Anti-Indigenous bias of medical school applicants: a cross-sectional study
Bibliographic record
Abstract
BACKGROUND: Structural and interpersonal anti-Indigenous racism is prevalent in Canadian healthcare. The Truth and Reconciliation Commission calls on medical schools to address anti-Indigenous bias in students. We measured the prevalence of interpersonal anti-Indigenous bias among medical school applicants to understand how the medical school selection process selects for or against students with high levels of bias. METHODS: All applicants to a single university in the 2020-2021 admissions cycle were invited to participate. Explicit anti-Indigenous bias was measured using two sliding scale thermometers. The first asked how participants felt about Indigenous people (from 0, indicating 'cold/unfavourable' to 100, indicating 'warm/favourable') and the second asked whether participants preferred white (scored 100) or Indigenous people (scored 0). Participants then completed an implicit association test examining preferences for European or Indigenous faces (negative time latencies suggest preference for European faces). Explicit and implicit anti-Indigenous biases were compared by applicant demographics (including gender and racial identity), application status (offered an interview, offered admission, accepted a position), and compared to undergraduate medical and mathematics students. RESULTS: There were 595 applicant respondents (32.4% response rate, 64.2% cisgender women, 55.3% white). Applicants felt warmly toward Indigenous people (median 96 (IQR 80-100)), had no explicit preference for white or Indigenous people (median 50 (IQR 37-55), and had mild implicit preference for European faces (- 0.22 ms (IQR -0.54, 0.08 ms)). There were demographic differences associated with measures of explicit and implicit bias. Applicants who were offered admission had warmer feelings toward Indigenous people and greater preference for Indigenous people compared to those were not successful. CONCLUSIONS: Medical school applicants did not have strong interpersonal explicit and implicit anti-Indigenous biases. Outlier participants with strong biases were not offered interviews or admission to medical school.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".