Linking Simulation-Based Educational Assessments and Patient-Related Outcomes
Bibliographic record
Abstract
PURPOSE: To examine the evidence supporting the use of simulation-based assessments as surrogates for patient-related outcomes assessed in the workplace. METHOD: The authors systematically searched MEDLINE, EMBASE, Scopus, and key journals through February 26, 2013. They included original studies that assessed health professionals and trainees using simulation and then linked those scores with patient-related outcomes assessed in the workplace. Two reviewers independently extracted information on participants, tasks, validity evidence, study quality, patient-related and simulation-based outcomes, and magnitude of correlation. All correlations were pooled using random-effects meta-analysis. RESULTS: Of 11,628 potentially relevant articles, the 33 included studies enrolled 1,203 participants, including postgraduate physicians (n = 24 studies), practicing physicians (n = 8), medical students (n = 6), dentists (n = 2), and nurses (n = 1). The pooled correlation for provider behaviors was 0.51 (95% confidence interval [CI], 0.38 to 0.62; n = 27 studies); for time behaviors, 0.44 (95% CI, 0.15 to 0.66; n = 7); and for patient outcomes, 0.24 (95% CI, -0.02 to 0.47; n = 5). Most reported validity evidence was favorable, though studies often included only correlational evidence. Validity evidence of internal structure (n = 13 studies), content (n = 12), response process (n = 2), and consequences (n = 1) were reported less often. Three tools showed large pooled correlations and favorable (albeit incomplete) validity evidence. CONCLUSIONS: Simulation-based assessments often correlate positively with patient-related outcomes. Although these surrogates are imperfect, tools with established validity evidence may replace workplace-based assessments for evaluating select procedural skills.
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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 | 0.129 | 0.558 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.010 |
| Bibliometrics | 0.012 | 0.012 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".