Promoting Equity, Diversity, and Inclusion in Medicine: A Comprehensive Toolkit for Change in Radiology
Bibliographic record
Abstract
This toolkit presents a comprehensive framework for a toolkit intended to increase equity, diversity, and inclusion (EDI) within the medical field and recommendations. We advocate for clear, comprehensive definitions and interpretations of fundamental EDI terms, laying the groundwork necessary for initiating and maintaining EDI initiatives. Furthermore, we offer a systematic approach to establishing EDI committees within medical departments, accentuating the pivotal role these committees play as they drive and steer EDI strategies. This toolkit also explores strategies tailored for the recruitment of a diverse workforce. This includes integral aspects such as developing inclusive job advertisements, implementing balanced search methods for candidates, conducting unbiased appraisals of applications, and structuring diverse hiring committees. The emphasis on these strategies not only augments the diversity within medical institutions but also sets the stage for a more holistic approach to healthcare delivery. Therefore, by adopting the recommended strategies and guidelines outlined in this framework, medical institutions and specifically radiology departments can foster an environment that embodies inclusivity and equity, thereby enhancing the quality of patient care and overall health outcomes.
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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 | 0.062 | 0.061 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.013 | 0.009 |
| Science and technology studies | 0.005 | 0.008 |
| Scholarly communication | 0.011 | 0.011 |
| Open science | 0.005 | 0.020 |
| Research integrity | 0.007 | 0.012 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".