Impact of rating demands on rater-based assessments of clinical competence
Bibliographic record
Abstract
PURPOSE: Many assessment practices used in primary care rely upon judgements provided by individuals observing trainees or colleagues. Despite there being many reasons to view these observations as cognitively complex, the extent to which fallibility in judgement reflects mental workload has not been examined experimentally. The objective of this study was to evaluate the impact of increasing rating demands on rater-based assessments of clinical competence. METHODS: Participants were randomly assigned to one of four conditions (in a 2×2 factorial design) and asked to rate three pre-recorded unscripted clinical encounters illustrating three levels of performance (high, medium, low). We looked at the effect on participants of having a larger (seven) or smaller (two) number of dimensions to rate, and/or distracting them with extraneous tasks (attending to patient status and the activity of additional individuals observable on video). Outcome measures included number of dimension-relevant behaviours identified, ability to differentiate between levels of performance, and inter-rater reliability. RESULTS: Using the two dimensions common to both groups, ANOVA revealed a significant effect of the number of dimensions included in the scale on the number of relevant behaviours identified: participants in the 2D group identified more features than those in the 7D group. Both groups were able to differentiate between levels of performance, but post hoc analyses revealed significance on all pairwise comparisons in the 2D group and not in the 7D group. Inter-rater reliability increased from 0.45 in the 7D group to 0.70 when participants were required to consider only two dimensions. By contrast, the distractions had little effect. CONCLUSIONS: The results of this study provide preliminary evidence that requiring raters to consider a greater number of dimensions can decrease (a) the number of dimension-relevant behaviours identified, (b) the capacity to differentiate between levels of performance, and (c) 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 | 0.047 | 0.290 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".