Who You Know or What You Know? Effect of Examiner Familiarity With Residents on OSCE Scores
Bibliographic record
Abstract
BACKGROUND: Despite the goal of objective structured clinical examinations (OSCEs) to be objective, examiner biases may influence scores. Examiner familiarity with candidates is a potential bias that has not been well studied. METHOD: To determine the effect of familiarity, OSCE scores for 158 internal medicine residents were analyzed by whether examiners were familiar with them, based on previous clinical encounters, and if previous impressions were positive or negative. A hierarchical multivariable analysis of variance was performed to control for resident, examiner, and level of training. RESULTS: Across 480 interactions (50 examiners, 158 residents), multivariable analysis showed that positive familiarity was associated with a significant increase in ratings (+0.37 on a 5-point scale), comparable to the difference between first- and third/fourth-year residents. CONCLUSIONS: Familiarity with candidates is a significant source of examiner bias in OSCE scores. Consideration should be paid to the influence of examiners' previous knowledge of examinees and attempts made to mitigate this bias.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.013 | 0.093 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".