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Record W2888694928 · doi:10.1002/dev.21774

Trajectory of heart period to socioaffective threat in shy children

2018· article· en· W2888694928 on OpenAlexafffund
Kristie L. Poole, Louis A. Schmidt

Bibliographic record

VenueDevelopmental Psychobiology · 2018
Typearticle
Languageen
FieldPsychology
TopicChild and Adolescent Psychosocial and Emotional Development
Canadian institutionsMcMaster University
FundersSocial Sciences and Humanities Research Council of CanadaNatural Sciences and Engineering Research Council of CanadaCanadian Institutes of Health Research
KeywordsShynessPsychologyLongitudinal studyDevelopmental psychologyPeriod (music)DemographyMedicinePsychiatryAnxiety

Abstract

fetched live from OpenAlex

Abstract Although shyness is characterized by distinct psychophysiological correlates, we know very little about the development of these correlates. In this longitudinal study, we examined how children's shyness was associated with trajectories of heart period (HP) to socioaffective threat across four assessments spanning approximately 2 years. Children (Mage = 6.39 years) viewed age‐appropriate, socioaffective videos at each visit while having their HP measured concurrently. A growth curve analysis revealed that low shy children had a relatively lower HP at enrollment, but experienced increases in HP across visits, while high shy children exhibited relatively stable low HP across visits while viewing threat‐related socioaffective video stimuli. These patterns did not exist for HP during resting baseline or HP to nonthreatening video stimuli. These findings suggest that longitudinal patterns of HP among shy children may reflect a stable, characteristic way of responding to socioaffective threat, and possibly a physiological mechanism underlying shyness in some children.

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 score0.005
Threshold uncertainty score0.010

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 integrity0.0000.001
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 head0.018
GPT teacher head0.303
Teacher spread0.286 · 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

Citations8
Published2018
Admission routes2
Has abstractyes

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