Applying behavioural activation (BA) and simulation-based learning (SBL) approaches to enhance MSW students’ competence in suicide risk assessment, prevention, and intervention (SRAPI)
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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 it