Non-Suicidal Self-Injury Within Offender Populations: A Systematic Review
Bibliographic record
Abstract
Non-suicidal self-injury (NSSI), defined as deliberate self-directed tissue damage, presents a serious health concern for offender populations. Approximately one-third of offenders report a history of NSSI, and it is the most common reason for mental health treatment within correctional settings. To date, no review exists with a specific focus on NSSI in criminal justice contexts. Therefore, the primary aim of this article is to review research on NSSI within correctional settings. Specifically, we explore the role of risk factors for NSSI. We also examine the functions of NSSI within correctional contexts. In addition, we evaluate the evidence for potential assessment tools and treatments for NSSI. Taken together, our review suggests that risk factors for NSSI must be considered differently in correctional settings, due to the high base rates of these vulnerabilities. Further, although environmental control is a more salient function of NSSI within correctional settings, the primary motive for engaging in this behavior remains emotion regulation. Finally, despite the emergence of several promising treatments for NSSI within correctional settings, larger scale studies are necessary to determine the efficacy of these interventions.
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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 | 0.003 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 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) | 0.004 | 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".