The Effect of Candidatesʼ Perceptions of the Evaluation Method on Reliability of Checklist and Global Rating Scores in an Objective Structured Clinical Examination
Bibliographic record
Abstract
PURPOSE: Process-oriented global ratings, which assess "overall performance" on one or a number of domains, have been purported to capture nuances of expert performance better than checklists. Pilot data indicate that students change behaviors depending on their perceptions of how they are being scored, while experts do not. This study examines the impact of the students' orientation to the rating system on OSCE scores and the interstation reliability of the checklist and global scores. METHOD: A total of 57 third- and fourth-year medical students at one school were randomly assigned to two groups and performed a ten-station OSCE. Group 1 was told that scores were based on checklists. Group 2 was informed that performance would be rated using global ratings geared toward assessing overall competence. All candidates were scored by physician-examiners who were unaware of the students' orientations to the rating system and who used both checklists and global rating forms. RESULTS: A mixed two-factor ANOVA identified a significant interaction of rating form by group (F(1,55) = 5.5, p <.05), with Group 1 (checklist-oriented) having higher checklist scores but lower global scores than did Group 2 (oriented to global ratings). In addition, Group 1 had higher interstation alpha coefficients than did Group 2 for both global scores (0.74 versus 0.63) and checklist scores (0.63 versus 0.40). CONCLUSIONS: The interaction effect on total exam scores suggests that students adapt their behaviors to the system of evaluation. However, the lower reliability coefficients for both forms found in the process-oriented global-rating group suggest that an individual's capacity to adapt to the system of global rating forms is relatively station-specific, possibly depending on his or her expertise in the domain represented in each station.
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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 | 0.023 | 0.114 |
| 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 | 0.001 |
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".