Effects of multi-faceted cognitive training program for elders with cognitive impairment living in social welfare home for older persons
Bibliographic record
Abstract
Background: Elders living in social welfare home are at risk of developing dementia due to lacking of opportunities to perform problem solving activities under non-familiar situations. Dementia declines cognitive functions leading to a limitation of performing individual activities of daily living. Therefore, the elders need a cognitive training program which may be enhancing their cognitive abilities. That program’s effectiveness would be beneficial in slowing down the rise in numbers of demented elders who live with cognitive impairment at the social welfare home. Objectives: To study the effects of a multi-faceted cognitive training program on cognitive abilities in elders with cognitive impairment living in social welfare home. Materials and methods: Participants were the elders living at Thammapakorn Social Welfare Development Center for Older Persons, Chiang Mai Province. Twenty-four elders with cognitive impairment were selected through purposive sampling method combined a set of screening tests, including the Mini-Mental State Examination (MMSE), the Montreal Cognitive Assessment (MoCA), and the Depression Assessment 9 Questions (9Qs). Participants were divided into a control and an experimental group (n=12 each). The experimental group underwent multi-faceted cognitive training program 3 times per week, for 6 consecutive weeks. The outcome measurements were the Digit Span Test, the Thai Cognitive-Perceptual Test (Thai-CPT), and the Dynamic Loewenstein Occupational Therapy Cognitive Assessment (DLOTCA). Data was analyzed using descriptive, Mann-Whitney U Test, and Wilcoxon Signed Rank Test. Results: After completing the multi-faceted cognitive training program, both experimental and control groups had demonstrated statistically significant differences in post-test in attention, memory, and executive functions. Within the experimental group, there was no significant difference in those cognitive areas, whereas the reduction of cognitive scores was significantly found within the control group. Conclusion: The multi-faceted cognitive training program could prevent cognitive deterioration for elders with cognitive impairment living in social welfare home.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".