Bibliographic record
Abstract
INTRODUCTION: High-quality instruments are required to assess and provide feedback to practicing physicians. Multisource feedback (MSF) uses questionnaires from colleagues, coworkers, and patients to provide data. It enables feedback in areas of increasing interest to the medical profession: communication, collaboration, professionalism, and interpersonal skills. The purpose of the study was to apply the 7 assessment criteria as a framework to examine the quality of MSF instruments used to assess practicing physicians. METHODS: The criteria for assessment (validity, reproducibility, equivalence, feasibility, educational effect, catalytic effect, and acceptability) were examined for 3 sets of instruments, drawing on published data. RESULTS: Three MSF instruments with a sufficient body of research for inclusion-the Canadian Physician Achievement Review instruments and the United Kingdom's GMC and CFEP360 instruments-were examined. There was evidence that MSF has been assessed against all criteria except educational effects, although variably for some of the instruments. The greatest emphasis was on validity, reproducibility, and feasibility for all of the instruments. Assessments of the catalytic effect were not available for 1 of the 2 UK instruments and minimally examined for the other. Data about acceptability are implicit in the UK instruments from their endorsement by the Royal College of General Practice and explicitly examined in the Canadian instruments. DISCUSSION: The 7 criteria provided a useful framework to assess the quality of MSF instruments and enable an approach to analyzing gaps in instrument assessment. These criteria are likely to be helpful in assessing other instruments used 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 | 0.505 | 0.799 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.005 | 0.004 |
| 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".