Improving physician diversity and inclusion benefits physicians and patients
Bibliographic record
Abstract
A diverse physician workforce in the Canadian health care system would result in more cultural competence, greater patient satisfaction, and improved population health. However, increasing representation and diversity does not automatically resolve issues of inequity, inequality, and discrimination. In this article, we discuss three broad areas of health care — the clinical environment, academic advancement, and leadership — that require intentional, systemic change if we are to make a lasting impact in terms of increasing the diversity and inclusion of underrepresented groups in medicine, and consequently, improve health outcomes. Inclusive and equitable practices to target pay inequity, unconscious bias, opposition to career advancement, and sexual harassment are integral to diverse physician recruitment and retention. Equity strategies and checks to remediate systemic biases in academic advancement through grant funding, academic criteria of merit for promotion, and the acknowledgment of differences of experience can be employed to improve equity in academic medicine. The long-standing culture, policies, and traditions of institutions within the medical establishment must be combated with a collaborative effort to foster equity through the engagement of academics and physicians from underrepresented minority groups, and the implementation of implicit bias training and meaningful accountability for creating a safe, equitable work environment for diverse physicians. Any proposed solution to improve equity and diversity should not be taken as a fixed principle to follow uncritically, but rather as a starting point for understanding and implementing the unique changes required in various local contexts.
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 | 0.009 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.016 | 0.006 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.018 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".