Phonological Processing and Reading in Children With Speech Sound Disorders
Bibliographic record
Abstract
PURPOSE: To examine the relationship between phonological processing skills prior to kindergarten entry and reading skills at the end of 1st grade, in children with speech sound disorders (SSD). METHOD: The participants were 17 children with SSD and poor phonological processing skills (SSD-low PP), 16 children with SSD and good phonological processing skills (SSD-high PP), and 35 children with typical speech who were first assessed during their prekindergarten year using measures of phonological processing (i.e., speech perception, rime awareness, and onset awareness tests), speech production, receptive and expressive language, and phonological awareness skills. This assessment was repeated when the children were completing 1st grade. The Test of Word Reading Efficiency was also conducted at that time. First-grade sight word and nonword reading performance was compared across these groups. RESULTS: At the end of 1st grade, the SSD-low PP group achieved significantly lower nonword decoding scores than the SSD-high PP and typical speech groups. The 2 SSD groups demonstrated similarly good receptive language skills and similarly poor articulation skills at that time, however. No between-group differences in sight word reading were observed. All but 1 child (in the SSD-low PP group) obtained reading scores that were within normal limits. CONCLUSION: Weaknesses in phonological processing were stable for the SSD-low PP subgroup over a 2-year period.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".