What is known about suicide prevention gatekeeper training and directions for future research
Bibliographic record
Abstract
INTRODUCTION: Suicide prevention training that teaches skills to support a person experiencing thoughts of suicide and create community support networks, often termed, "gatekeeper" training (GKT), has been a longstanding pillar of international, national, and local suicide prevention efforts. GKT aims to improve knowledge, attitudes, and self-efficacy in identifying individuals at risk for suicide, hopefully enhancing one's willingness and ability to intervene with a person experiencing a crisis. However, little is known about GKT's effectiveness in creating the essential behavior change (e.g., increase in intervening behaviors) it sets out to accomplish. METHODS: This paper explores the history and theoretical background of GKT, reviews the current state of research on GKT, and provides framing and recommendations for next steps to advance research and practice around GKT. RESULTS & CONCLUSION: Through positioning GKT appropriately within the field of suicide prevention, we argue that the field of suicide prevention needs more rigorous research around GKT that includes long-term follow-up data on usage of skills learned during training, data on outcomes of those who have received an intervention from a trained gatekeeper, and the integration of implementation science to further our understanding of which trainings are appropriate for which helpers.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.009 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.006 | 0.009 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".