Validity Evidence for a Brief Online Key Features Examination in the Internal Medicine Clerkship
Bibliographic record
Abstract
PURPOSE: Medical educators use key features examinations (KFEs) to assess clinical decision making in many countries, but not in U.S. medical schools. The authors developed an online KFE to assess third-year medical students' decision-making abilities during internal medicine (IM) clerkships in the United States. They used Messick's unified validity framework to gather validity evidence regarding response process, internal structure, and relationship to other variables. METHOD: From February 2012 through January 2013, 759 students (at eight U.S. medical schools) had 75 minutes to complete one of four KFE forms during their IM clerkship. They also completed a survey regarding their experiences. The authors performed item analyses and generalizability studies, comparing KFE scores with prior clinical experience and National Board of Medical Examiners Subject Examination (NBME-SE) scores. RESULTS: Five hundred fifteen (67.9%) students consented to participate. Across KFE forms, mean scores ranged from 54.6% to 60.3% (standard deviation 8.4-9.6%), and Phi-coefficients ranged from 0.36 to 0.52. Adding five cases to the most reliable form would increase the Phi-coefficient to 0.59. Removing the least discriminating case from the two most reliable forms would increase the alpha coefficient to, respectively, 0.58 and 0.57. The main source of variance came from the interaction of students (nested in schools) and cases. Correlation between KFE and NBME-SE scores ranged from 0.24 to 0.47 (P < .01). CONCLUSIONS: These results provide strong evidence for response-process and relationship-to-other-variables validity and moderate internal structure validity for using a KFE to complement other assessments in U.S. IM clerkships.
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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 | 0.096 | 0.327 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| 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".