Assessment of Radiology Physicians by a Regulatory Authority
Bibliographic record
Abstract
PURPOSE: To determine whether it is possible to develop a feasible, valid, and reliable multisource feedback program for radiologists. MATERIALS AND METHODS: Surveys with 38, 29, and 20 items were developed to assess individual radiologists by eight radiologic colleagues (peers), eight referring physicians, and eight co-workers (eg, technicians), respectively, by using five-point scales along with an "unable to assess" category. Radiologists completed a self-assessment on the basis of the peer questionnaire. Items addressed key competencies related to clinical competence, collegiality, professionalism, workplace behavior, and self-management. The study was approved by the University of Calgary Conjoint Health Ethics Research Board. RESULTS: Data from 190 radiologists were available. The mean numbers of respondents per physician were 7.5 of eight (1259 of 1520, 83%), 7.15 of eight (1337 of 1520, 88%), and 7.5 of eight (1420 of 1520, 93%) for peers, referring physicians, and co-workers, respectively. The internal consistency reliability indicated all instruments had a Cronbach alpha of more than 0.95. The generalizability coefficient analysis indicated that the peer, referring physicians, and co-worker instruments achieved a generalizability coefficient of 0.88, 0.79, and 0.87, respectively. The factor analysis indicated that four factors on the colleague questionnaire accounted for 70% of the total variance: clinical competence, collegiality, professional development, and workplace behavior. For the referring physician survey, three factors accounted for 64.1% of the variance: professional development, professional consultation, and professional responsibility. Two factors on the co-worker questionnaire accounted for 63.2% of the total variance: professional responsibility and patient interaction. CONCLUSION: The psychometric examination of the data suggests that the instruments developed to assess radiologists are a feasible way to assess radiology practice and provide evidence for validity and 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 | 0.042 | 0.165 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".