Treating Nonsuicidal Self-Injury: A Systematic Review of Psychological and Pharmacological Interventions
Bibliographic record
Abstract
OBJECTIVE: Nonsuicidal self-injury (NSSI), the deliberate, self-inflicted damage of bodily tissue without the intent to die, is associated with various negative outcomes. Although basic and epidemiologic research on NSSI has increased during the last 2 decades, literature on effective interventions targeting NSSI is still emerging. Here, we present a comprehensive, systematic review of existing psychological and pharmacological treatments designed specifically for NSSI, or including outcome assessments examining change in NSSI. METHOD: We conducted a systematic search of PsycINFO, MEDLINE, and ERIC databases to retrieve relevant articles that met inclusion criteria; specifically, uncontrolled and controlled trials that 1) presented quantitative outcome data on NSSI, and 2) clearly differentiated NSSI from suicidal self-injury (SSI). Consistent with the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition, definition of NSSI, we excluded studies examining populations with developmental or intellectual disabilities, or with psychotic disorders. RESULTS: Several interventions appear to hold promise for reducing NSSI, including dialectical behaviour therapy, emotion regulation group therapy, manual-assisted cognitive therapy, dynamic deconstructive psychotherapy, atypical antipsychotics (aripiprazole), naltrexone, and selective serotonin reuptake inhibitors (with or without cognitive-behavioural therapy). Nevertheless, there remains a paucity of well-controlled studies investigating treatment efficacy for NSSI. CONCLUSIONS: Structured psychotherapeutic approaches focusing on collaborative therapeutic relationships, motivation for change, and directly addressing NSSI behaviours seem to be most effective in reducing NSSI. Medications targeting the serotonergic, dopaminergic and opioid systems also have demonstrated some benefits. Future studies employing controlled designs as well as a clear delineation of NSSI and SSI will improve knowledge regarding treatment effects.
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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 | 0.005 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".