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Record W2326249638 · doi:10.1097/nmd.0000000000000169

Stress Reactivity of Emotional and Verbal Speech Content in Schizophrenia

2014· article· en· W2326249638 on OpenAlexfundno aff
M. Dombrowski, Amanda McCleery, Stanford W. Gregory, Nancy M. Docherty

Bibliographic record

VenueThe Journal of Nervous and Mental Disease · 2014
Typearticle
Languageen
FieldNeuroscience
TopicNeuroscience and Music Perception
Canadian institutionsnot available
FundersCanadian Institutes of Health Research
KeywordsReactivity (psychology)PsychologySchizophrenia (object-oriented programming)Stress (linguistics)Nonverbal communicationTrier social stress testAudiologyCognitive psychologyDevelopmental psychologyFight-or-flight responseMedicinePsychiatry

Abstract

fetched live from OpenAlex

Speech fundamental frequencies (SFFs) are nonverbal sound frequencies that convey emotion in speech. The degree of SFF long-term averaged spectra (LTAS) convergence between conversants reflects aspects of conversant-reported quality of the interaction (e.g., emotional synchrony). This study investigated whether SFF LTAS convergence between inpatients diagnosed with schizophrenia (n = 20) and an interviewer was associated with severity of illness (SOI), formal speech disturbance (FSD), and stress reactivity of FSD. Participants provided speech samples describing stressful and nonstressful life experiences. In the stress condition, SFF LTAS was negatively correlated with SOI and FSD. Moreover, patients exhibiting stress reactivity of FSD also evidenced stress reactivity of SFF LTAS. These findings suggest that the emotional and verbal contents of speech are disrupted by stress in schizophrenia, and SOI is associated with FSD and reduced emotional communication during stressful conditions. The interaction between stress reactivity of FSD and SFF LTAS supports the construct validity of a reactivity dimension in schizophrenia.

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)0.000
metaresearch head score (Gemma)0.002
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 score0.002

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.001
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 head0.037
GPT teacher head0.265
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 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

Citations11
Published2014
Admission routes1
Has abstractyes

Explore more

Same venueThe Journal of Nervous and Mental DiseaseSame topicNeuroscience and Music PerceptionFrench-language works237,207