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Record W2767131282 · doi:10.1177/1087054717737162

A Measure of Emotional Regulation and Irritability in Children and Adolescents: The Clinical Evaluation of Emotional Regulation–9

2017· article· en· W2767131282 on OpenAlexaff
Jenna Pylypow, Declan Quinn, Don Duncan, Lloyd Balbuena

Bibliographic record

VenueJournal of Attention Disorders · 2017
Typearticle
Languageen
FieldMedicine
TopicAttention Deficit Hyperactivity Disorder
Canadian institutionsInterior HealthUniversity of Saskatchewan
Fundersnot available
KeywordsRasch modelPsychologyIrritabilityEmotional regulationClinical psychologyRating scaleScale (ratio)Developmental psychologyEmotional disorderPsychiatryAnxiety

Abstract

fetched live from OpenAlex

Objective: To develop a scale for emotional regulation using item response theory. Method: Eighteen Swanson Nolan and Pelham (SNAP-IV) items that loaded on an emotional dysregulation factor were submitted to Rasch analysis. After eliminating the items that violated Rasch criteria, the remaining items were examined for reliability and validated against the Conners’ emotional lability index. Results: A nine-item scale for emotional regulation was developed that satisfies the Rasch model and reliably distinguishes emotionally dysregulated/irritable children and adolescents. A score of 4 or higher in this scale has optimal accuracy for identifying children and adolescents with current significant dysfunction in emotional regulation. Among youth with ADHD inattentive, hyperactive–impulsive, and combined types, 42%, 56%, and 71% met the Clinical Evaluation of Emotional Regulation–9 (CEER-9) threshold for emotional lability, respectively. Conclusion: A nine-item scale whose sum total is a measure of emotional regulation is proposed as a tool for clinical and research purposes.

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)0.004
metaresearch head score (Gemma)0.002
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 score0.026
Threshold uncertainty score0.372

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
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 head0.057
GPT teacher head0.377
Teacher spread0.320 · 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

Citations19
Published2017
Admission routes1
Has abstractyes

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