Elevating equity: advancing diversity and inclusivity through trialing bias reduction tools in the general internal medicine program resident application process
Bibliographic record
Abstract
BACKGROUND: Decades of medical data show worse patient outcomes among racial and gender minorities due to implicit, explicit, and structural biases. Increasing representation of marginalized groups among care providers is imperative to help address this. Limited literature exists on bias awareness strategies for interviewers during the selection of applicants to General Internal Medicine (GIM) programs in Canada. This study examines the trial of bias reduction tools to increase interviewers' awareness of implicit biases. METHODS: The Model of Improvement framework guided the trail of an instructional video, the adapted implicit association test (IAT), and a paper awareness tool (PAT) to increase interviewers' awareness of implicit biases during the University of Alberta's GIM applicant selection. An anonymous online survey was disseminated to physician interviewers. Descriptive statistics (percentages) and a modified sentiment analysis was completed. RESULTS: 10 of 14 interviewers completed the survey. Respondents reported an increased awareness of using bias reduction tools (IAT, 25%; video, 71%; PAT, 67%) to inform them on their implicit biases. The future use of IAT, video, and PAT was supported by 50%, 71%, and 67% of interviewers, respectively. CONCLUSIONS: Interviewers prefer the instructional video and PAT over the IAT. Textual responses suggest existing concerns for biases inherent to the interview process yet 70% (7/10) of respondents believe that interviews should have a weighting of 50% towards final ranking of candidates. As many institutions continue to rely on interviews to evaluate candidates, our findings indicate the need for a national study to develop a framework to mitigate inherent biases during interviewing of candidates.
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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 | 0.392 | 0.455 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.006 | 0.007 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.004 | 0.014 |
| Research integrity | 0.002 | 0.003 |
| 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".