Effect of Preschool Working Memory, Language, and Narrative Abilities on Inferential Comprehension at School-Age in Children with Spina Bifida Myelomeningocele and Typically Developing Children
Bibliographic record
Abstract
Children with spina bifida myelomeningocele (SBM) are more likely to display a pattern of good-decoding/poor comprehension than their neurologically intact peers. The goals of the current study were to (1) examine the cognitive origins of one of the component skills of comprehension, bridging inferences, from a developmental perspective and (2) to test the effects of those relations on reading comprehension achievement. Data from a sample of children with SBM and a control group (n = 78) who participated in a longitudinal study were taken from age 36-month and 9.5-year time points. A multiple mediation model provided evidence that three preschool cognitive abilities (working memory/inhibitory control, oral comprehension, narrative recall), could partially explain the relation between group and bridging inference skill. A second mediation model supported that each of the 36-month abilities had an indirect effect on reading comprehension through bridging inference skill. Findings contribute to an understanding of both typical and atypical comprehension development, blending theories from the developmental, cognitive, and neuropsychological literature.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".