Gatekeeper Training as a Preventative Intervention for Suicide: A Systematic Review
Bibliographic record
Abstract
OBJECTIVE: Suicide prevention remains a challenge across communities in North America and abroad. We examine a suicide prevention effort that is widely used, termed gatekeeper training. There are 2 aims: review the state of the evidence on gatekeeper training for suicide prevention, and propose directions for further research. METHOD: Studies were identified by searching MEDLINE (PubMed) and PsycINFO from inception to the present for the key words suicide, suicide prevention, and gatekeeper. In addition, a manual scan of relevant articles' bibliographies was undertaken. RESULTS: Gatekeeper training has been implemented and studied in many populations, including military personnel, public school staff, peer helpers, clinicians, and Aboriginal people. This type of training has been shown to positively affect the knowledge, skills, and attitudes of trainees regarding suicide prevention. Large-scale cohort studies in military personnel and physicians have reported promising results with a significant reduction in suicidal ideation, suicide attempts, and deaths by suicide. CONCLUSIONS: Gatekeeper training is successful at imparting knowledge, building skills, and molding the attitudes of trainees; however, more work needs to be done on longevity of these traits and referral patterns of gatekeepers. There is a need for randomized controlled trials. In addition, the unique effect of gatekeeper training on suicide rates needs to be fully elucidated.
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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 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.004 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".