Training of attentional control in mild cognitive impairment with executive deficits: Results from a double-blind randomised controlled study
Bibliographic record
Abstract
This study evaluated the efficacy of a cognitive intervention for attentional control in older adults with mild cognitive impairment (MCI) with an executive deficit. It also sought to verify if the benefits of training generalised to primary and secondary outcome measures. Participants (n = 24) were randomly assigned to a training programme or active control condition. The experimental group completed a computer-based training programme involving Variable Priority (VP) coordination of both components of a dual task, to which was added a self-regulatory strategy designed to augment meta-cognition. The active control group performed Fixed Priority (FP) training: rote practice of the same dual task involving a visual detection task combined with an alpha-arithmetic task. Six one-hour training sessions were held three times a week for two weeks. Participants were tested pre- and post-training to detect improvement and transfer effects. Both groups improved on the visual detection and alpha-arithmetic tasks completed in focused attention, but only participants receiving VP training significantly improved their dual-task cost in accuracy for the visual detection task. As for transfer effects, both FP and VP training produced improvements on select outcome measures: focused attention, speed of processing, and switching abilities. No reliable advantage for generalisability of VP over FP training was found. Overall, these findings indicate that cognitive intervention may improve attentional control in persons with MCI and an executive deficit.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".