The effect of differential rater function over time (DRIFT) on objective structured clinical examination ratings
Bibliographic record
Abstract
CONTEXT: Despite the impartiality implied in its title, the objective structured clinical examination (OSCE) is vulnerable to systematic biases, particularly those affecting raters' performance. In this study our aim was to examine OSCE ratings for evidence of differential rater function over time (DRIFT), and to explore potential causes of DRIFT. METHODS: We studied ratings for 14 internal medicine resident doctors over the course of a single formative OSCE, comprising 10 12-minute stations, each with a single rater. We evaluated the association between time-slot and rating for a station. We also explored a possible interaction between time-slot and station difficulty, which would support the hypothesis that rater fatigue causes DRIFT, and considered 'warm-up' as an alternative explanation for DRIFT by repeating our analysis after excluding the first two OSCE stations. RESULTS: Time-slot was positively associated with rating on a station (regression coefficient 0.88, 95% confidence interval [CI] 0.38-1.38; P = 0.001). There was an interaction between time-slot and station difficulty: for the more difficult stations the regression coefficient for time-slot was 1.24 (95% CI 0.55-1.93; P = 0.001) compared with 0.52 (95% CI - 0.08 to 1.13; P = 0.09) for the less difficult stations. Removing the first two stations from our analyses did not correct DRIFT. CONCLUSIONS: Systematic biases, such as DRIFT, may compromise internal validity in an OSCE. Further work is needed to confirm this finding and to explore whether DRIFT also affects ratings on summative OSCEs. If confirmed, the factors contributing to DRIFT, and ways to reduce these, should then be explored.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".