Effects of a computerized working memory training program on working memory, attention, and academics in adolescents with severe LD and comorbid ADHD: a randomized controlled trial
Bibliographic record
Abstract
BACKGROUND: Youths with coexisting learning disabilities (LD) and attention deficit hyperactivity disorder (ADHD) are at risk for poor academic and social outcomes. The underlying cognitive deficits, such as poor working memory (WM), are not well targeted by current treatments for either LD or ADHD. Emerging evidence suggests that WM might be improved by intensive and adaptive computerized training, but it remains unclear whether this intervention would be effective for adolescents with severe LD and comorbid ADHD. METHODS: A total of sixty 12- to 17-year olds with LD/ADHD (52 male, 8 female, IQ > 80) were randomized to one of two computerized intervention programs: working memory training (Cogmed RM) or math training (Academy of Math) and evaluated before and 3 weeks after completion. The criterion measures of WM included auditory-verbal and visual-spatial tasks. Near and far transfer measures included indices of cognitive and behavioral attention and academic achievement. RESULTS: Adolescents in the WM training group showed greater improvements in a subset of WM criterion measures compared with those in the math-training group, but no training effects were observed on the near or far measures. Those who showed the most improvement on the WM training tasks at school were rated as less inattentive/hyperactive at home by parents. CONCLUSIONS: Results suggest that WM training may enhance some aspects of WM in youths with LD/ADHD, but further development of the training program is required to promote transfer effects to other domains of function.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".