Racial Differences in Pain Treatment and Empathy in a Canadian Sample
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
BACKGROUND: Evidence of inadequate pain treatment as a result of patient race has been extensively documented, yet remains poorly understood. Previous research has indicated that nonwhite patients are significantly more likely to be undertreated for pain. OBJECTIVE: To determine whether previous findings of racial biases in pain treatment recommendations and empathy are generalizable to a sample of Canadian observers and, if so, to determine whether empathy biases mediate the pain treatment disparity. METHODS: Fifty Canadian undergraduate students (24 men and 26 women) watched videos of black and white patients exhibiting facial expressions of pain. Participants provided pain treatment decisions and reported their feelings of empathy for each patient. RESULTS: Participants demonstrated both a prowhite treatment bias and a prowhite empathy bias, reporting more empathy for white patients than black patients and prescribing more pain treatment for white patients than black patients. Empathy was found to mediate the effect of race on pain treatment. CONCLUSIONS: The results of the present study closely replicate those from a previous study of American observers, providing evidence that a prowhite bias is not a peculiar feature of the American population. These results also add support to the claim that empathy plays a crucial role in racial pain treatment disparity.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it