Cognitive Rehabilitation for Attention Deficit/Hyperactivity Disorder (ADHD): Promises and Problems.
Bibliographic record
Abstract
OBJECTIVE: Cognitive training entails the repeated exercise of a specific cognitive process over a period of time to improve performance on the trained task as well as on tasks that were not specifically trained (transfer effect). Cognitive training shows promise in remediating deficits in children with attention deficit/hyperactivity disorder (ADHD) - a disorder believed to stem from deficient cognitive processes - where the focus has been primarily on training working memory and attention. We discuss evidence from studies that have produced broad, limited, or no transfer effects with the goal of identifying factors that may be responsible for this heterogeneity. RESULTS: There are several implicit assumptions that appear to drive researchers' decisions regarding both the selection of cognitive abilities to train as well as the training tasks chosen to target those abilities. We identify these implicit assumptions and their weaknesses. We also draw attention to design limitations that may be contributing to lack of transfer. CONCLUSION: Although the overall pattern of findings from these studies is promising, the methodological and theoretical limitations associated with the literature limit conclusions about the efficacy of cognitive training as a rehabilitation method for ADHD. We hypothesize several suggestions that may improve training effects and summarize the evidence which led to our hypotheses.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".