Observational learning in simulation-based social work education: comparison of interviewers and observers
Bibliographic record
Abstract
Simulation-based learning is gaining attention in social work education. While research suggests clear pedagogical benefits for those who engage simulated clients as interviewers, little is known about the learning processes among observers of simulation teaching. Using social learning and social cognitive theories as a theoretical framework, we examined observational learning in simulation by comparing the experiences of students who participated as an interviewer versus students who participated as an observer. An online survey was administered to Bachelor and Master of Social Work students (N = 66) to collect quantitative and qualitative responses (N = 107) about their learning experience from the perspective of either an interviewer or an observer. Quantitative analyses revealed that interviewers perceived simulation with SCs to be more beneficial to their clinical learning compared to observers. No other differences were found between the two groups. A thematic analysis of qualitative data showed the following three unique learning processes among observers: (1) emotional distance from practice, (2) observation of the relationship between theory and practice, and (3) vicarious learning from peers. Results suggest that educators leverage student learning opportunities in observing roles and actively engage them during simulation debriefing sessions. Implications for simulation-based education and further research 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 | 0.076 | 0.192 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".