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Record W2618208570 · doi:10.1002/dev.21522

Afternoon cortisol provides a link between self‐regulated anger and peer‐reported aggression in typically developing children in the school context

2017· article· en· W2618208570 on OpenAlexaff
Eva Oberle, Kaitlyn McLachlan, Nicole Catherine, Ursula Brain, Kimberly A. Schonert‐Reichl, Joanne Weinberg, Tim F. Oberlander

Bibliographic record

VenueDevelopmental Psychobiology · 2017
Typearticle
Languageen
FieldNeuroscience
TopicStress Responses and Cortisol
Canadian institutionsBC Children's HospitalSimon Fraser UniversityUniversity of GuelphLearning PartnershipUniversity of British Columbia
Fundersnot available
KeywordsAggressionAngerPsychologyContext (archaeology)Cortisol awakening responseDevelopmental psychologyClinical psychologyHydrocortisoneInternal medicineMedicine

Abstract

fetched live from OpenAlex

Aggression jeopardizes positive development in children and predicts social and academic maladjustment in school. The present study determined the relationships among anger dysregulation (a marker of emotion regulation), cortisol activity (a biomarker of stress), and peer-nominated aggression in typically developing children in their everyday classroom setting (N = 151, Mean age = 10.86, SD =.74). Salivary cortisol was collected at 09:15, 11:45, and 14:45 hr across 4 consecutive days. Children provided self-reports of anger regulation; peers reported proactive and reactive aggressive behaviors. Hierarchical linear regression analyses, followed by a bootstrapping analysis identified basal afternoon cortisol as a significant mediator between anger regulation and peer-reported aggression. More dysregulated anger significantly predicted lower afternoon cortisol, which in turn predicted increased peer-reported aggression. These results align with previous research on links among hypocortisolism, emotional regulation, and behavior, and suggest a possible meditational pathway between emotion and behavior regulation via decreased afternoon cortisol levels.

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)0.000
metaresearch head score (Gemma)0.001
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 score0.130
Threshold uncertainty score0.724

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
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 science0.0010.000
Research integrity0.0000.000
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 head0.045
GPT teacher head0.318
Teacher spread0.273 · 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

Citations18
Published2017
Admission routes1
Has abstractyes

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