An Examination of Self-Reported Assessment Activities Documented by Specialist Physicians for Maintenance of Certification
Bibliographic record
Abstract
INTRODUCTION: Specialists in a Maintenance of Certification program are required to participate in assessment activities, such as chart audit, simulation, knowledge assessment, and multisource feedback. This study examined data from five different specialties to identify variation in participation in assessment activities, examine differences in the learning stimulated by assessment, assess the frequency and type of planned changes, and assess the association between learning, discussion, and planned changes. METHODS: E-portfolio data were categorized and analyzed descriptively. Chi-squared tests examined associations. RESULTS: A total of 2854 anatomical pathologists, cardiologists, gastroenterologists, ophthalmologists, and orthopedic surgeons provided data about 6063 assessment activities. Although there were differences in the role that learning played by discipline and assessment type, the most common activities documented across all specialties were self-assessment programs (n = 2122), feedback on teaching (n = 1078), personal practice assessments which the physician did themselves (n = 751), annual reviews (n = 682), and reviews by third parties (n = 661). Learning occurred for 93% of the activities and was associated with change. For 2126 activities, there were planned changes. Activities in which there was a discussion with a peer or supervisor were more likely to result in a change. CONCLUSIONS AND DISCUSSION: Although specialists engaged in many types of assessment activities to meet the Maintenance of Certification program requirements, there was variability in how assessment stimulated learning and planned changes. It seems that peer discussion may be an important component in fostering practice change and forming plans for improvement which bears further study.
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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 | 0.005 | 0.033 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".