Why I Wrote the New Royal College General Internal Medicine Exam: Redefining Our Identity and Revalidation
Bibliographic record
Abstract
Summary In 2010, the Royal College of Physicians and Surgeons of Canada (RCPSC) recognized General Internal Medicine (GIM) as a distinct subspecialty. Soon after this recognition came a new written certification exam, the successful completion of which awards the applicant the title of General Internist. For those of us who trained prior to the new status and examination, GIM was the default designation after four years of internal medicine training if a subspecialty was not pursued. What does this new subspecialty status mean for our professional identity, qualifications, and public credibility? Twelve years after my successful completion of the Internal Medicine (IM) certification exams, I voluntarily applied for consideration to write the first RCPSC exam in GIM, without a clear reason why. My reflection on the days leading up to the exam and writing the exam itself led me to understand why I did it. The process addressed my skepticism around designating GIM as a unique subspecialty, and through this I have come to appreciate the need for our profession to embrace revalidation.
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 | 0.054 | 0.209 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.017 | 0.019 |
| Scholarly communication | 0.016 | 0.007 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.008 | 0.024 |
| Insufficient payload (model declined to judge) | 0.008 | 0.005 |
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".