Analytic global OSCE ratings are sensitive to level of training
Bibliographic record
Abstract
PURPOSE: There are several reasons for using global ratings in addition to checklists for scoring objective structured clinical examination (OSCE) stations. However, there has been little evidence collected regarding the validity of these scales. This study assessed the construct validity of an analytic global rating with 4 component subscales: empathy, coherence, verbal and non-verbal expression. METHODS: A total of 19 Year 3 and 38 Year 4 clinical clerks were scored on content checklists and these global ratings during a 10-station OSCE. T-tests were used to assess differences between groups for overall checklist and global scores, and for each of the 4 subscales. RESULTS: The mean global rating was significantly higher for senior clerks (75.5% versus 71.3%, t55 = 2.12, P < 0.05) and there were significant differences by level of training for the coherence (t55 = 3.33, P < 0.01) and verbal communication (t55 = 2.33, P < 0.05) subscales. Interstation reliability was 0.70 for the global rating and ranged from 0.58 to 0.65 for the subscales. Checklist reliability was 0.54. CONCLUSION: In this study, a summated analytic global rating demonstrated construct validity, as did 2 of the 4 scales measuring specific traits. In addition, the analytic global rating showed substantially higher internal consistency than did the checklists, a finding consistent with that seen in previous studies cited in the literature. Global ratings are an important element of OSCE measurement and can have good psychometric properties. However, OSCE researchers should clearly describe the type of global ratings they use. Further research is needed to define the most effective global rating scales.
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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 | 0.020 | 0.098 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".