Effect of rater training on the reliability of technical skill assessments: a randomized controlled trial
Bibliographic record
Abstract
BACKGROUND: Rater training improves the reliability of observational assessment tools but has not been well studied for technical skills. This study assessed whether rater training could improve the reliability of technical skill assessment. METHODS: Academic and community surgeons in Royal College of Physicians and Surgeons of Canada surgical subspecialties were randomly allocated to either rater training (7-minute video incorporating frame-of-reference training elements) or no training. Participants then assessed trainees performing a suturing and knot-tying task using 3 assessment tools: a visual analogue scale, a task-specific checklist and a modified version of the Objective Structured Assessment of Technical Skill global rating scale (GRS). We measured interrater reliability (IRR) using intraclass correlation type 2. RESULTS: There were 24 surgeons in the training group and 23 in the no-training group. Mean assessment tool scores were not significantly different between the 2 groups. The training group had higher IRR than the no-training group on the visual analogue scale (0.71 v. 0.46), task-specific checklist (0.46 v. 0.33) and GRS (0.71 v. 0.61). However, confidence intervals were wide and overlapping for all 3 tools. CONCLUSION: For education purposes, the reliability of the visual analogue scale and GRS would be considered "good" for the training group but "moderate" for the no-training group. However, a significant difference in IRR was not shown, and reliability remained below the desired level of 0.8 for high-stakes testing. Training did not significantly improve assessment tool reliability. Although rater training may represent a way to improve reliability, further study is needed to determine effective training methods.
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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 | 0.016 | 0.033 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.006 | 0.004 |
| Insufficient payload (model declined to judge) | 0.011 | 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".