The influence of ethnic concordance and discordance on verbal reports and nonverbal behaviours of pain
Bibliographic record
Abstract
This study's aim was to examine the influence of ethnic concordance on Chinese participants' pain report and nonverbal pain expression in a laboratory setting. Participants (n=102) were exposed to a cold pressor task under 1 of 2 conditions: Chinese milieu (n=52; participants exposed to Chinese experimenters and language), or European Canadian milieu (n=50; participants exposed to Euro-Canadian experimenters and English language). A reference group with 86 Euro-Canadian participants, exposed to the Euro-Canadian milieu only, was included for comparison. The Chinese groups did not differ on pain intensity during the pain task. However, Chinese participants in the Chinese milieu reported significantly higher affective pain and displayed more nonverbal behaviour of pain than the Chinese participants in the Euro-Canadian milieu. In addition, compared to the Euro-Canadian group, both Chinese groups reported higher pain intensity during the pain task and greater affective pain after immersion. The results demonstrated that an ethnically concordant milieu is associated with increased nonverbal pain displays and affective pain report. These findings suggest that research on ethnic disparities in pain treatment should examine ethnic concordance between observer and individual in pain.
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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 | 0.008 |
| 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) | 0.002 | 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".