Seeing Ourselves in Others: Understanding and Addressing Biases in Medical School Admissions Processes
Bibliographic record
Abstract
Purpose: Medical school admissions is a vital area for advancing diversity, equity, and inclusion (DEI). Integrating bias recognition and management (BRM) within the context of admissions is critical in advancing DEI. However, there is a dearth of empirically informed literature on BRM in the admissions context. Therefore, this study sought to explore how individuals involved in admissions decisions process and integrate bias related feedback. Methods: The authors conducted a qualitative exploratory study using constructivist grounded theory. 21 semi-structured interviews were conducted with various participants in the admissions process at a North American medical school who had participated in bias related training. Participants included medical school faculty, senior medical students, and community volunteers. Results: Overall, participants expressed diverse perspectives on their personal biases and how these biases impact admissions decisions. Their reflections were shaped by their identities, values, and priorities, which varied based on whether they were faculty members, students, or community members. Participants also highlighted that their biases influenced their perceptions of the ideal admissions candidate, thus influencing their decision-making process. They emphasized the need for more opportunities to engage in dialogue with peers to openly share and discuss how to recognize and manage their biases. Conclusion: Our study suggests that fostering critical reflection about identity tensions, building and sustaining a community of practice, and facilitating sustained dialogue may provide admissions committees with an evidence-informed, meaningful, and sustained approach to advancing DEI through bias recognition and management.
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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 | 0.067 | 0.131 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.018 | 0.027 |
| Scholarly communication | 0.018 | 0.014 |
| Open science | 0.003 | 0.022 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".