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Record W2035960478 · doi:10.1037/abn0000041

Stress sensitivity and stress sensitization in psychopathology: An introduction to the special section.

2015· article· en· W2035960478 on OpenAlexaff
Kate L. Harkness, Elizabeth P. Hayden, Nestor L. Lopez‐Duran

Bibliographic record

VenueJournal of Abnormal Psychology · 2015
Typearticle
Languageen
FieldPsychology
TopicMental Health Research Topics
Canadian institutionsWestern University
Fundersnot available
KeywordsPsychopathologyStressorSensitizationPsychologyStress (linguistics)Developmental psychologyClinical psychologyCognitive psychologyNeuroscience

Abstract

fetched live from OpenAlex

The goal of this special section is to examine the mechanisms of enhanced sensitivity and sensitization to stress as they influence the etiology and pathophysiology of psychopathology. The 12 articles in the section focus on some of the most crucial and unanswered questions regarding the underlying mechanisms and functional consequences of stress sensitivity and stress sensitization in psychopathology. They address the constructs of stress sensitivity and stress sensitization using state-of-the-art, and often novel, methodologies. The special section also focuses on an important terminological distinction between two related but distinct stress mechanisms that are often conflated. Individuals who are sensitive to stress possess this characteristic as a putative trait that develops through genetically mediated transactional relations between temperamental characteristics and the early contextual environment. In contrast, individuals who are sensitized to stress become so over time through repeated exposure to external, as well as endogenous, stressors. Enhanced stress sensitivity and sensitization have been included in conceptual models of psychopathology. Yet, the specific mechanisms by which these stress processes impact the onset and course of psychiatric disorders are not fully understood. These articles focus on several mechanistic accounts of stress sensitivity and sensitization.

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)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.407
Threshold uncertainty score0.499

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
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 head0.058
GPT teacher head0.414
Teacher spread0.357 · 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 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

Citations89
Published2015
Admission routes1
Has abstractyes

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