Use of the Consultation Letter Rating Scale among Geriatric Medicine Postgraduate Trainees
Bibliographic record
Abstract
OBJECTIVES: The implementation of competency-based evaluations increases the emphasis on in-training assessment. The Consultation Letter Rating Scale (CLRS), published by the Royal College of Physicians and Surgeons of Canada, is a tool that assesses written-communication competencies. This multisite project evaluated the tool's validity, reliability, feasibility, and acceptability for use in postgraduate geriatric medicine training. METHODS: Geriatric medicine trainees provided consultation letters from the 2017-2018 academic year. Geriatricians reviewed a standardized module and completed the tool for all the deidentified letters. The reviewers recorded the time used to complete the tool for each letter and completed a survey on content validity. Trainees completed a survey on the tool's usefulness. Responses were reviewed independently by two authors for thematic content. The unweighted and the weighted κ were used to measure interrater reliability. RESULTS: A total of 10 of 11 (91%) eligible trainees each provided five letters that were reviewed independently by six geriatricians, leading to a total of 300 assessments. A very small portion (4% [N = 12]) of assessments was incomplete. An average of 4.82 minutes (standard deviation = 3.17) was used to complete the tool. There was high interrater agreement for overall scores, with a multiple-rater weighted κ of 83% (95% confidence interval = 76%-89%). The interrater agreement was lower for the individual components. Both raters and trainees found the comments more useful than the numerical ratings. CONCLUSIONS: Our results support the use of the CLRS for facilitating feedback on the quality of consult letters to improve written-communication competencies among geriatric medicine trainees. J Am Geriatr Soc 67:2157-2160, 2019.
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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 | 0.022 | 0.089 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".