Linking Simulation-Based Educational Assessments and Patient-Related Outcomes
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it