Application of the Challenge Point Framework During Treatment of Speech Sound Disorders
Bibliographic record
Abstract
Purpose The purpose of this article is to provide trial-by-trial practice performance data in relation to learning (outcome probe data) as collected from 18 treatment sessions provided to children with severe speech sound disorders. The data illustrate the practice-learning paradox: Specific, perfect practice performance is not required for speech production learning. Method We detailed how nine student speech-language pathologists (SSLPs) implemented and modified the motor learning practice conditions to reach a proposed challenge point during speech practice. Eleven participants diagnosed with a severe speech sound disorder received high-intensity speech therapy 3 times per week for 6 weeks. SSLPs implemented treatment procedures with the goal of achieving at least 100 practice trials while manipulating practice parameters to maintain practice at the challenge point. Specifically, child performance was monitored for accuracy in five-trial increments, and practice parameters were changed to increase functional task difficulty when the child's performance was high (four or five correct responses) or to decrease functional task difficulty when the child's performance was low (fewer than four correct responses). The practice stimulus, type and amount of feedback, structure of practice, or level of support might be changed to ensure practice at the challenge point. Results On average, the children achieved 102 practice trials per session at a level of 58% correct responses. Fast achievement of connected speech with the lowest amount of support was associated with high scores on generalization probes. Even with high levels of error during practice, the children improved percent consonants correct with maintenance of learning 3 months posttreatment. Conclusion The results of this study show that it may not be necessary to overpractice or maintain a high degree of performance accuracy during treatment sessions to achieve transfer and retention of speech production learning.
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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 | 0.007 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".