Hawks, Doves and Rasch decisions: Understanding the influence of different cycles of an OSCE on students’ scores using Many Facet Rasch Modeling
Bibliographic record
Abstract
INTRODUCTION: OSCEs are commonly conducted in multiple cycles (different circuits, times, and locations), yet the potential for students' allocation to different OSCE cycles is rarely considered as a source of variance-perhaps in part because conventional psychometrics provide limited insight. METHODS: We used Many Facet Rasch Modeling (MFRM) to estimate the influence of "examiner cohorts" (the combined influence of the examiners in the cycle to which each student was allocated) on students' scores within a fully nested multi-cycle OSCE. RESULTS: Observed average scores for examiners cycles varied by 8.6%, but model-adjusted estimates showed a smaller range of 4.4%. Most students' scores were only slightly altered by the model; the greatest score increase was 5.3%, and greatest score decrease was -3.6%, with 2 students passing who would have failed. DISCUSSION: Despite using 16 examiners per cycle, examiner variability did not completely counter-balance, resulting in an influence of OSCE cycles on students' scores. Assumptions were required for the MFRM analysis; innovative procedures to overcome these limitations and strengthen OSCEs are discussed. CONCLUSIONS: OSCE cycle allocation has the potential to exert a small but unfair influence on students' OSCE scores; these little-considered influences should challenge our assumptions and design of OSCEs.
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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 | 0.132 | 0.359 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| 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".