Assessing the quality of supervisors’ completed clinical evaluation reports
Bibliographic record
Abstract
CONTEXT: Although concern has been raised about the value of clinical evaluation reports for discriminating among trainees, there have been few efforts to formalise the dimensions and qualities that distinguish effective versus less useful styles of form completion. METHODS: Using brainstorming and a modified Delphi technique, a focus group determined the key features of high-quality completed evaluation reports. These features were used to create a rating scale to evaluate the quality of completed reports. The scale was pilot-tested locally; the results were psychometrically analysed and used to modify the scale. The scale was then tested on a national level. Psychometric analysis and final modification of the scale were completed. RESULTS: Sixteen features of high-quality reports were identified and used to develop a rating scale: the Completed Clinical Evaluation Report Rating (CCERR). The reliability of the scale after a national field test with 55 raters assessing 18 in-training evaluation reports (ITERs) was 0.82. Further revisions were made; the final version of the CCERR contains nine items rated on a 5-point scale. With this version, the mean ratings of three groups of 'gold-standard' ITERs (previously judged to be of high, average and poor quality) differed significantly (P < 0.05). DISCUSSION: The CCERR is a validated scale that can be used to help train supervisors to complete and assess the quality of evaluation reports.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.011 | 0.054 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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 teacher head, 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".