General Internal Medicine (GIM): do the Puzzle Pieces Portray the Picture? A Continuous Quality Improvement Process for Entrustable Professional Activities (EPAs)
Bibliographic record
Abstract
Defining General Internal Medicine (GIM) has been difficult due to the tension between ensuring flexibility for varied environments and the need for national standards. With the launch of competency-based medical education, the Royal College of Physicians and Surgeons of Canada Specialty Committee in GIM (SCGIM) (national standard-setting body) had the opportunity to explicitly define the discipline via elaboration of the GIM competencies and Entrustable Professional Activities (EPAs). Defining the EPAs is the essence of defining the tasks of the discipline. We describe our SCGIM approach to the continuous review of the theoretical written documentation around EPAs in the “real world environment” in order to continuously refine the EPAs and ensure they are facilitating skill attainment. Major lessons learned (1) centralized feedback with simple reporting and multiple input is best; (2) there is tension between theory (perfect EPAs) and practical implementation; (3) it takes time to see how the EPAs are performing.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.037 | 0.094 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.004 | 0.016 |
| Scholarly communication | 0.011 | 0.017 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.006 | 0.019 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".