Risk factors for self-harm in prison: a systematic review and meta-analysis
Bibliographic record
Abstract
Background Self-harm is a leading cause of morbidity in prisoners. Although a wide range of risk factors for self-harm in prisoners has been identified, the strength and consistency of effect sizes is uncertain. We aimed to synthesise evidence and assess the risk factors associated with self-harm inside prison. Methods In this systematic review and meta-analysis, we searched four electronic databases (PubMed, Embase, Web of Science, and PsycINFO) for observational studies on risk factors for self-harm in prisoners published from database inception to Oct 31, 2019, supplemented through correspondence with authors of studies. We included primary studies involving adults sampled from general prison populations who self-harmed in prison and a comparison group without self-harm in prison. We excluded studies with qualitative or ecological designs, those that reported on lifetime measures of self-harm or on selected samples of prisoners, and those with a comparison group that was not appropriate or not based on general prison populations. Data were extracted from the articles and requested from study authors. Our primary outcome was the risk of self-harm for risk factors in prisoners. We pooled effect sizes as odds ratios (OR) using random effects models for each risk factor examined in at least three distinct samples. We assessed study quality on the basis of the Newcastle-Ottawa Scale and examined between-study heterogeneity. The study protocol was registered with PROSPERO, CRD42018087915. Findings We identified 35 independent studies from 20 countries comprising a total of 663 735 prisoners, of whom 24 978 (3·8%) had self-harmed in prison. Across the 40 risk factors examined, the strongest associations with self-harm in prison were found for suicide-related antecedents, including current or recent suicidal ideation (OR 13·8, 95% CI 8·6–22·1; I 2 =49%), lifetime history of suicidal ideation (8·9, 6·1–13·0; I 2 =56%), and previous self-harm (6·6, 5·3–8·3; I 2 =55%). Any current psychiatric diagnosis was also strongly associated with self-harm (8·1, 7·0–9·4; I 2 =0%), particularly major depression (9·3, 2·9–29·5; I 2 =91%) and borderline personality disorder (9·2, 3·7–22·5; I 2 =81%). Prison-specific environmental risk factors for self-harm included solitary confinement (5·6, 2·7–11·6; I 2 =98%), disciplinary infractions (3·5, 1·2–9·7; I 2 =99%), and experiencing sexual or physical victimisation while in prison (3·2, 2·1–4·8; I 2 =44%). Sociodemographic (OR range 1·5–2·5) and criminological (1·8–2·3) factors were only modestly associated with self-harm in prison. We did not find clear evidence of publication bias. Interpretation The wide range of risk factors across clinical and custody-related domains underscores the need for a comprehensive, prison-wide approach towards preventing self-harm in prison. This approach should incorporate both population and targeted strategies, with multiagency collaboration between the services for mental health, social care, and criminal justice having a key role. Funding Wellcome Trust.
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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 | 0.021 | 0.047 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.020 | 0.038 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".