Maintenance of certification: how do we teach practicing physicians?
Bibliographic record
Abstract
Maintenance of certification (MOC) has become increasingly important in medicine to ensure maintenance of competence throughout a physician's career. This paper reviews current issues and challenges associated with MOC in medicine, including how to define medical competencies for practicing physicians, assessment, and how best to support physicians' lifelong learning in a continuous and self-motivated way. We explore how the combination of self-monitoring, regular feedback, and peer support could improve self-assessment. Effective MOC programs are learner-driven, focused on every day practice, and incorporate educational principles. We discuss the importance of MOC to the physicians' actual practice to improve acceptability. We review the benefits of tailored programs as well as decentralization of MOC programs to better characterize the physician's practice. Lastly, we discuss the value of simulation-based medical education in MOC programs. Simulation-based education could be used to practice uncommon complications, life-threatening scenarios, non-technical skills improvement, and become proficient with new technology. As learners find simulation experiences educationally valuable, clinically relevant, and positive, simulation could be a way of increasing physicians' participation in MOC programs.
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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 | 0.006 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".