Bibliographic record
Abstract
CanadaChanges to the TDM Examination in 2026The Medical Council of Canada (MCC) is pleased to announce the implementation of a new assessment model for the Therapeutics Decision-Making (TDM) Examination, planned for January 2026. Administered by the MCC on behalf of the Practice-Ready Assessment (PRA) programs, the TDM Examination is part of the selection process for qualified internationally trained physicians to enter most PRA programs.To enhance the candidate experience and increase the efficiency of the exam process, a new format will be implemented featuring multiple-choice questions and short-menu questions divided into two sections, and an optional 20-minute break between the sections will be offered. The MCC anticipates these changes will also allow for a faster release of results.The revised TDM Examination will maintain its integrity, validity and reliability, with the questions assessing the competence of candidates at the level required of a family physician practicing independently and safely in Canada.The exam will continue to be delivered globally through Prometric both at test centers and via remote proctoring.The MCC website will be updated with additional information as it becomes available.Further information is available at https://mcc.ca/news/changes-to-the-tdm-examination-in-2026/IndiaAdditional Medical Seats to Solve Doctor Shortage?In the Union Budget 2025, India's Union Finance Minister Nirmala Sitharaman announced the addition of 10,000 new medical seats across medical colleges and hospitals in the coming year to help address the nation's doctor shortage. This is part of a broader plan to increase India's medical education capacity by 75,000 seats over the next five years. However, regional disparities in doctor distribution, infrastructure challenges, and the retention of medical professionals in rural areas remain key hurdles to achieving healthcare equity across India.Further information is available at https://timesofindia.indiatimes.com/education/news/will-indias-10000-new-medical-seats-solve-the-doctor-shortage-crisis/articleshow/117824316.cms
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.008 | 0.001 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.543 | 0.280 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".