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Factorial Temperament Structure in Stuttering, Voice-Disordered, and Typically Developing Children

2009· article· en· W1972180004 on OpenAlexaff
Kurt Eggers, Luc F. De Nil, Bea Van den Bergh

Bibliographic record

VenueJournal of Speech Language and Hearing Research · 2009
Typearticle
Languageen
FieldPsychology
TopicStuttering Research and Treatment
Canadian institutionsUniversity of Toronto
FundersStrong
KeywordsTemperamentPsychologyExtraversion and introversionStutteringDevelopmental psychologyEquivalence (formal languages)AudiologyAffect (linguistics)PersonalityClinical psychologyBig Five personality traitsSocial psychologyMedicineMathematics

Abstract

fetched live from OpenAlex

PURPOSE: The purpose of this study was to determine whether the underlying temperamental structure of the Dutch Children's Behavior Questionnaire (CBQ; B. Van den Bergh & M. Ackx, 2003) was identical for children who stutter (CWS), typically developing children (TDC), and children with vocal nodules (CWVN). METHOD: A principal axis factor analysis was performed on data obtained with the Dutch CBQ from 69 CWS, 149 TDC, and 41 CWVN. All children were between the ages of 3;0 (years;months) and 8;11. RESULTS: Results indicated a 3-factor solution, identified as Extraversion/Surgency, Negative Affect, and Effortful Control, for each of the participant groups, showing considerable similarity to previously published U.S., Chinese, Japanese, and Dutch samples. Congruence coefficients were highest for CWS and TDC and somewhat more modest when comparing CWVN and TDC. The Effortful Control factor consistently yielded the lowest congruence coefficients. CONCLUSION: These data confirm that although stuttering, voice-disordered, and typically developing children may differ quantitatively with regard to mean scores on temperament scales, they are similar in terms of their overall underlying temperament structure. The equivalence of temperament structure provides a basis for further comparison of mean group scores on the individual temperament scales.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.534
Threshold uncertainty score0.395

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 science0.0000.000
Research integrity0.0000.001
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 head0.046
GPT teacher head0.416
Teacher spread0.369 · 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

Citations53
Published2009
Admission routes1
Has abstractyes

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