Can computerized working memory training improve impaired working memory, cognition and psychological health?
Bibliographic record
Abstract
OBJECTIVE: To study if computerized working memory (WM) training, in the sub-acute phase after acquired brain injury, in patients with impaired WM, improves WM, cognition and psychological health. RESEARCH DESIGN: A randomized study (n = 47) with an intervention group (IG) and a control group (CG), mean age 47.7 years. The WAIS-III NI, Digit span, Arithmetic, Letter-Number Sequences (Working Memory sub-scale), Spatial span, the Barrow Neurological Institute Screen for Higher Cerebral Functions (BNIS) and the self-rating scales DEX and HADS were administered at baseline and at follow-ups at 6 and 18 weeks. Both groups underwent integrated rehabilitation. The IG also trained with the computerized WM training program, Cogmed QM, which was offered to the CG and followed up after the study completion. RESULTS: Both groups improved after their WM training in Working Memory, BNIS and in Digit span, particularly the reversed section. Both the BNIS and the Digit span differed significantly between the IG and CG due to the greater improvement in the IG after their WM training. Psychological health improved as both groups reported less depressive symptoms and the CG also less anxiety, after the training. CONCLUSION: Results indicated that computerized WM training can improve working memory, cognition and psychological health.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".