Resident Equity, Diversity, and Inclusion Committee: A Mechanism for Programmatic Change
Bibliographic record
Abstract
To the Editor: In recent years, the importance of selecting a physician body that reflects the population it serves has been recognized as fundamental to excellence in training and patient care. To this end, North American medical education programs have developed policies and programs to increase diversity in medicine. However, learners from marginalized backgrounds are still disproportionately overrepresented in reported experiences of discrimination, harassment, and intimidation. 1 The resident equity, diversity, and inclusion (EDI) committee at the University of Toronto Paediatric Residency Program was conceptualized by residents and program leadership with 3 goals: (1) ensure that that program policies, practices, and organizations align with evidence-based EDI principles; (2) bring an intersectional, antioppressive, and antiracist lens to resident education; and (3) create initiatives to promote an organizational culture of EDI. The committee has a consultative role with separate oversight of other resident committees, and reports to the residency program committee and program director, to ensure the power to enact systems-level change. The committee brings together resident EDI experts who reflect diverse identities, experiences, and perspectives. Two resident co-chairs were identified by the program director after which 5 resident representatives were selected for their EDI expertise, and 1 member with experience in a different training program was chosen to provide an external perspective. Individuals from historically marginalized groups were prioritized. The program director also sits on the committee with the option for on-camera discussions and consultation with experts. The committee operates on a consensus-based decision-making model to allow for nuanced exploration of EDI issues. Established in 2021, the committee identified goals through an action priority matrix, organized resident education sessions about antioppression principles to recognize and address anti-Black/Indigenous racism, and supported residents through rising anti-Asian racism and the Israeli–Palestine conflict. Work is ongoing to establish a longitudinal resident EDI curriculum, recognize EDI work in awards, reduce barriers to learners celebrating non-Eurocentric holidays, critically review the resident selection process, and establish EDI-themed morbidity and mortality rounds. We hope that sharing the underlying principles, goals, composition, and decision-making structure of our resident EDI committee can serve as both a launching point for dialogue and action and a blueprint for establishing similar trainee-led committees at other programs with the goal of ensuring justice in residency programs at individual and systemic levels. Acknowledgments: The authors thank Dr. Adelle Atkinson and the other members of the Resident EDI Committee—Dr. Ashna Asim, Dr. Yamna Ali, Dr. Bonnie Cheung, Dr. Sabrina Lue Tam, and Dr. Lorna Sampson-Riden—for their guidance and input throughout this process.
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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 | 0.040 | 0.162 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.007 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.006 | 0.006 |
| Research integrity | 0.016 | 0.023 |
| Insufficient payload (model declined to judge) | 0.011 | 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".