400 - Effects of Computerized Cognitive Training with an Elderly Community Sample
Bibliographic record
Abstract
Community-focused programs that promote active and healthy aging can help preserve cognitive capacities, prevent or reverse cognitive deficits. Computer-based cognitive training (CCT) is a promising non-pharmacological, cost -effective and accessible intervention to face the effects of age-related cognitive decline. Previous studies proved CCT to have equal or better efficacy compared to traditional interventions. This comparative multifactorial study aims to test the efficacy of a CCT in a non-randomized community sample of 74 older adults: G1-CCT Experimental group (n=43) (Mean age M=72.21, SD=12.65) and G2- Paper-Pencil Control group (n=31; M=77.94, SD=10.51). Pensioners (97.3%), mostly women (83.8 %) with basic education (51.4%) and without dementia diagnosis, completed a cognitive training program of 17 or 34 group sessions (twice a week). G2 undertook a classic cognitive paper-pencil stimuli tasks. G1, performed, additionally, individual CCT with COGWEB® in a multimodal format (intensive training of attention, calculation, memory, gnosis, praxis, executive functions). Both groups completed Portuguese versions of Mini -Mental State Examination (MMSE), Montreal Cognitive Assessment (MOCA); Geriatric Depressive Scale (GDS); Mini Dependence Assessment (MDA); WHOOQL 5 and Social Support Satisfaction Scale (ESSS) before and after participating in the program. Both groups reported better post-test scores on basic cognitive functions (MMSE, MOCA), Depression symptoms (GDS-30), subjective well-being and quality of life (WHOOQL-5). G1 presented higher MOCA and lower GDS scores before and after CCT, although, group differences become less expressive when interaction effects are considered. Results are in line with findings from past studies, CCT supported by the new technologies, is as a relevant cost-effective therapeutic tool for health professionals working with older adults. Particularly for preventive purposes of neuro-cognitive disorders.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".