How Do Physicians Assess Their Family Physician Colleagues' Performance? Creating a Rubric to Inform Assessment and Feedback
Bibliographic record
Abstract
INTRODUCTION: The Colleges of Physicians and Surgeons of Alberta and Nova Scotia (CPSNS) use a standardized multisource feedback program, the Physician Achievement Review (PAR/NSPAR), to provide physicians with performance assessment data via questionnaires from medical colleagues, coworkers, and patients on 5 practice domains: consultation communication, patient interaction, professional self-management, clinical competence, and psychosocial management of patients. Physicians receive a confidential report; the intent is practice improvement. However, research indicates that feedback from medical colleagues appears to be less understood than that from coworkers or patients, due to a lack of specificity and concerns regarding feedback credibility. The purpose of this study was to determine how physicians make decisions about performance ratings for family physician (FP) colleagues in the 5 practice domains. METHODS: This was an exploratory qualitative study using focus groups-one with 11 family physicians and one with 12 specialists-who had served as NSPAR "medical colleague'' reviewers. We analyzed focus group transcripts using content analysis. RESULTS: Family and specialist physicians provided examples of behaviors indicative of both high- and low-scoring performance for items within the 5 practice domains. From these, an assessment rubric was created to inform both external reviewers and the physicians being reviewed of performance expectations. Reviewers reported using varied sources of information to make assessments, including shared patients, medical records, referral letters, feedback from others, and self-reference. DISCUSSION: The CPSNS has used the assessment rubric to create an online resource to inform medical colleague assessment and enhance the usefulness of their NSPAR scores. Further research will be required to determine its impact.
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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 | 0.051 | 0.114 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".