Characteristics of Physicians Referred for a Competence Assessment: A Comparison of State Medical Board and Hospital Referred Physicians
Bibliographic record
Abstract
ABSTRACT This study compares key characteristics and performance of physicians referred to a clinical competence assessment and education program by state medical boards (boards) and hospitals. Physicians referred by boards (400) and by hospitals (102) completed a CPEP clinical competence assessment between July 2002 and June 2010. Key characteristics, self-reported specialty, and average performance rating for each group are reported and compared. Results show that, compared with hospital-referred physicians, board-referred physicians were more likely to be male (75.5% versus 88.3%), older (average age 54.1 versus 50.3 years), and less likely to be currently specialty board certified (80.4% versus 61.8%). On a scale of 1 (best) to 4 (worst), average performance was 2.62 for board referrals and 2.36 for hospital referrals. There were no significant differences between board and hospital referrals in the percentage of physicians who graduated from U.S. and Canadian medical schools. The most common specialties referred differed for boards and hospitals. Conclusion: Characteristics of physicians referred to a clinical competence program by boards and hospitals differ in important respects. The authors consider the potential reasons for these differences and whether boards and hospitals are dealing with different subsets of physicians with different types of performance problems. Further study is warranted.
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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 | 0.001 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".