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 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 | 0.025 | 0.118 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".