The need for a culturally-tailored gatekeeper training intervention program in preventing suicide among Indigenous peoples: a systematic review
Bibliographic record
Abstract
BACKGROUND: Suicide is a leading cause of death among Indigenous youth worldwide. The aim of this literature review was to determine the cultural appropriateness and identify evidence for the effectiveness of current gatekeeper suicide prevention training programs within the international Indigenous community. METHOD: Using a systematic strategy, relevant databases and targeted resources were searched using the following terms: 'suicide', 'gatekeeper', 'training', 'suicide prevention training', 'suicide intervention training' and 'Indigenous'. Other internationally relevant descriptors for the keyword "Indigenous" (e.g. "Maori", "First Nations", "Native American", "Inuit", "Metis" and "Aboriginal") were also used. RESULTS: Six articles, comprising five studies, met criteria for inclusion; two Australian, two from USA and one Canadian. While pre and post follow up studies reported positive outcomes, this was not confirmed in the single randomised controlled trial identified. However, the randomised controlled trial may have been underpowered and contained participants who were at higher risk of suicide pre-training. CONCLUSION: Uncontrolled evidence suggests that gatekeeper training may be a promising suicide intervention in Indigenous communities but needs to be culturally tailored to the target population. Further RCT evidence is required.
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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 | 0.008 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.005 | 0.005 |
| 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) | 0.004 | 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".