global public health concern by the World Health Assembly nearly three decades ago. Despite reported decreases in violent crime in many countries, reoffending rates worldwide remain high. Amongst people released from prison, there are some at high-risk of perpetrating interpersonal violence. Identifying these key individuals, who are most in need of effective interventions to prevent future criminality, is crucial to reducing societal violence, as their contribution to this major problem is considerable. In this thesis, I focus on violence risk assessment and prevention of future violence in people released from prison by employing methods from psychiatric epidemiology, public mental health and prediction modelling. I start by estimating the prevalence of a modifiable risk factor for violence (i.e. treatable mental disorders) amongst adolescents in juvenile detention and correctional facilities. I select this subgroup of the global prison population as most severe mental disorders emerge in late adolescence, and thus this period provides a critical window to improve prognosis and intervention. My second and third studies externally validate a novel, scalable and transparent violence prediction model—the Oxford Risk of Recidivism (OxRec) tool—in two new countries. I investigate the predictive ability of OxRec in both lower middle-income and high-income settings using data from Tajikistan and England to identify individuals who could be targeted for empirically supported interventions in prison and on release. Lastly, I evaluate the effectiveness of widely implemented psychological interventions for people in prison to reduce offending after release. I synthesise the evidence by solely including randomised controlled trials to identify the current most effective treatments, and inform future evidence-based research and policy in this area.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".