Reducing racial disparities in pain treatment: The role of empathy and perspective-taking
Bibliographic record
Abstract
Epidemiological evidence indicates that African Americans receive lower quality pain treatment than European Americans. However, the factors causing these disparities remain unidentified, and solutions to this problem remain elusive. Across three laboratory experiments, we examined the hypotheses that empathy is not only causing pain treatment disparities but that empathy-inducing interventions can reduce these disparities. Undergraduates (Experiments 1 and 2) and nursing professionals (Experiment 3) watched videos of real Black and White patients' genuine facial expressions of pain, provided pain treatment decisions, and reported their feelings of empathy for each patient. The efficacy of an empathy-inducing, perspective-taking intervention at reducing pain treatment disparities was also examined (Experiments 2 and 3). When instructed to attempt to provide patients with the best care, participants exhibited significant pro-White pain treatment biases. However, participants engaged in an empathy-inducing, perspective-taking intervention that instructed them to imagine how patients' pain affected patients' lives exhibited upwards of a 55% reduction in pain treatment bias in comparison to controls. Furthermore, Pro-White empathy biases were highly predictive of pro-White pain treatment biases. The magnitude of the empathy bias experienced predicted the magnitude of the treatment bias exhibited. These findings suggest that empathy plays a crucial role in racial pain treatment disparities in that it appears not only to be one likely cause of pain treatment disparities but also is an important means for reducing racial disparities in pain treatment.
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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 | 0.004 | 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 | 0.001 | 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".