Can I Ask a Question About URiM Awards That I Don’t Know the Answer to? Designing an Award for Underrepresented Medical Education Researchers
Bibliographic record
Abstract
Meaningful Equity, Diversity, and Inclusion (EDI) efforts may be stymied by concerns about whether proposed initiatives are performative or tokenistic. The purpose of this project was to analyze discussions by the Research in Medical Education (RIME) Program Planning committee about how best to recognize and support underrepresented in medicine (URiM) researchers in medical education to generate lessons learned that might inform local, national, and international actions to implement meaningful EDI initiatives. Ten RIME Program Planning Committee members and administrative staff participated in a focus group held virtually in August 2021. Focus group questions elicited opinions about "if and how" to establish a URiM research award. The focus group was recorded, transcribed, and thematically analyzed. Recognition of privilege, including who has it and who doesn't, underpinned the focus group discussion, which revolved around 2 themes: (1) tensions between optics and semantics, and (2) potential unintended consequences of trying to level the medical education playing field. The overarching storyline threaded throughout the focus group discussion was intentionality. Focus group participants sought to avoid performativity by creating an award that would be meaningful to recipients and to career gatekeepers such as department chairs and promotion and tenure committees. Ultimately, participants decided to create an award that focused on exemplary Equity, Diversity, and Inclusion (EDI) scholarship, which was eventually named the "RIME URiM Research Award." Difficult but productive conversations about EDI initiatives are necessary to advance underrepresented in medicine (URiM) scholarship. This transparent commentary may trigger further critical conversations.
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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 | 0.074 | 0.196 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.023 | 0.022 |
| Scholarly communication | 0.017 | 0.020 |
| Open science | 0.003 | 0.016 |
| Research integrity | 0.008 | 0.017 |
| Insufficient payload (model declined to judge) | 0.012 | 0.006 |
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".