A racial bias test with tertiary cancer centre employees: why anti-racist measures are required for First Nations Australians cancer care equity
Bibliographic record
Abstract
Objective To examine implicit bias in employees at a cancer centre using an Australian race (Aboriginal-white) Implicit Association Test (IAT), in an attempt to understand a potential factor for inequitable outcomes of First Nations Australians cancer patients. Methods All employees at an Australian cancer centre were invited to take part in a web-based, cross-sectional study using an Australian race IAT. The results were analysed using Welch t-tests, linear regression and ANOVA. Results Overall, 538/2871 participants (19%) completed the IAT between January and June 2020. The mean IAT was 0.147 (s.d. 0.43, P < 0.001, 95% CI 0.11-0.18), and 60% had a preference for white over First Nations Australians. There was no significant mean difference in IAT scores between sub-groups of gender, age or clinical/non-clinical employees. 21% of employees (95% CI 17.65-24.53) had moderate to strong preference for white over First Nations Australians, compared to 7.1% with moderate to strong preference for First Nations over white Australians (95% CI 5.01-9.09). Conclusions Inequitable cancer survival for First Nations patients has been well established and cancer is now the leading cause of mortality. This paper documents the presence of racial bias in employees at one cancer centre. We argue that this cannot be understood outside the history of colonialism and its effects on First Nations Australians, healthcare workers and our society. Further research is required to evaluate measures of racism, its effect on health care, and how to eliminate it.
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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 | 0.024 | 0.102 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".