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Record W2012790804 · doi:10.1016/j.pain.2006.02.007

Catastrophic thinking and heightened perception of pain in others

2006· article· en· W2012790804 on OpenAlexaff
M. J.L. Sullivan, Marc O. Martel, Dean A. Tripp, Ashley Savard, Geert Crombez

Bibliographic record

VenuePain · 2006
Typearticle
Languageen
FieldNeuroscience
TopicPain Management and Placebo Effect
Canadian institutionsQueen's UniversityUniversité de Montréal
Fundersnot available
KeywordsPerceptionPain perceptionPsychologyCognitive psychologyMedicineNeuroscienceAnesthesia

Abstract

fetched live from OpenAlex

Past research has shown that pain catastrophizing contributes to heightened pain experience. The hypothesis advanced in this study was that individuals who score high on measures of pain catastrophizing would also perceive more intense pain in others. The study also examined the role of pain behaviour as a determinant of the relation between catastrophizing and estimates of others' pain. To test the hypothesis, 60 undergraduates were asked to view videotapes of individuals taking part in a cold pressor procedure. Each individual in the videotapes was shown three times over the course of a 1min immersion such that the same individual was observed experiencing different levels of pain. Correlational analyses revealed a significant positive correlation between levels of pain catastrophizing and inferred pain intensity, r=.31, p<.01. Follow-up analyses indicated that catastrophizing was associated with a heightened propensity to rely on pain behaviour as a basis for drawing inferences about others' pain experience. Catastrophizing was associated with more accurate pain inferences on only one of three indices of inferential accuracy. The pattern of findings suggests that increasing reliance on pain behaviour as a means of inferring others' pain will not necessarily yield more accurate estimates. Discussion addresses the processes that might underlie the propensity to attend more to others' pain behaviour, and the clinical and interpersonal consequences of perceiving more pain in others.

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)0.015
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.707
Threshold uncertainty score0.505

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0150.001
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 head0.012
GPT teacher head0.222
Teacher spread0.211 · 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 designSimulation or modeling
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

Citations100
Published2006
Admission routes1
Has abstractyes

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