Do Racial Stereotypes Contribute to Medical Misdiagnosis of Child Abuse?
Bibliographic record
Abstract
Despite growing recognition that misdiagnoses of child abuse can lead to wrongful convictions, little empirical work has examined how the medical community may contribute to these errors. Previous research has documented the existence and content of stereotypes that associate race with child abuse. The current study examines whether emergency medical professionals rely on this stereotype to fill in gaps in ambiguous cases involving Black children, thereby increasing the potential for misdiagnoses of child abuse. Specifically, we tested whether the race-abuse stereotype led participants to attend to more abuse-related details than infection-related details when an infant patient was Black versus White. We also tested whether this heuristic decision-making would be affected by contextual case facts; specifically, we examined whether race bias would be exacerbated or mitigated by a family’s involvement with child protective services (CPS). Results showed that participants did exhibit some biased information processing in response to the experimental manipulations. Even so, the race-abuse stereotype and heuristic decision-making did not cause participants to diagnose a Black infant patient with abuse more often than a White infant patient, regardless of his family’s involvement with CPS. These findings help illuminate how race may lead to different outcomes in cases of potential child abuse, while also demonstrating potential pathways through which racial disparities in misdiagnosis of abuse and subsequent wrongful convictions can be prevented.
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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 | 0.025 | 0.132 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 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".