Advancing equity in healthcare systems: understanding implicit bias and infant mortality
Bibliographic record
Abstract
Using data from the Centers for Disease Control and Prevention’s Wide-ranging Online Data for Epidemiologic Research (CDC WONDER) and Project Implicit, this study examined whether anti-Black implicit racial biases predict infant mortality for Black Americans. We examined state-level mean Black-White Implicit Association Test (BW-IAT) Bias Scores and controlled for explicit bias scores and White infant mortality rates for over 1.7 million American participants across ten different ethnoracial groups between 2018–2020. Hierarchical linear regressions determined state-level anti-Black implicit bias significantly predicted state-level Black infant mortality rates, above and beyond explicit bias and White infant mortality, in 2018 ( b = .32, t (34) = 2.09, p < .05), 2019 ( b = .30, t (34) = 2.09, p < .05), and 2020 ( b = .32, t (34) = 2.18, p < .05). State-level anti-Black implicit bias also explained a significant proportion of variance in state-level infant mortality rates, in 2018 ( R 2 = 0.30, F( 3, 35) = 4.89, p < 0.01), 2019 ( R 2 = .33, F (3, 36) = 5.95, p < .01), and 2020 ( R 2 = .39, F (3, 35) = 7.58, p < .001). Also, among healthcare professionals, there are similar levels of implicit biases compared to the general American population. Findings suggest that implicit racial bias is a risk factor for Black infant mortality. These findings also point to the ethical challenge implicit biases pose to equitable decision-making and patient-provider relationships in healthcare. By integrating these insights into interdisciplinary discussions, this study provides supporting data for systemic reforms and anti-bias training to create a healthcare system grounded in fairness and equity.
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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 | 0.022 | 0.055 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".