Evaluation of the outcomes of the Quebec provincial suicide prevention gatekeeper training on knowledge, recognition of attitudes, perceived self‐efficacy, intention to help, and helping behaviors
Bibliographic record
Abstract
INTRODUCTION: Gatekeeper (GK) training is a suicide prevention strategy in which community members learn to identify individuals at risk of suicide and refer them for appropriate help. Despite its widespread use, few studies have investigated its effects, including changes in helping behaviors. AIMS: To assess the impact of GK training on participants' knowledge, recognition of the influence of attitudes, perceived self-efficacy, intention to help and helping behaviors, and to identify variables associated with GK behaviors. METHODS: Mixed linear effects and forward stepwise logistic regressions were used to analyze data from 159 participants receiving the Quebec Provincial GK Training program offered by five different suicide prevention centers using pretest, posttest and 6-month follow-up questionnaires. RESULTS: Participants' knowledge of the GK role and suicide prevention, intention to help, self-efficacy, knowledge of services, and recognition of the influence of attitudes significantly increased following training. Most changes decreased at follow-up but remained higher than at pretest. Lower levels of education and higher intention to help were significant predictors of engaging in helping behaviors in the first 6 months after receiving training. CONCLUSIONS: The Quebec GK training appears to be effective in preparing participants for their role but does not appear to significantly increase helping behaviors.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".