Inter-Rater Reliability of Grading Undergraduate Portfolios in Veterinary Medical Education
Bibliographic record
Abstract
The reliability of high-stakes assessment of portfolios containing an aggregation of quantitative and qualitative data based on programmatic assessment is under debate, especially when multiple assessors are involved. In this study carried out at the Faculty of Veterinary Medicine, Utrecht University, the Netherlands, two independent assessors graded the portfolios of students in their second year of the 3-year clinical phase. The similarity of grades (i.e., equal grades) and the level of the grades were studied to estimate inter-rater reliability, taking into account the potential effects of the assessor's background (i.e., originating from a clinical or non-clinical department) and student's cohort group, gender, and chosen master track (Companion Animal Health, Equine Health, or Farm Animal/Public Health). Whereas the similarity between the two grades increased from 58% in the first year the grading system was introduced to around 80% afterwards, the grade level was lower over the next 3 years. The assessor's background had a minor effect on the proportion of similar grades, as well as on grading level. The assessor intraclass correlation was low (i.e., all assessors scored with a similar grading pattern [same range of grades]). The grades awarded to female students were higher but more often dissimilar. We conclude that the grading system was well implemented and has a high inter-rater reliability.
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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 | 0.111 | 0.186 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".