Implicit Race Bias in Pediatric Patients: Understanding Patient Perspectives
Bibliographic record
Abstract
INTRODUCTION: Implicit racial bias has been well studied in adults, including among orthopaedic surgeons, through the Implicit Association Test (IAT). Recent studies suggest implicit race bias is also present among children. Explicit racial preference has been studied in children through The Clark Doll Test since the 1930s. The purpose of this study was to determine whether implicit and explicit racial biases are present among pediatric orthopaedic patients. METHODS: A prospective, cross-sectional survey was administered to pediatric orthopaedic patients aged 7 to 18 years at clinics in a tertiary pediatric hospital setting. The survey included a Clark Doll Test to determine whether pediatric patients expressed explicit bias, followed by a race IAT to determine whether pediatric patients expressed implicit bias. Preference and magnitude of implicit bias as demonstrated on the IAT was calculated using standard D-scores. RESULTS: A total of 96 patients were consented and included in this study. Overall, pediatric patients demonstrated a slight pro-White implicit bias (M = 0.22) on IAT testing. Pediatric patients who identified as White or European American and Hispanic or Latinx both had the strongest pro-White implicit bias (M = 0.35). Patients who identified as Black or African American demonstrated no implicit racial bias (M = -0.13) on IAT testing. No notable explicit bias was observed in participants of any racial background. DISCUSSION: This study contributes evidence that pediatric orthopaedic patients express implicit racial bias on IAT testing, with an overall slight pro-White bias. It also provides insight into the dissociation of implicit and explicit racial bias in childhood and adolescence. CONCLUSION: We encourage future research on implicit bias among pediatric patients in the orthopaedic community to provide a better understanding and possible solutions to bias-related challenges in health care.
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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 | 0.001 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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; 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".