Global systematic review of the effects of suicide prevention interventions in Indigenous peoples
Bibliographic record
Abstract
OBJECTIVE: Suicide rates are often higher in Indigenous than in non-Indigenous peoples. This systematic review assessed the effects of suicide prevention interventions on suicide-related outcomes in Indigenous populations worldwide. METHODS: We searched CINAHL, Embase, PubMed, PsycINFO, ProQuest Dissertations & Theses and Web of Science from database inception to April 2020. Eligible were English language, empirical and peer-reviewed studies presenting original data assessing the primary outcomes of suicides and suicide attempts and secondary outcomes of suicidal ideation, intentional self-harm, suicide or intentional self-harm risk, composite measures of suicidality or reasons for life in experimental and quasi-experimental interventions with Indigenous populations worldwide. We assessed the risk of bias with the Cochrane Risk of Bias Tool and the Risk of Bias Assessment for Non-randomised Studies. FINDINGS: We included 24 studies from Australia, Canada, New Zealand and the USA, comprising 14 before-after studies, 4 randomised controlled trials (RCTs), 3 non-randomised controlled trials, 2 interrupted time-series designs and 1 cohort study. Suicides decreased in four and suicide attempts in six before-after studies. No studies had a low risk of bias. There was insufficient evidence to confirm the effectiveness of any one suicide prevention intervention due to shortage of studies, risk of bias, and population and intervention heterogeneity. Review limitations include language bias, no grey literature search and data availability bias. CONCLUSION: For the primary outcomes of suicides and suicide attempts, the limited available evidence supports multilevel, multicomponent interventions. However, there are limited RCTs and controlled studies.
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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 | 0.020 | 0.066 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.009 |
| Bibliometrics | 0.016 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".