Using Kane’s Validity Framework to Compare an Integrated and Single-Skill Objective Structured Clinical Examination
Bibliographic record
Abstract
OBJECTIVE: The aim of this study was to compare the validity of an integrated objective structured clinical examination (OSCE) station assessing both oral and written components with that of an OSCE station assessing 1 single skill (oral only), both targeted at assessing taking a best possible medication history. METHODS: A convergent mixed-methods design that used the 4 inferences of Kane's validity framework (scoring, generalization, extrapolation, and implications) as a scaffold to integrate qualitative data (post-OSCE reflections) and quantitative data (assessment grades and categories of medication errors) was applied. RESULTS: In 2022, 216 students completed the OSCE station with the oral component alone, while in 2023, 254 students completed the integrated (oral and written) OSCE station. Students in 2023 performed significantly better, with a median score of 88% vs 80% in 2022. There was a greater proportion of commission errors in the integrated assessment (20.4% vs 15.3%), but fewer omission errors (29.9% vs 31.8%) and patient profile errors (5.1% vs 69.4%). Student reflections revealed that conversations were rushed in the integrated assessment, with a greater focus on written formatting, but an appreciation for the authenticity and structured format of the integrated OSCE compared with the single-skill OSCE alone. CONCLUSION: Students completing the integrated OSCE (with oral and written components) had fewer patient profile and medication omission errors than students who completed the oral-only OSCE. Considering Kane's validity framework, there was a stronger argument for the more authentic integrated OSCE in terms of the inferences of extrapolation and implications.
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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 | 0.126 | 0.352 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.004 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.007 |
| 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 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".