Change in Severity of Mental Disorder of Remand Prisoners: An Observational Group-Based Trajectory Study
Bibliographic record
Abstract
BACKGROUND: Mental disorder is common among prisoners; however, little is known about how illness severity changes during incarceration, and especially to what extent there are different trajectories of change. AIMS: Our aims were to investigate trajectories of illness severity among male and female inmates with serious mental disorders, and to investigate whether clinical or demographic variables are associated with different trajectories. METHODS: We carried out a retrospective cohort study of newly remanded inmates who had three or more serial measures of illness severity as measured by psychiatrists using the Clinical Global Impression-Corrections (CGI-C), and used group-based trajectory modelling to identify trajectories. We investigated whether clinical and demographic variables were associated with different groups. RESULTS: We found an overall reduction in the severity of illness (mean change in CGI-C score -0.74, SD 1.5), with women showing greater improvement than men. We identified three distinct trajectories among men and three among women, all showing improvement in illness severity. Approximately 15% of the entire cohort had full resolution of symptoms, whereas the remainder showed partial improvement. Women, younger inmates, and those with substance use disorders were more likely to have full resolution of symptoms. CONCLUSIONS: Although most prisoners showed improvement, and a small proportion had full resolution of symptoms, a significant number continued to have moderately severe symptoms. There is a need for comprehensive treatment within the detention centre, but also a need for transfer to hospital for those with severe symptoms as improvement within the correctional setting tends to be modest.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".