Recommendations for Improvement of Equity, Diversity, and Inclusion in the CaRMs Selection Process
Bibliographic record
Abstract
Equity, diversity and inclusion (EDI) in the medical field is crucial for meeting the healthcare needs of a progressively diverse society. A diverse physician workforce enables culturally sensitive care, promotes health equity, and enhances the comprehension of the various needs and viewpoints of patients, ultimately resulting in more effective treatments and improved patient outcomes. However, despite the recognized benefits of diversity in the medical field, certain specialties, such as Radiology, have struggled to achieve adequate equity, diversity and inclusion, which results in a discrepancy in the demographics of Canadian radiologists and the patients we serve. In this review, we propose strategies from a committee within the Canadian Association of Radiologists (CAR) EDI working group to improve EDI in the CaRMS selection process. By adopting these strategies, residency programs can foster a more diverse and inclusive environment that is better positioned to address the health needs of a progressively diverse patient population, leading to improved patient outcomes, greater patient satisfaction, and advancements in medical innovation.
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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 | 0.033 | 0.082 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.014 | 0.003 |
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".