Painfully prejudiced? Racial stereotypicality and gender in pain perception, treatment, and empathy
Bibliographic record
Abstract
Black and female individuals are systemically undertreated for pain. While studies on racial biases in pain care have predominantly focused on the effects of between-race differences, few have considered the potential role of racial stereotypicality (how closely an individual resembles “typical” features of their race). Past research demonstrates that Black individuals perceived to be higher in racial stereotypicality face significant disadvantages in various domains, such as criminal justice and education, due to stronger associations with negative racial stereotypes. Findings suggest that stereotypicality may also have implications for healthcare. Therefore, the present study used a 2 (Racial Stereotypicality: Less Stereotypically Black vs. More Stereotypically Black) × 2 (Gender: Male vs. Female) between-subjects design to investigate how targets’ racial stereotypicality and gender influence lay perceivers’ pain perception, empathy, and treatment decisions. Furthermore, this study examined the relationships between perceivers’ perceptions of targets’ trustworthiness and attractiveness and their empathy toward targets. In an online experiment on Qualtrics, participants (N = 233) were randomly assigned to view one of four medical vignettes, each containing a photo insurance card, medical chart, and pain rating. Participants then rated the target’s pain, indicated how likely they would be to recommend a series of treatments for the target, completed a measure of empathic concern toward the target, and rated the target’s trustworthiness and attractiveness. Racial stereotypicality and gender did not significantly affect perceived pain, empathy, and most treatment decisions. However, perceivers’ empathy was positively correlated with targets’ perceived trustworthiness and attractiveness. This study provides a valuable starting point for further investigation into the role of racial stereotypicality and gender in pain care. Future research with working clinicians and advanced analyses are essential to deepening our understanding of the complexities of pain care disparities, and ultimately, achieving equitable pain 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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".