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Record W2161996710 · doi:10.1111/1469-8986.3860879

Comparison of hemodynamic responses to social and nonsocial stress: Evaluation of an anger interview

2001· article· en· W2161996710 on OpenAlexaff
Kenneth M. Prkachin, David E. Mills, Caroline Zwaal, Janice Husted

Bibliographic record

VenuePsychophysiology · 2001
Typearticle
Languageen
FieldNeuroscience
TopicStress Responses and Cortisol
Canadian institutionsUniversity of WaterlooUniversity of Northern British Columbia
Fundersnot available
KeywordsAngerPsychologyHemodynamicsHeart rateBlood pressureStressorStroke volumeMental arithmeticCardiac outputCardiologyDevelopmental psychologyClinical psychologyInternal medicineMedicine

Abstract

fetched live from OpenAlex

Hemodynamic responses to an anger interview and cognitive and physical stressors were compared, and the stability of associated hemodynamic reactions examined. Participants experienced control, handgrip, counting, and mental arithmetic tests and an anger interview on two occasions. Systolic and diastolic blood pressure, heart rate, stroke volume, and cardiac output were measured. Total peripheral resistance was also derived. The anger interview produced larger, more sustained changes in blood pressure in both sessions than the other stressors. These changes were largely a consequence of increased peripheral resistance. Consistent with previous findings, handgrip was associated with a resistance-type reaction whereas arithmetic was associated with a cardiac output-type reaction. There was low-to-modest stability of hemodynamic reactions to the interview. Further research is necessary to optimize its utility in studies of cardiovascular function. Nevertheless, the findings underscore the ability of ecologically relevant stressors to provoke unique configurations of cardiovascular activity.

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)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
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)0.0010.000

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 head0.167
GPT teacher head0.458
Teacher spread0.290 · 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 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

Citations34
Published2001
Admission routes1
Has abstractyes

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