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Record W1982017466 · doi:10.1016/j.pain.2010.07.022

Mildly negative social encounters reduce physical pain sensitivity

2010· article· en· W1982017466 on OpenAlexafffund
Terry K. Borsook, Geoff MacDonald

Bibliographic record

VenuePain · 2010
Typearticle
Languageen
FieldPsychology
TopicDeath Anxiety and Social Exclusion
Canadian institutionsUniversity of Toronto
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsHypoalgesiaPsychologySocial connectednessPain toleranceMoodPain catastrophizingPerceptionAnalgesicClinical psychologyDevelopmental psychologyPhysical therapyThreshold of painSocial psychologyChronic painNociceptionMedicineAnesthesiaPsychiatryHyperalgesiaInternal medicine

Abstract

fetched live from OpenAlex

While previous research has demonstrated a reduction in physical pain sensitivity in response to social exclusion, the manipulations employed have arguably been far removed from typical daily experience. The purpose of this study was to investigate the effects of relatively ordinary social encounters on the perception of pain. Healthy participants rated the intensity and unpleasantness of painful stimuli before and after engaging in a structured interaction with a confederate who was instructed to either be warm and friendly or indifferent. A control group was asked to perform a similar structured activity, but alone. Consistent with predictions, participants who experienced the mildly negative social exchange reported lower pain intensity and unpleasantness after the encounter relative to baseline, whereas those exposed to the positive social exchange did not evidence any change in pain ratings. These results were not mediated by changes in mood or perceived connectedness. If mildly negative social encounters can provoke an analgesic effect, it is possible that social hypoalgesia may be considerably more commonplace than previously realized. Discussion focuses on the role of stress-induced hypoalgesia, and the implications of the results for clinical assessments of 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)0.005
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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.647
Threshold uncertainty score0.564

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.001
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 integrity0.0000.001
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 head0.014
GPT teacher head0.314
Teacher spread0.300 · 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 designNot applicable
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

Citations45
Published2010
Admission routes2
Has abstractyes

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