Teaching Note—Enhancing Social Work Education in Mental Health, Addictions, and Suicide Risk Assessment
Bibliographic record
Abstract
Social workers play a critical role in assessing and treating individuals and families with mental health and addiction concerns. Although social workers are key professionals in the mental health workforce, there are gaps in the training and education of mental health, addictions, and suicide, and many students are inadequately prepared for field education. Simulation-based learning is an exemplar method of teaching and assessing practice competencies across several health-care professions including social work. This teaching note describes a simulation-based learning activity in which MSW students build competence in mental health, substance use, and suicide risk assessments with standardized clients. This innovation is integrated in a social work practice in mental health course and was developed in partnership with a community mental health and addiction treatment center. Through this partnership, we developed core competencies, case scenarios, as well as teaching resources and assessment instruments. An advisory committee consisting of MSW students, faculty members, and field instructors evaluated the simulation-based learning innovation and made recommendations for the next iteration. Implications for teaching social work practice in mental health are discussed.
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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 | 0.004 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.015 | 0.003 |
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".