Neuroscience and Social Work: A Simulation-Based Workshop for Social Work Students
Bibliographic record
Abstract
The teaching of practice-based techniques in social work education is crucial for preparing social work students for clinical practice. Increasingly, neuroscientific and brain-based concepts are being incorporated into clinical social work practice, highlighting their importance for consideration into social work education. Potential benefits of including neuroscience-based concepts into social work curricula include their links to understanding human behavior and development, stress, trauma, and substance misuse. However, there remains a gap in research investigating how best to teach these concepts to future social workers. In response, this paper describes a pilot project that incorporated the teaching of neuroscience skills with simulation-based learning in social work education. The study explored students’ experience of a 1.5-day online workshop on fundamentals of neuroscience in social work through a post-workshop survey and reflexive thematic analysis. Study findings reveal benefits of simulation in teaching specific brain-based clinical skills and techniques, and in the use of a workshop-model approach. The paper provides recommendations for incorporating neuroscience concepts into teaching and practice, and suggests the usefulness of simulation to develop these skills.
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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 | 0.008 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.004 | 0.002 |
| 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".