Progression of Language Complexity During Treatment With the Lidcombe Program for Early Stuttering Intervention
Bibliographic record
Abstract
The Lidcombe Program is an operant treatment for early stuttering. Outcomes indicate that the program is effective; however, the underlying mechanisms leading to a successful reduction of stuttering remain unknown. The purpose of this study was to determine whether fluency achieved with the Lidcombe Program was accompanied by concomitant reduction of utterance length and decreases in linguistic complexity. Standardized language tests were administered pretreatment to 4 male preschool children. Spontaneous language samples were taken 2 weeks prior to treatment, at Weeks 1, 4, 8, and 12 during treatment, and 6 months after the onset of treatment. Samples were analyzed for mean length of utterance (MLU), percentage of simple and complex sentences, number of different words (NDW), and percentage of syllables stuttered. Analysis revealed that all participants presented with language skills in the average and above average range. The children achieved an increase in stutter-free speech accompanied by increases in MLU, percentage of complex sentences, and NDW. For these preschool children who stutter, improved stutter-free speech during treatment with the program appeared to be achieved without a decrease in linguistic complexity. Theoretical and clinical implications are discussed.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".