Bibliographic record
Abstract
PURPOSE OF REVIEW: Reports of higher than community rates of mental disorder in incarcerated populations first appeared in the mid-1970s. These findings have been confirmed over the past three decades in numerous studies across a wide spectrum of forensic settings. Recent research has benefited from enhanced methodological sophistication, and reliable rates across clinical domains and divergent forensic population groups are now available. This article reviews the literature on the prevalence of mental illness in forensic settings over the past 10 years, with special reference to specific subgroups. RECENT FINDINGS: Overall rates of any mental disorder, including personality disorder and addiction, remain high, in general ranging between 55% and 80%. The findings of recent, systematic surveys and of 22 studies reviewed here reveal rates of psychosis that are several times higher in correctional settings than in the community. Mood disorder rates are elevated also, with higher morbidity reported for women than for men. Findings in specialized populations indicate similarly elevated rates of mental disorder among adolescent and geriatric prisoners, while addiction rates rank highest across all population domains. SUMMARY: The prevalence of psychiatric illness in correctional settings is significantly elevated, with higher than community rates reported for most mental disorders. It is estimated that in the USA one in five incarcerated persons is afflicted with major psychiatric illness; with an estimated 9-10 million persons imprisoned worldwide, the burden of psychiatric illness in this vulnerable and marginalized population poses a serious challenge to researchers and clinicians alike.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".