THE PREVALENCE OF STUTTERING, VOICE DISORDER, AND SPEECH SOUND DISORDERS IN PRESCHOOLERS IN SHAHREKORD, IRAN
Bibliographic record
Abstract
The inability to communicate easily and clearly can have far-reaching debilitating effects, not only in childhood, but throughout a lifetime. The aim of this study was to determine the prevalence of stuttering, voice disorder, and speech sound disorders in Persian preschoolers in Shahrekord, Iran. Information about 1,387 children ages 5 to 6 was obtained via face-to-face screening and assessment. The total prevalence of speech disorders was 17.1%. The prevalence of stuttering was 1.5%, while 13.4% had a speech sound disorder, and 2.2% had voice disorder. The prevalence of stuttering was higher in males (2.2%) than females (0.7%); of speech sound disorders was higher in males (17.4%) than females (9.1%); and of voice disorder was higher in males (2.6%) than females (1.6%). The prevalence of stuttering and speech sound disorder was significantly different according to gender and positive family history. The prevalence figures revealed that a considerable number of preschoolers with speech disorders were missed in parents’ and teachers’ reports. Those children required more intensive communication support than they were receiving. Therefore, classroom teachers should work with speech and language pathologists to identify and assess preschoolers with communication disorders, and to develop intervention strategies.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".