Rater Training in Medical Education: A Scoping Review
Bibliographic record
Abstract
There is an increasing focus in medical education on trainee evaluation. Often, reliability and other psychometric properties of evaluations fall below expected standards. Rater training, a process whereby raters undergo instruction on how to consistently evaluate trainees and produce reliable and accurate scores, has been suggested to improve rater performance within behavioral sciences. A scoping literature review was undertaken to examine the effect of rater training in medical education and address the question: "Does rater training improve performance attending physician evaluations of medical trainees?" Two independent reviewers searched PubMed®, MEDLINE®, EMBASE™, the Cochrane Library, CINAHL®, ERIC™, and PsycInfo® databases and identified all prospective studies examining the effect of rater training on physician evaluations of medical trainees. Consolidated Standards of Reporting Trials (CONSORT) and Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) checklists were used to assess quality. Fourteen prospective studies met the inclusion criteria. All had heterogeneity in design, type of rater training, and measured outcomes. Pooled analysis was not performed. Four studies examined rater training used to assess technical skills; none identified a positive effect. Ten studies assessed its use to evaluate non-technical skills: six demonstrated no effect, while four showed a positive effect. The overall quality of studies was poor to moderate. Rater training in medical education literature is heterogeneous, limited, and describes minimal improvement on the psychometric properties of trainee evaluations when implemented. Further research is required to assess rater training's efficacy in medical education.
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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 | 0.248 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.008 | 0.007 |
| Bibliometrics | 0.024 | 0.026 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".