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Record W3162839810 · doi:10.1017/s0954579421000183

Observational measures of early irritability predict children's psychopathology risk

2021· article· en· W3162839810 on OpenAlexafffund
Ola Mohamed Ali, Lindsay N. Gabel, Kasey Stanton, Erin A. Kaufman, Daniel N. Klein, Elizabeth P. Hayden

Bibliographic record

VenueDevelopment and Psychopathology · 2021
Typearticle
Languageen
FieldPsychology
TopicChild and Adolescent Psychosocial and Emotional Development
Canadian institutionsWestern University
FundersCanadian Institutes of Health Research
KeywordsIrritabilityAngerObservational studyPsychologyPsychopathologyContext (archaeology)Clinical psychologyDevelopmental psychologyChild psychopathologyObservational methods in psychologyConstruct (python library)PsychiatryAnxietyMedicine

Abstract

fetched live from OpenAlex

Irritability is a transdiagnostic feature of diverse forms of psychopathology and a rapidly growing literature implicates the construct in child maladaptation. However, most irritability measures currently used are drawn from parent-report questionnaires not designed to measure irritability per se; furthermore, parent report methods have several important limitations. We therefore examined the utility of observational ratings of children's irritability in predicting later psychopathology symptoms. Four-hundred and nine 3-year-old children (208 girls) completed observational tasks tapping temperamental emotionality and parents completed questionnaires assessing child irritability and anger. Parent-reported child psychopathology symptoms were assessed concurrently to the irritability assessment and when children were 5 and 8 years old. Children's irritability observed during tasks that did not typically elicit anger predicted their later depressive and hyperactivity symptoms, above and beyond parent-reported irritability and context-appropriate observed anger. Our findings support the use of observational indices of irritability and have implications for the development of observational paradigms designed to assess this construct in childhood.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.076
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.047
GPT teacher head0.284
Teacher spread0.237 · 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.

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
Published2021
Admission routes2
Has abstractyes

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