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Record W2161506337 · doi:10.1037/a0023876

A biopsychosocial formulation of pain communication.

2011· article· en· W2161506337 on OpenAlexaff
Thomas Hadjistavropoulos, Kenneth D. Craig, Steve Duck, Annmarie Caño, Liesbet Goubert, Philip L. Jackson, Jeffrey S. Mogil, Pierre Rainville, Michael Sullivan, Amanda C de C Williams, Tine Vervoort, Theresa Dever Fitzgerald

Bibliographic record

VenuePsychological Bulletin · 2011
Typearticle
Languageen
FieldNeuroscience
TopicPain Management and Placebo Effect
Canadian institutionsUniversité de MontréalUniversité LavalMcGill UniversityUniversity of British ColumbiaUniversity of Regina
Fundersnot available
KeywordsBiopsychosocial modelPsychologyVariety (cybernetics)Context (archaeology)Cognitive psychologyProcess (computing)Social psychologyDevelopmental psychologyPsychotherapist

Abstract

fetched live from OpenAlex

We present a detailed framework for understanding the numerous and complicated interactions among psychological and social determinants of pain through examination of the process of pain communication. The focus is on an improved understanding of immediate dyadic transactions during painful events in the context of broader social phenomena. Fine-grain consideration of social transactions during pain leads to an appreciation of sociobehavioral events affecting both suffering persons as well as caregivers. Our examination considers knowledge from a variety of perspectives, including clinical health psychology, social and developmental processes, evolutionary psychology, communication studies, and behavioral neuroscience.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.009
Threshold uncertainty score0.029

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0020.020
Scholarly communication0.0030.005
Open science0.0010.002
Research integrity0.0030.003
Insufficient payload (model declined to judge)0.0090.001

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 head0.098
GPT teacher head0.309
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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
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

Citations468
Published2011
Admission routes1
Has abstractyes

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