Journey into the unknown: Considering the international medical graduate perspective on the road to Canadian residency during the COVID-19 pandemic.
Bibliographic record
Abstract
The COVID-19 pandemic has had a tremendous effect on education programs worldwide, including medical education. Particularly, International Medical Graduates (IMGs) planning to pursue residency training in Canada have been profoundly impacted. Cancellation of away electives, as well as changes to the format, timeline, and requirements of mandatory medical licensing exams has left IMG residency applicants in uncharted territory. Given that IMGs comprise up to 25% of the Canadian healthcare force, and often are based in underserviced areas, the licensure and eligibility of IMGs to continue to enter the Canadian healthcare force is of the utmost importance in the midst of the COVID-19 pandemic. As the pandemic evolves, it is imperative that key decision makers and stakeholders continue to consider the downstream effect for IMGs and their eligibility to practice in Canada.
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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 | 0.011 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.050 | 0.023 |
| Scholarly communication | 0.017 | 0.010 |
| Open science | 0.005 | 0.013 |
| Research integrity | 0.016 | 0.032 |
| Insufficient payload (model declined to judge) | 0.012 | 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".