Are Examiners’ Judgments in OSCE-Style Assessments Influenced by Contrast Effects?
Bibliographic record
Abstract
PURPOSE: Laboratory studies have shown that performance assessment judgments can be biased by "contrast effects." Assessors' scores become more positive, for example, when the assessed performance is preceded by relatively weak candidates. The authors queried whether this effect occurs in real, high-stakes performance assessments despite increased formality and behavioral descriptors. METHOD: Data were obtained for the 2011 United Kingdom Foundational Programme clinical assessment and the 2008 University of Alberta Multiple Mini Interview. Candidate scores were compared with scores for immediately preceding candidates and progressively distant candidates. In addition, average scores for the preceding three candidates were calculated. Relationships between these variables were examined using linear regression. RESULTS: Negative relationships were observed between index scores and both immediately preceding and recent scores for all exam formats. Relationships were greater between index scores and the average of the three preceding scores. These effects persisted even when examiners had judged several performances, explaining up to 11% of observed variance on some occasions. CONCLUSIONS: These findings suggest that contrast effects do influence examiner judgments in high-stakes performance-based assessments. Although the observed effect was smaller than observed in experimentally controlled laboratory studies, this is to be expected given that real-world data lessen the strength of the intervention by virtue of less distinct differences between candidates. Although it is possible that the format of circuital exams reduces examiners' susceptibility to these influences, the finding of a persistent effect after examiners had judged several candidates suggests that the potential influence on candidate scores should not be ignored.
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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 | 0.031 | 0.255 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".