Methods to increase equity, inclusion, and diversity in Canadian urology programs
Bibliographic record
Abstract
INTRODUCTION: Women and ethnic minorities are underrepresented at all levels of training and practice in urology residency programs. Equity, diversity, and inclusion (EDI) is a growing field of interest in medical research and business literature, especially regarding recruitment. The objective of this review was to evaluate evidence-based strategies to increase EDI to improve urology residency recruitment. METHODS: A review was conducted using Ovid Medline to identify publications reporting strategies to increase women and underrepresented minorities (URM ) in healthcare fields. An evaluation of business models was incorporated. Identified strategies were sorted and ranked based on how many papers reported an increased proportion of women or URM in their program following implementation. RESULTS: We assessed 234 publications from 1972-2022. Eleven underwent full review. Six additional pieces of business literature were reviewed and incorporated. The following methods were most often identified to increase diversity: mentorship and holistic application review (six publications), as well as funded internship programs and diverse selection committees (four publications). Diversity statements and application blinding were highlighted by multiple business sources but were each only reviewed in one medical publication. CONCLUSIONS: Recommendations identified include mentorship, holistic application review by diverse selection committees with bias training, and development of funded internship programs. Standardized questions and rubrics were also well-studied. Business strategies, such as publishing diversity statements and application blinding, are less studied in medical education literature. This study is unique in its inclusion of both medical and business literature and highlights concrete strategies for urology residency programs to increase EDI during recruitment.
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 | 0.073 | 0.150 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.017 | 0.016 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".