Executive Dysfunctions, Reading Disabilities and Speech-Language Pathology Evaluation
Bibliographic record
Abstract
BACKGROUND: Many students with reading disabilities exhibit persisting reading problems despite intervention. The crucial difference between effective and struggling readers is their executive functions (EFs), and improved functions impact positively on learning to read and reading to learn. OBJECTIVES: Firstly, to show that high-risk and struggling students' persisting language and reading difficulties are accompanied by executive dysfunctions. Secondly, to present one student's daily struggles at school in a narrative based on teacher, parent and child interviews. METHOD: This retrospective study is based on speech-language pathology (SLP) evaluations of a clinical sample of 23 girls and boys aged 6-16 from a range of middle class families. While language and reading evaluations were tailored to the students' particular situation, i.e. age, grade, languages or complaint, EFs were examined in all with the Behaviour Rating Inventory of Executive Function teacher questionnaire. RESULTS: Virtually all students exhibited executive dysfunctions, and many showed a high risk of attention deficit hyperactivity disorders. CONCLUSIONS: This study demonstrated that inclusion of EFs in SLP evaluations is valuable in uncovering executive dysfunction comorbidity that may underlie persisting reading disorders. It is proposed that speech-language pathologists explicitly and routinely braid language and reading with EFs in their evaluations so to effectively predict, uncover and prevent persisting reading disabilities in students.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.002 | 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".