A comparative analysis of graduate preparedness for a career in General Internal Medicine before and after national subspecialty recognition to inform curricular changes: have we met the mark?
Bibliographic record
Abstract
Background: A survey of General Internal Medicine (GIM) graduates published in 2006 revealed large training gaps that informed the development of the first national GIM objectives of training in 2010. The first recognized GIM certification examination was written by candidates in 2014. The landscape is again changing with the introduction in 2019 of competency-by-design (CBD) to GIM training. This study aims to examine pre-existing and emerging training gaps with standardization of GIM curricula and identify new training needs to inform CBD curricula. Methods: GIM graduates from all 16 Canadian programs from 2014 -2019 were emailed a survey modeled after the original study published in 2006. Graduates were asked about their preparedness and importance ratings for various elements of practice. Results: Many of the previously identified gaps (difference between importance and preparedness ratings) have been resolved in specific clinical areas (obstetrical and perioperative medicine) and skills (exercise stress testing) although some still require ongoing work in areas such as substance use disorders. Importantly, gaps still exist in preparedness for some intrinsic roles (e.g. managerial skills). Conclusions: The development of a national GIM curriculum has helped close some educational gaps but some still exist. Our study provides data needed to meet the evolving needs of our graduates.
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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 | 0.004 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.002 | 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 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".