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Psychophysiological Models of Stress

2019· reference-entry· en· W2976231839 on OpenAlexaff
Ellen Zakreski, Jens C. Pruessner

Bibliographic record

Venuenot available
Typereference-entry
Languageen
FieldNeuroscience
TopicStress Responses and Cortisol
Canadian institutionsMcGill University
Fundersnot available
KeywordsDiseasePsychological stressStress (linguistics)PsychologyHeart rateCognitive psychologyNeuroscienceBlood pressureMedicineClinical psychologyPathologyInternal medicine

Abstract

fetched live from OpenAlex

Psychophysiological models have a long history within stress research of trying to explain the link between stress exposure and psychological and physiological disease. The current chapter tries to offer complementary perspectives on this issue. First, it covers the relevant physiological systems (sympathetic, parasympathetic, enteric nervous system) and their markers (heart rate, heart rate variability, blood pressure), such that the reader receives an overview of the significant factors at play. Second, it provides an overview of the various forms of stress (acute, chronic, and stress during early life periods) that are believed to put the individual at heightened risk to develop stress-related disease. Finally, it presents the theories and models that have emerged over the years that try to explain how the various forms of stress can eventually lead to psychological and physical disease. The chapter ends with a short outlook on some recent work emphasizing the interaction between the various systems at play, and how that by itself can play a role in the origin of stress-related disease.

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)0.001
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: Review · Consensus signal: none
Teacher disagreement score0.013
Threshold uncertainty score0.044

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0010.002
Scholarly communication0.0020.002
Open science0.0010.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0130.003

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 head0.090
GPT teacher head0.319
Teacher spread0.228 · 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
GenreReview

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

Citations3
Published2019
Admission routes1
Has abstractyes

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