Recidivism and Inmate Mental Illness
Bibliographic record
Abstract
Purpose: With over 700,000 mentally ill inmates are held in U.S. jails and prisons, this study provides a comprehensive assessment of the effect of mental illness among released prisoners on a series of re-entry recidivism outcomes. Methods: Using a cohort of 200,889 inmates released from Florida prisons from 2004 to 2011, several recidivism outcomes are examined among 40,145 individuals with a mental health diagnosis and 10,826 with a serious mental illness are compared with inmates without a mental illness diagnosis. We control for a host of factors known to influence recidivism outcomes using binary logistic regression for one, two, and three year follow-up periods and survival analysis to assess the timing to recidivism. Results: Inmates diagnosed with any type of mental illness are significantly more likely to recidivate and among inmates with a mental illness, those diagnosed with a serious mental condition are significantly more likely to recidivate than those with a less serious mental illness diagnosis. Conclusions: Policies and practices need to ensure that in-prison and community mental health systems have sufficient resources and capacity to adequately address the needs of inmates with mental health issues to reduce the likelihood of these individuals re-offending and ultimately returning to prison.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".