Progress testing: is there a role for the OSCE?
Bibliographic record
Abstract
CONTEXT: The shift from a time-based to a competency-based framework in medical education has created a need for frequent formative assessments. Many educational programmes use some form of written progress test to identify areas of strength and weakness and to promote continuous improvement in their learners. However, the role of performance-based assessments, such as objective structured clinical examinations (OSCEs), in progress testing remains unclear. OBJECTIVE: The aims of this paper are to describe the use of an OSCE to assess learners at different stages of training, describe a structure for reporting scores, and provide evidence for the psychometric properties of different rating tools. METHODS: A 10-station OSCE was administered to internal medicine residents in postgraduate years (PGYs) 1-4. Candidates were assessed using a checklist (CL), a global rating scale (GRS) and a training level rating scale (TLRS). Reliability was calculated for each measure using Cronbach's alpha. Differences in performance by year of training were explored using analysis of variance (anova). Correlations between scores obtained using the different rating instruments were calculated. RESULTS: Sixty-nine residents participated in the OSCE. Inter-station reliability was greater (0.88) using the TLRS compared with the CL (0.84) and GRS (0.79). Using all three rating instruments, scores varied significantly by year of training (p < 0.001). Scores from the different rating instruments were highly correlated: CL and GRS, r = 0.93; CL and TLRS, r = 0.90, and GRS and TLRS, r = 0.94 (p < 0.001). Candidates received feedback on their performance relative to examiner expectations for their PGY level. CONCLUSIONS: Scores were found to have high reliability and demonstrated significant differences in performance by year of training. This provides evidence for the validity of using scores achieved on an OSCE as markers of progress in learners at different levels of training. Future studies will focus on assessing individual progress on the OSCE over time.
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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 | 0.104 | 0.207 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.005 | 0.009 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.006 | 0.004 |
| Insufficient payload (model declined to judge) | 0.010 | 0.004 |
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".