Phonological Memory and Naming Speed Predict Response to Intervention in Elementary School-Aged Children with Word-level Reading Difficulties
Bibliographic record
Abstract
Background: We examined the predictors of response to intervention (RTI) in an individually administered program based on the Orton-Gillingham method.The study used a single-group design with intervention and pre-and post-testing.Method: Prior to intervention, 50 children with word-level reading difficulties aged 5 to 12 were administered measures of phonological awareness, naming speed, and phonological memory.They also were given, both prior to and following intervention, eight reading and spelling measures.Results: Participants made significant gains in all reading and spelling measures (p < .001).Regression analyses indicated that, after controlling participants' age, number of sessions, and pre-test scores, pre-intervention phonological memory predicted growth in most reading and spelling outcomes (ps ranging from .05 to .001) and naming speed predicted growth in rate, fluency, and sight word efficiency (ps < .05).Conclusion: Phonological awareness did not predict growth in any outcome, suggesting that programs which successfully target it eliminate its predictive power.Weaknesses in phonological memory and naming speed may be preventing some children from benefitting from interventions such as this one.Suggestions for mitigating these effects and implications for future research and interventions 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 | 0.001 | 0.007 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".