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Record W2004316218 · doi:10.1016/j.pain.2011.04.023

The influence of ethnic concordance and discordance on verbal reports and nonverbal behaviours of pain

2011· article· en· W2004316218 on OpenAlexaffabout
Annie Y. Hsieh, Dean A. Tripp, Li‐Jun Ji

Bibliographic record

VenuePain · 2011
Typearticle
Languageen
FieldMedicine
TopicMusculoskeletal pain and rehabilitation
Canadian institutionsQueen's University
Fundersnot available
KeywordsNonverbal communicationConcordanceEthnic groupPsychologyClinical psychologyMedicineDevelopmental psychologyInternal medicine

Abstract

fetched live from OpenAlex

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.

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.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.154
Threshold uncertainty score0.214

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.015
GPT teacher head0.274
Teacher spread0.259 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations42
Published2011
Admission routes2
Has abstractyes

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