The objective structured clinical examination: can physician‐examiners participate from a distance?
Bibliographic record
Abstract
OBJECTIVES: Currently, a 'pedagogical gap' exists in distributed medical education in that distance educators teach medical students but typically do not have the opportunity to assess them in large-scale examinations such as the objective structured clinical examination (OSCE). We developed a remote examiner OSCE (reOSCE) that was integrated into a traditional OSCE to establish whether remote examination technology may be used to bridge this gap. The purpose of this study was to explore whether remote physician-examiners can replace on-site physician-examiners in an OSCE, and to determine the feasibility of this new examination method. METHODS: Forty Year 3 medical students were randomised into six reOSCE stations that were incorporated into two tracks of a 10-station traditional OSCE. For the reOSCE stations, student performance was assessed by both a local examiner (LE) in the room and a remote examiner (RE) who viewed the OSCE encounters from a distance. The primary endpoint was the correlation of scores between LEs and REs across all reOSCE stations. The secondary endpoint was a post-OSCE survey of both REs and students. RESULTS: Statistically significant correlations were found between LE and RE checklist scores for history taking (r = 0.64-r = 0.80), physical examination (r = 0.41-r = 0.54), and management stations (r = 0.78). Correlations between LE and RE global ratings were more varied (r = 0.21-r = 0.77). Correlations on three of the six stations reached significance. Qualitative analysis of feedback from REs and students showed high acceptance of the reOSCE despite technological issues. CONCLUSIONS: This preliminary study demonstrated that OSCE ratings by LEs and REs were reasonably comparable when using checklists. Remote examination may be a feasible and acceptable way of assessing students' clinical skills, but further validity evidence will be required before it can be recommended for use in high-stakes examinations.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".