The healing relationship in Indigenous patients' pain care: Influences of racial concordance and patient ethnic salience on healthcare providers' pain assessment
Bibliographic record
Abstract
Indigenous persons suffer from among the highest rates of chronic pain in the United States. Using a relationship-centered medical decision-making framework, this study sought to examine the influence of Indigenous racial concordance and patient ethnic salience on providers’ assessment of pain. From May to October 2010, pre-identified healthcare providers working exclusively with Indigenous patients in the United States were randomly assigned an online clinical case vignette presenting an Indigenous patient reporting chronic lower back pain. A 2 × 2 analysis of variance, between-subjects design, was conducted with the predictor variables racial concordance and patient ethnic salience on the outcome measure of providers’ ratings of patient’s pain on a visual analogue scale. We found a significant interactional effect between racial concordance and patient ethnic salience on providers’ pain assessment ratings. Indigenous providers tended to rate the patient with higher Indigenous ethnic salience more congruently with the self-reported pain ratings, perhaps due to perceived similarities and lowered unconscious bias. This is the first known study to examine racial concordance of the healthcare provider and ethnic salience of the patient in pain care. This study informs healthcare provider practice and consideration of patients’ racial/cultural attributes and possible influence on assessment bias, which may be particularly relevant among Indigenous patients. More research is needed to identify specific interventions to improve cultural awareness and sensitivity for Indigenous persons who suffer from pain.
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How this classification was reachedexpand
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 | 0.009 | 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".