The Status of Diversity in Canadian Radiology—Where We Stand and What Can We Do About It
Bibliographic record
Abstract
Radiology has been identified as one of the medical specialties with the least gender, racial, and ethnic diversity. Despite the demonstrated benefits of gender and race diversity in medicine and industry, including innovation, empathy and improved patient outcomes, diversity in radiology in Canada is still lacking. In 2019, women represented around 63% of current medical graduates. However, within Canadian radiology practices, only 31.6% of radiologists are women. Women are also underrepresented in academic positions and the widening gender gap is present at higher academic ranks, indicating that women may not advance through academic hierarchies at the same pace as men. Although data on racial diversity in Canadian radiology practices is currently lacking, the representation of visible minorities in the general Canadian population is not reflected across Canadian radiology practices. Similarly, despite the Canadian Truth and Reconciliation Commission calling for action to increase the number of Indigenous healthcare workers, Indigenous people remain underrepresented in medicine and radiology. The importance of increasing diversity in radiology has gained recognition in recent years. Many solutions and strategies for national associations and radiology departments to improve diversity have been proposed. Leadership commitment is required to implement these programs to increase diversity in radiology in Canada with the ultimate goal of improving patient care. We review the current literature and available data on diversity within radiology in Canada, including the status of gender, race/ethnicity, and Indigenous people. We also present potential solutions to increase diversity.
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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 | 0.009 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.012 |
| Science and technology studies | 0.025 | 0.014 |
| Scholarly communication | 0.013 | 0.006 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 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".