Getting People with Serious Mental Illnesses on Track: Insights from the Health-Based Model of Desistance
Bibliographic record
Abstract
Scholarship from the life-course paradigm has produced much evidence on the crime-reducing benefits of turning points such as securing a good job or developing a stable, positive relationship. Building on these insights, recent work has demonstrated the utility of incorporating health into the study of desistance; for various reasons, both mental and physical health statuses have been shown to influence the likelihood of achieving these key life-course milestones. What is less well understood, however, is how mental and physical health may interact with each other and how this model applies to certain salient subgroups in criminal justice, such as those with serious mental illnesses. Importing the mental health–crime literature, we examine both the main and synergistic effects of mental and physical health on employment focus and relationship worry among a sample of persons with serious mental illness (N = 184). Findings from logistic and ordinary least squares regression models reveal that better physical health is associated with improved employment focus and that this effect is moderated by mental health status. In addition, better physical health is associated with a decrease in worry over one’s relationships. These findings point to the value of including physical and mental health states in life-course and desistance research, studies of persons with serious mental illnesses, and intervention and policy efforts to improve lives and promote desistance.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".