Question, persuade, refer suicide prevention training among individuals employed in the veterinary industry
Bibliographic record
Abstract
Objective: Individuals who work in the veterinary industry are at increased risk of poor mental health outcomes and suicide. Rates of serious psychological distress has been worsening amongst this population over recent years. Veterinarians and veterinary technicians are up to five times more likely to attempt suicide in comparison to the general public. There are limited data available on veterinary assistants and additional support staff that work in the industry. Question, Persuade, Refer (QPR) is a suicide prevention training program shown to have both short-term and long-term benefits. QPR is offered for free to members of the American Veterinary Medical Association; however, there are no known studies looking at QPR’s effectiveness within the veterinary industry. The purpose of this project is to determine if QPR training is effective in increasing the knowledge surrounding suicide prevention so that individuals working in the veterinary industry are better equipped to identify and refer at-risk colleagues. Methods: Participants completed a pretest, a QPR online education module, and a posttest. Descriptive statistics and a paired Wilcoxon signed rank test were used to analyze the results. Results: QPR suicide prevention training resulted in an increase in score for all questions between the pre- and post-test, with seven out of nine having statistical significance. Conclusions: QPR is a suicide prevention training that can be used to teach individuals who do not have a background in mental health how to recognize warning signs of suicide, interact with an individual who may be experiencing suicidal thoughts, and guide the individual to seek professional help. Increasing awareness and knowledge on the topic can help individuals within the veterinary industry identify at-risk colleagues, improve mental health outcomes, and reduce the number of suicides within the industry.
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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 | 0.011 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".