Psychosocial interventions for the prevention of self-harm repetition: protocol for a systematic review and network meta-analysis
Bibliographic record
Abstract
INTRODUCTION: Suicide is an important public health problem. Providing evidence-based psychosocial interventions to individuals presenting with self-harm is recognised as an important suicide prevention strategy. Therefore, it is crucial to understand which intervention is most effective in preventing self-harm repetition. We will evaluate the comparative efficacy of psychosocial interventions for the prevention of self-harm in adults. METHODS AND ANALYSIS: We will perform a systematic review and network meta-analysis (NMA) of randomised controlled trials (RCTs) testing psychosocial interventions for the prevention of self-harm repetition. We will include RCTs in adults (mean age: 18 years or more) who presented with self-harm in the 6 months preceding enrolment in the trial. Interventions will be categorised according to their similarities and underpinning theoretical approaches (eg, cognitive behavioural therapy, case management). A health sciences librarian will update and adapt the search strategy from the most recent Cochrane pairwise systematic review on this topic. The searches will be performed in MEDLINE (Ovid), Embase (Ovid), PsycInfo (Ovid), CINAHL (EBSCO), Cochrane Central (Wiley), Cochrane Protocols (Wiley), LILACS and PSYNDEX from 1 July 2020 (Cochrane review last search date) to 1 September 2023. The primary efficacy outcome will be self-harm repetition. Secondary outcomes will include suicide mortality, suicidal ideation and depressive symptoms. Retention in treatment (ie, drop-outs rates) will be analysed as the main acceptability outcome. Two reviewers will independently assess the study eligibility and risk of bias (using RoB-2). An NMA will be performed to synthesise all direct and indirect comparisons. Ranked forest plots and Vitruvian plots will be used to represent graphically the results of the NMA. Credibility of network estimates will be evaluated using Confidence in NMA (CINeMA). ETHICS AND DISSEMINATION: As this is the protocol for an aggregate-data level NMA, ethical approval will not be required. Results will be disseminated at national/international conferences and in peer-review journals. TRIAL REGISTRATION NUMBER: CRD42021273057.
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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 | 0.068 | 0.096 |
| Meta-epidemiology (narrow) | 0.008 | 0.005 |
| Meta-epidemiology (broad) | 0.027 | 0.037 |
| Bibliometrics | 0.013 | 0.012 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.006 | 0.006 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.089 | 0.007 |
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".