Mental health screening tools in correctional institutions: a systematic review
Bibliographic record
Abstract
BACKGROUND: Past studies have identified poor rates of detection of mental illness among inmates. Consequently, mental health screening is a common feature to various correctional mental health strategies and best practice guidelines. However, there is little guidance to support the selection of an appropriate tool. This systematic review compared the sensitivity and specificity of mental health screening tools among adult jail or prison populations. METHODS: A systematic review of MEDLINE and PsycINFO up to 2011, with additional studies identified from a search of reference lists. Only studies involving adult jail or prison populations, with an independent measure of mental illness, were included. Studies in forensic settings to determine fitness to stand trial or criminal responsibility were excluded. Twenty-four studies met all inclusion and exclusion criteria for the review. All articles were coded by two independent authors. Study quality was coded by the lead author. RESULTS: Twenty-two screening tools were identified. Only six tools have replication studies: the Brief Jail Mental Health Screen (BJMHS), the Correctional Mental Health Screen for Men (CMHS-M), the Correctional Mental Health Screen for Women (CMHS-W), the England Mental Health Screen (EMHS), the Jail Screening Assessment Tool (JSAT), and the Referral Decision Scale (RDS). A descriptive summary is provided in lieu of use of meta-analytic techniques due to the lack of replication studies and methodological variations across studies. CONCLUSIONS: The BJMHS, CMHS-M, CMHS-W, EMHS and JSAT appear to be the most promising tools. Future research should consider important contextual factors in the implementation of a screening tool that have received little attention. Randomized or quasi-randomized trials are recommended to evaluate the effectiveness of screening to improve the detection of mental illness compared to standard practices.
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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 | 0.013 | 0.060 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.009 |
| Bibliometrics | 0.013 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".