Does Making the Numerical Values of Verbal Anchors on a Rating Scale Available to Examiners Inflate Scores on a Long Case Examination?
Bibliographic record
Abstract
PURPOSE: Rating scales are frequently used for scoring assessments in medical education. The effect of changing the structural elements of a rating scale on students' examination scores has received little attention in the medical education literature. This study assessed the impact of making the numerical values of verbal anchors on a rating scale available to examiners in a long case examination (LCE). METHOD: During the 2011-2012 academic year, the numerical values of verbal anchors on a rating scale for an internal medicine clerkship LCE were made available to faculty examiners. Historically, and specifically in the control year of 2010-2011, examiners only saw the scale's verbal anchors and were blinded to the associated numerical values. To assess the impact of this change, the authors compared students' LCE scores between the two cohort years. To assess for differences between the two cohorts, they compared students' scores on other clerkship assessments, which remained the same between the two cohorts. RESULTS: From 2010-2011 (n = 226) to 2011-2012 (n = 218), the median LCE score increased significantly from 82.11% to 85.02% (P < .01). Students' performance on the other clerkship assessments was similar between cohorts. CONCLUSIONS: Providing examiners with the numerical values of verbal anchors on a rating scale, in addition to the verbal anchors themselves, led to a significant increase in students' scores on an internal medicine clerkship LCE. When constructing or changing rating scales, educators must consider the potential impact of the rating scale structure on students' scores.
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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 | 0.088 | 0.458 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".