Prevalence and characteristics of anti-Indigenous bias among Albertan physicians: a cross-sectional survey and framework analysis
Bibliographic record
Abstract
OBJECTIVE: Recent deaths of Indigenous patients in the Canadian healthcare system have been attributed to structural and interpersonal racism. Experiences of interpersonal racism by Indigenous physicians and patients have been well characterised, but the source of this interpersonal bias has not been as well studied. The aim of this study was to describe the prevalence of explicit and implicit interpersonal anti-Indigenous biases among Albertan physicians. DESIGN AND SETTING: This cross-sectional survey measuring demographic information and explicit and implicit anti-Indigenous biases was distributed in September 2020 to all practising physicians in Alberta, Canada. PARTICIPANTS: 375 practising physicians with an active medical licence. OUTCOMES: Explicit anti-Indigenous bias, measured by two feeling thermometer methods: participants slid an indicator on a thermometer to indicate their preference for white people (full preference is scored 100) or Indigenous people (full preference, 0), and then participants indicated how favourably they felt toward Indigenous people (100, maximally favourable; 0, maximally unfavourable). Implicit bias was measured using an Indigenous-European implicit association test (negative scores suggest preference for European (white) faces). Kruskal-Wallis and Wilcoxon rank-sum tests were used to compare bias across physician demographics, including intersectional identities of race and gender identity. MAIN RESULTS: Most of the 375 participants were white cisgender women (40.3%; n=151). The median age of participants was 46-50 years. 8.3% of participants felt unfavourably toward Indigenous people (n=32 of 375) and 25.0% preferred white people to Indigenous people (n=32 of 128). Median scores did not differ by gender identity, race or intersectional identities. White cisgender men physicians had the greatest implicit preferences compared with other groups (-0.59 (IQR -0.86 to -0.25); n=53; p<0.001). Free-text responses discussed 'reverse racism' and expressed discomfort with survey questions addressing bias and racism. CONCLUSIONS: Explicit anti-Indigenous bias was present among Albertan physicians. Concerns about 'reverse racism' targeting white people and discomfort discussing racism may act as barriers to addressing these biases. About two-thirds of respondents had implicit anti-Indigenous bias. These results corroborate the validity of patient reports of anti-Indigenous bias in healthcare and emphasise the need for effective intervention.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".