MétaCan
Menu
Back to cohort
Record W2145233313 · doi:10.1016/j.pain.2006.02.001

The relation between catastrophizing and the communication of pain experience

2006· article· en· W2145233313 on OpenAlexaff
M. J.L. Sullivan, Marc O. Martel, Dean A. Tripp, Ashley Savard, Geert Crombez

Bibliographic record

VenuePain · 2006
Typearticle
Languageen
FieldMedicine
TopicMusculoskeletal pain and rehabilitation
Canadian institutionsQueen's UniversityUniversité de Montréal
Fundersnot available
KeywordsCold pressor testPain catastrophizingEmpathyPsychologyCoping (psychology)Physical therapyClinical psychologyChronic painMedicinePsychiatryBlood pressure

Abstract

fetched live from OpenAlex

The Communal Coping Model of pain catastrophizing proposes that pain catastrophizers enact pain behaviors in order to solicit support or empathy from their social environment. By this account, pain catastrophizers might be expected to engage in behavior aimed at maximizing the probability that their pain will be perceived by others in their social environment. To test this prediction, 40 undergraduates were videotaped during a cold pressor procedure. A separate sample of 20 (10 men, 10 women) undergraduates were asked to view the video sequences and infer the pain ratings of the cold pressor participants. Correlational analyses revealed that higher levels of pain catastrophizing of the cold pressor participants were associated with observer inferences of more intense pain, r=.39, p<.01. The relation between cold pressor participants' level of pain catastrophizing and observer inferences of pain intensity was mediated by the cold pressor participants' pain behavior. Although pain catastrophizing was associated with observers' inferences of more intense pain, cold pressor participants' level of pain catastrophizing was not associated with observers' accuracy in inferring self-reported pain. Implications of the findings for theory and clinical practice are addressed.

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)0.008
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 score0.472
Threshold uncertainty score0.264

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0080.002
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 head0.009
GPT teacher head0.262
Teacher spread0.253 · 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

Citations176
Published2006
Admission routes1
Has abstractyes

Explore more

Same venuePainSame topicMusculoskeletal pain and rehabilitationFrench-language works237,207