Differential rater function over time (DRIFT) during student simulations
Bibliographic record
Abstract
Background The field of paramedicine continues to advance in scope. Simulation training is frequently used to teach and evaluate students. Simulation examinations are often evaluated using a standardised global rating scale (GRS) that is reliable and valid. However, differential rater function over time (DRIFT) has not been evaluated when using the GRS during simulations. Aims This study aimed to assess if DRIFT arises when applying the GRS. Methods Data were collected at six simulation evaluations. Raters were randomly assigned to evaluate several students at the same station. Each station lasted 12 minutes and there was a total of 11 stations. A model to test DRIFT scores was created and was tested against both a leniency and perceptual model. Findings Of the models explored, one that included students, the rater, and the dimensions had the greatest evidence (–3151 Bayes factors). This model was then tested against leniency (K=–9.1 dHart) and perceptual models (K=–7.1 dHart). This suggests a substantial finding against DRIFT; however, the tested models used a wide parameter so the possibility of a minor effect is not fully excluded. Conclusion DRIFT was not found; however, further studies with multiple centres and longer evaluations should be conducted.
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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 | 0.075 | 0.343 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| 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".