The Construct and Criterion Validity of the Mini-CEX
Bibliographic record
Abstract
PURPOSE: To conduct a meta-analysis of published studies to determine the construct and criterion validity of the mini-clinical evaluation exercise (mini-CEX) to measure clinical performance. METHOD: The authors included all peer-reviewed studies published from 1995 to 2012 that reported the relationship between participants' performance on the mini-CEX and on other standardized academic and clinical performance measures. Moderator variables and performance and standardized exam measures were extracted and reviewed independently using a standardized coding protocol. RESULTS: Performance measures from 11 studies were identified. A random-effects model of weighted mean effect size differences (d) resulted in: (1) construct validity coefficients for the mini-CEX on the trainees' performance across different residency year levels ranging from d=0.25 (95% confidence intervals [CI]: 0.04-0.46) to d=0.50 (95% CI: 0.31-0.70), and (2) concurrent validity coefficients for the mini-CEX based on personnel ratings ranging from d=0.23 (95% CI: 0.04-0.50) to d=0.50 (95% CI: 0.34-0.65). Also, a random-effects model of weighted correlation effect size differences (r) resulted in predictive validity coefficients for the mini-CEX on trainees' performance across different standardized measures ranging from r=0.26 (95% CI: 0.16-0.35) to r=0.85 (95% CI: 0.47-0.96). CONCLUSIONS: The construct and criterion validity of the mini-CEX was supported by small to large effect size differences based on measures between trainees' achievement and clinical skills performance, indicating that it is an important instrument for the direct observation of trainees' clinical performance.
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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 | 0.153 | 0.238 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.012 | 0.036 |
| Bibliometrics | 0.012 | 0.008 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| 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".