Applying behavioural activation (BA) and simulation-based learning (SBL) approaches to enhance MSW students’ competence in suicide risk assessment, prevention, and intervention (SRAPI)
Bibliographic record
Abstract
Providing adequate training on suicide assessment and intervention for students is of utmost importance in social work education. Given students’ anxiety around working with clients who have potential risk of suicide, scholars underline the benefits of experiential learning to enhance competence in MSW students in suicide risk assessment, prevention, and intervention (SRAPI). This paper illustrates how a required advanced mental health practice course has utilized simulation-based learning (SBL) and behavioural activation (BA) to foster specific skill building in SRAPI. Using a flipped classroom approach permitted students to increase knowledge related to suicide and BA using a self-guided online format. They also had opportunities to apply the online learning to classroom learning through practice activities. SBL was an innovative pedagogical approach that was critical for SRAPI training as it provides students with opportunities to engage in simulated practice with no harm to real clients. Using BA, students learned to conduct detailed functional analysis of suicide risks with corresponding graded tasks to mitigate suicidal risk. This paper discusses lessons learned from the SRAPI training and makes suggestions for future research and educational policies in social work education.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".