Curricular Considerations: The Process of Integrating Simulation-Based Learning Into a Social Work Communication and Interviewing Skills Course
Bibliographic record
Abstract
Simulation-based learning (SBL) is used as an educational tool within health professions education disciplines, including medicine and nursing. More recently, SBL has been applied within social work education as a growing body of research, demonstrating its efficacy in teaching social work competencies. SBL provides students with safe and practical opportunities to apply their skills within highly realistic settings. The growing body of literature on SBL within social work education informed the development of a new Bachelor of Social Work (BSW) course focused on communication and interviewing skills at Wilfrid Laurier University. The purpose of this editorial is to provide an example of a collaborative process for integrating simulation as a pedagogy within course design. This collaborative process involved four stages: designing the course, preparing, and revising the simulations, facilitating the simulations, and evaluating student learning and experience. This editorial may assist instructors by providing a pedagogical framework for incorporating SBL into both new and existing curricula.
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How this classification was reachedexpand
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 | 0.047 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.004 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".