Individual differences in text processing and recall in children with and without <scp>ADHD</scp>
Bibliographic record
Abstract
Abstract The current study examined whether children with and without ADHD differed in text processing when contradictory information was present. Forty‐seven children between 10 and 14 years old performed a self‐paced reading task. Half the passages contained contradictory information. Additionally, language and cognitive skills were assessed to examine the relationships between text processing and individual differences in these abilities (working memory, oral sentence recognition, verbal and non‐verbal intelligence, word reading fluency and decoding ability). Results indicate that the non‐ADHD group modulated their reading behaviour based on the presence of inconsistent information, whereas the ADHD group did so in response to the consistent information. However, this task effect in the ADHD group was primarily observed with children who scored low on background measures (e.g., verbal intelligence, working memory). Additionally, the children with ADHD recalled fewer units of information than their non‐ADHD peers. Correlations demonstrated that the pattern of relationships between the text comprehension measures (i.e., true‐false test and text recall) and the background measures differed between the two groups, such that measures were more closely associated with each other in the ADHD group. Results are discussed in terms of educational implications to support children with ADHD who experience reading comprehension difficulties.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".