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Record W2044865631 · doi:10.1016/j.pain.2006.06.002

The effects of experimenter status and cardiovascular reactivity on pain reports

2006· article· en· W2044865631 on OpenAlexaff
Tavis S. Campbell, Mark D. Holder, Christopher France

Bibliographic record

VenuePain · 2006
Typearticle
Languageen
FieldNeuroscience
TopicStress Responses and Cortisol
Canadian institutionsUniversity of British ColumbiaOkanagan University CollegeUniversity of British Columbia, Okanagan CampusUniversity of Calgary
Fundersnot available
KeywordsCold pressor testPain tolerancePsychologyReactivity (psychology)Blood pressureClinical psychologyTask (project management)Association (psychology)AudiologyDevelopmental psychologyThreshold of painMedicineHeart rateInternal medicineAlternative medicine

Abstract

fetched live from OpenAlex

Existing evidence suggests that experimenter characteristics can influence research participants' pain reports. To examine the possibility that cardiovascular reactivity may serve as a potential mediating mechanism, blood pressure responses to stress were recorded in 117 healthy women during an arithmetic task and a subsequent cold pressor pain task. Laboratory sessions were conducted by two experimenters (i.e., a university professor and a graduate research assistant), who differed significantly on participant ratings of perceived social status. Participants tested by the university professor showed greater blood pressure responsivity to arithmetic and had higher pain tolerance and lower pain unpleasantness ratings compared to those tested by the research assistant. Further, hierarchical regression analyses indicated that blood pressure reactivity was a potential mediator of the association between experimenter status and both pain tolerance and unpleasantness, suggesting that perceived experimenter status may elicit physiological adaptations that can reduce sensitivity to painful laboratory stimuli.

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)0.005
metaresearch head score (Gemma)0.024
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: Methods · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.995
Threshold uncertainty score0.028

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.024
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.008
GPT teacher head0.231
Teacher spread0.222 · 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.

Study designObservational
DomainMethods
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

Citations23
Published2006
Admission routes1
Has abstractyes

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