The prevalence and special educational requirements of dyscompetent physicians
Bibliographic record
Abstract
Underperformance among physicians is not well studied or defined; yet, the identification and remediation of physicians who are not performing up to acceptable standards is central to quality care and patient safety. Methods for estimating the prevalence of dyscompetence include evaluating available data on medical errors, malpractice claims, disciplinary actions, quality control studies, medical record review studies, and in-stream assessments of physician performance. These data provide a range of estimates from 0.6% to 50%, depending on the method. A reasonable estimate of dyscompetence appears to be 6% to 12%. Age-related cognitive decline, impairment due to substance use disorders, and other psychiatric illness can contribute to underperformance, diminishing physicians' insight into their level of performance as well as their ability to benefit from an educational experience.Currently, dyscompetent physicians in the United States are identified through either the legal system or peer review. The primary method of resolving issues of underperformance in physicians is through continuing medical education (CME). Although a number of specialized assessment and education programs exist in the United States, these programs are largely underutilized. Similar programs exist in Canada and have provided evidence of the efficacy of a more specialized and individualized educational approach for underperforming physicians. Current specialty programs focused on this population employ individual assessments of knowledge and performance, individually designed educational programs, long-term plans for maintenance of educational activity, and repeated assessment of performance level. Noting that few CME programs offer these requirements, a number of changes to current medical quality assurance programs that might foster such educational requirements for underperforming physicians are provided.
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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 | 0.002 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".