Training actors: a primer for social work educators working with standardized clients (SCs) in classroom simulations
Bibliographic record
Abstract
Classroom simulations using human actors as simulated clients (SC) have been proven effective in enhancing social work education and pedagogy. Beyond acting, carefully trained actors are critical members of the teaching team who contribute directly to the overall effectiveness of simulation-based learning through feedback and interactions with students. Despite their significant influence on student experiences of simulation, however, the processes of recruiting, hiring, training and supporting actors for effective involvement are not clearly captured in the social work literature. As a group of social work educators committed to simulation-based learning in clinical social work classrooms, we understand the value of trained actors and rely on their expertise and skill for the success of simulation activities. This article draws on foundational social work simulation literature, describes our teaching team’s experience working with actors, and shares practical tools for training SCs in a social work simulation program. We aim to map this process to elicit further discussions about actor training and support and detail our process of recruiting and hiring, training, and maintaining relationships with actors.
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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 | 0.015 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.014 |
| Scholarly communication | 0.008 | 0.011 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.009 | 0.013 |
| Insufficient payload (model declined to judge) | 0.006 | 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".