Factorial Temperament Structure in Stuttering, Voice-Disordered, and Typically Developing Children
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".