The Assessment of Pathologists/Laboratory Medicine Physicians Through a Multisource Feedback Tool
Bibliographic record
Abstract
Abstract Context. —There is increasing interest in ensuring that physicians demonstrate the full range of Accreditation Council for Graduate Medical Education competencies. Objective. —To determine whether it is possible to develop a feasible and reliable multisource feedback instrument for pathologists and laboratory medicine physicians. Design. —Surveys with 39, 30, and 22 items were developed to assess individual physicians by 8 peers, 8 referring physicians, and 8 coworkers (eg, technologists, secretaries), respectively, using 5-point scales and an unable-to-assess category. Physicians completed a self-assessment survey. Items addressed key competencies related to clinical competence, collaboration, professionalism, and communication. Results. —Data from 101 pathologists and laboratory medicine physicians were analyzed. The mean number of respondents per physician was 7.6, 7.4, and 7.6 for peers, referring physicians, and coworkers, respectively. The reliability of the internal consistency, measured by Cronbach α, was ≥.95 for the full scale of all instruments. Analysis indicated that the medical peer, referring physician, and coworker instruments achieved a generalizability coefficient of .78, .81, and .81, respectively. Factor analysis showed 4 factors on the peer questionnaire accounted for 68.8% of the total variance: reports and clinical competency, collaboration, educational leadership, and professional behavior. For the referring physician survey, 3 factors accounted for 66.9% of the variance: professionalism, reports, and clinical competency. Two factors on the coworker questionnaire accounted for 59.9% of the total variance: communication and professionalism. Conclusions. —It is feasible to assess this group of physicians using multisource feedback with instruments that are reliable.
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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 | 0.017 | 0.071 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".