Reading comprehension and reading related abilities in adolescents with reading disabilities and attention‐deficit/hyperactivity disorder
Bibliographic record
Abstract
Reading comprehension is a very complex task that requires different cognitive processes and reading abilities over the life span. There are fewer studies of reading comprehension relative to investigations of word reading abilities. Reading comprehension difficulties, however, have been identified in two common and frequently overlapping childhood disorders: reading disability (RD) and attention-deficit/hyperactivity disorder (ADHD). The nature of reading comprehension difficulties in these groups remains unclear. The performance of four groups of adolescents (RD, ADHD, comorbid ADHD and RD, and normal controls) was compared on reading comprehension tasks as well as on reading rate and accuracy tasks. Adolescents with RD showed difficulties across most reading tasks, although their comprehension scores were average. Adolescents with ADHD exhibited adequate single word reading abilities. Subtle difficulties were observed, however, on measures of text reading rate and accuracy as well as on silent reading comprehension, but scores remained in the average range. The comorbid group demonstrated similar difficulties to the RD group on word reading accuracy and on reading rate but experienced problems on only silent reading comprehension. Implications for reading interventions are outlined, as well as the clinical relevance for diagnosis.
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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 | 0.004 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".