Indirect Effects of Task-switching on Reading Comprehension via Decoding Skills among Adolescents with Autism
Bibliographic record
Abstract
Purpose: We examined whether variation in task-switching indirectly predicted variation in reading comprehension by way of variation in decoding, and furthermore, whether this effect differed among adolescents with ASD compared to an age-matched control group. Methods: We examined whether the association between task-switching and reading comprehension was mediated by decoding among a sample of autistic adolescents with autism spectrum disorder (ASD; N = 45, Mage = 14.9 years) and an age-matched comparison group (N = 43, Mage = 14.3 years). Analyses were conducted using path models to test for direct effects of decoding and task switching on reading comprehension, as well as indirect effects of task-switching on reading comprehension by way of decodingResults: Though the indirect effect did not significantly differ between the ASD and comparison groups, the indirect effect of task-switching on reading comprehension via decoding was only significant among adolescents with ASD. This suggest that task-switching plays a particularly prominent role in decoding and reading comprehension among adolescents with ASD.Conclusion: Though further work is necessary to replicate this effect, the findings may have implications for interventions that may target improvements in word reading abilities as a means for improving reading comprehension outcomes among youth with ASD.
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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 | 0.002 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 | 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".