Feasibility of Incorporating the Applied Suicide Intervention Skills Training Program into Nursing Education
Bibliographic record
Abstract
Suicide and mental health are both relevant topics that impact a diverse number of individuals in both personal and professional contexts, such as patients in healthcare. Therefore, it is important that healthcare workers, such as nurses, are trained and well equipped to intervene with at-risk individuals. There is a need for nurses to develop competency to better support and provide the appropriate care for patients with suicidal ideation. The objective of this study is to determine the feasibility of offering Applied Suicide Intervention Skills Training (ASIST) in the Faculty of Nursing at the University of Calgary, and to measure the impact of the workshop on students, staff, and faculty. Post and pre workshop surveys were collected and analyzed using descriptive quantitative and thematic qualitative data. Participants showed overwhelming support by strongly agreeing that suicide intervention program should be included in nursing education. Results suggest the potential benefits of incorporating suicide intervention training for nursing students such as improved knowledge and understanding of suicide. Participants reported improved confidence and preparedness to intervene with at-risk individuals. Participants can apply their skills professionally with patients, or personally with supporting friends and family. Similarly, faculty and staff can support students and colleagues on campus.
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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 | 0.013 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".