A Critical Appraisal of the Use of Standardized Client Simulations in Social Work Education
Bibliographic record
Abstract
Reliable and valid methods to evaluate student competence are needed in social work education, and practice examinations with standardized clients may hold promise for social work. The authors conducted a critical appraisal of standardized client simulations used in social work education to assess their effectiveness for teaching and for evaluating social work students' competence. Following a comprehensive search, 18 studies, including 515 social work students, were examined. The authors extracted data from these studies and study methods and assessed the results. This review found that studies vary in methodological quality; however, using standardized client simulations is well-received by students. Consistent implementation methods and reliable, valid assessment measures are needed to advance this evaluation method for social work.
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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 | 0.376 | 0.718 |
| Meta-epidemiology (narrow) | 0.003 | 0.004 |
| Meta-epidemiology (broad) | 0.009 | 0.009 |
| Bibliometrics | 0.029 | 0.016 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.007 | 0.005 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".