Reactivity to Stress and the Cognitive Components of Math Disability in Grade 1 Children
Bibliographic record
Abstract
This study investigated the relationship among working memory, processing speed, math performance, and reactivity to stress in 83 Grade 1 children. Specifically, 39 children with math disability (MD) were compared to 44 children who are typically achieving (TA) in mathematics. It is the first study to use a physiological index of stress (salivary cortisol levels) to measure children's reactivity while completing tasks that assess the core components of MD: working memory for numbers, working memory for words, digits backward, letter number sequence, digit span forward, processing speed for numbers and words, block rotation, and math tasks. Grade 1 children with MD obtained significantly lower scores on the letter number sequence and quantitative concepts tasks. Higher levels of reactivity significantly predicted poorer performance on the working memory for numbers, working memory for words, and quantitative concepts tasks for Grade 1 children, regardless of math ability. Grade 1 children with MD and higher reactivity had significantly lower scores on the letter number sequence task than the children with MD and low reactivity. The findings suggest that high reactivity impairs performance in working memory and math tasks in Grade 1 children, and young children with high reactivity may benefit from interventions aimed at lowering anxiety in stressful situations, which may improve learning.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.000 |
| 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".