The Effectiveness of safeTALK Suicide Awareness Training on Staff Members’ Willingness to Engage Suicidal Veterans at Veterans’ Facilities in New York State
Bibliographic record
Abstract
Veterans are an at-risk population in need of more suicide prevention, given the approximately 7,300 veteran suicides per year, 20 per day. In New York State, veteran suicides are a major issue. One particular suicide awareness training program, safeTALK, created by LivingWorks Education, Inc., a Canadian-based public service organization, has been tested in several countries including the United Kingdom, Australia, Canada, and Scotland, but it has not been tested yet in the United States. Since veteran suicides in New York State continue to occur higher than the national average, the purpose of this study was to determine the effectiveness of a mandated 3-hour safeTALK training on 29 staff workers serving veterans at three veterans’ facilities operated by Samaritan Daytop Village in New York State. Prior to the start of the training, each trainee received a pretest and posttest questionnaire packet. The staff trainees represented all three veterans’ facilities, and they worked in various departments. The findings revealed that the 3-hour safeTALK suicide awareness training had a significant positive effect on the staff members’ attitudes, self-efficacy beliefs, and willingness to listen and engage a veteran experiencing suicidal ideation. The training was not successful in changing the preexisting suicidal beliefs of the staff members, but the implications for safeTALK as an effective training method in the United States is discussed. Based on the results of the study, the researcher recommended further studies of other at-risk populations within the United States as well as a more comprehensive review of the qualitative and quantitative questions based on the research population.
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 | 0.001 | 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) | 0.002 | 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".