The Likelihood That Remedial Continuing Medical Education (CME) Reduces Disciplinary Recidivism Among Physicians
Bibliographic record
Abstract
PURPOSE: State medical boards are charged through their medical practice acts to regulate physician practice and, when necessary, discipline physicians for incompetent or inappropriate behavior. Boards often authorize remedial continuing medical education (CME) as part of a disciplinary action; however, it is unclear how effective remedial CME is in reducing the likelihood of physicians receiving additional discipline. This study examined the relationship between physicians who were required to complete remedial CME as part of their first discipline by state medical boards and the likelihood of additional discipline. METHOD: The national-level sample included 4,061 MD-physicians whose first discipline included license restrictions, probation, or other conditions imposed by state medical boards between 2011 and 2015. A multivariate logistic regression model examined whether physicians required to complete remedial CME as part of their first discipline were less likely to receive additional discipline by boards within 5 years. RESULTS: Of the 4,061 physicians, 36% (n = 1,449) were required to complete remedial CME as part of their first discipline, and 35% (n = 1,426) received additional discipline within 5 years. After accounting for other factors, physicians who were required to complete remedial CME as part of their first discipline by boards were less likely to receive additional discipline (odds ratio, 0.597; 95% confidence interval, 0.513-0.696; P < .001) within 5 years compared to physicians who were not required to complete remedial CME. CONCLUSIONS: Findings support remedial CME as a means to help reduce physician disciplinary recidivism in certain circumstances. Physicians required to complete remedial CME as part of their first discipline were less likely to receive additional discipline by state medical boards within 5 years.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.005 |
| 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 teacher head, 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".