GATEKEEPER TRAINING FOR SUICIDE PREVENTION IN FIRST NATIONS COMMUNITY MEMBERS: A RANDOMIZED CONTROLLED TRIAL
Bibliographic record
Abstract
BACKGROUND: Gatekeeper training aims to train people to recognize and identify those who are at risk for suicide and assist them in getting care. Applied Suicide Intervention Skills Training (ASIST), a form of gatekeeper training, has been implemented around the world without a controlled evaluation. We hypothesized that participants in 2 days of ASIST gatekeeper training would have increased knowledge and preparedness to help people with suicidal ideation in comparison to participants who received a 2-day Resilience Retreat that did not focus on suicide awareness and intervention skills (control condition). METHODS: First Nations on reserve people in Northwestern Manitoba, aged 16 years and older, were recruited and randomized to two arms of the study. Self-reported measures were collected at three time points-immediately pre-, immediately post-, and 6 months post intervention. The primary outcome was the Suicide Intervention Response Inventory, a validated scale that assesses the capacity for individuals to intervene with suicidal behavior. Secondary outcomes included self-reported preparedness measures and gatekeeper behaviors. RESULTS: In comparison with the Resilience Retreat (n = 24), ASIST training (n = 31) was not associated with a significant impact on all outcomes of the study based on intention-to-treat analysis. There was a trend toward an increase in suicidal ideation among those who participated in the ASIST in comparison to those who were in the Resilience Retreat. CONCLUSIONS: The lack of efficacy of ASIST in a First Nations on-reserve sample is concerning in the context of widespread policies in Canada on the use of gatekeeper training in suicide prevention.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".