Interventions to reduce suicidal thoughts and behaviours among people in contact with the criminal justice system: A global systematic review
Bibliographic record
Abstract
BACKGROUND: People who experience incarceration die by suicide at a higher rate than those who have no prior criminal justice system contact, but little is known about the effectiveness of interventions in other criminal justice settings. We aimed to synthesise evidence regarding the effectiveness of interventions to reduce suicide and suicide-related behaviours among people in contact with the criminal justice system. METHODS: We searched Embase, PsycINFO, MEDLINE, and grey literature databases for articles published between 1 January 2000 and 1 June 2021. The protocol was registered with PROSPERO (CRD42020185989). FINDINGS: Thirty-eight studies (36 primary research articles, two grey literature reports) met our inclusion criteria, 23 of which were conducted in adult custodial settings in high-income, Western countries. Four studies were randomised controlled trials. Two-thirds of studies (n=26, 68%) were assessed as medium quality, 11 (29%) were assessed as high quality, and one (3%) was assessed as low quality. Most had considerable methodological limitations and very few interventions had been rigorously evaluated; as such, drawing robust conclusions about the efficacy of interventions was difficult. INTERPRETATION: More high-quality evidence from criminal justice settings other than adult prisons, particularly from low- and middle-income countries, should be considered a priority for future research. FUNDING: This work was funded by the Australian government's National Suicide Prevention Taskforce. RB is supported by a National Health and Medical Research Council (NHMRC) Emerging Leader Investigator Grant (EL2; GNT2008073). MW is supported by a NHMRC Postgraduate Scholarship (GNT1151103). SF was funded by the NIHR HTA Programme (HTA Project:16/159/09).
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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 | 0.014 | 0.047 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.008 |
| Bibliometrics | 0.011 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".