The Influence of Nonpharmacological Complex Therapy Conducted at Community Day-Care Center on the Cognitive Functions and Mood in Older Adults
Bibliographic record
Abstract
Nonpharmacological therapeutic interventions in elderly may lead to the reduction of cognitive and depressive symptoms. The aim of the study was to evaluate changes in cognitive functions and mood er or not. in older adults participating in therapy, conducted in the community day-care center (CD-CC). 46 elderly adults (21 M, 25 W) (SG) were examined. The control group (CG) included 45 adults (12 M, 33 W), who participated in the activities of the University of the Third Age (U3A). The following measuring tools were used: Mini-Mental State Examination (MMSE), Clock-Drawing Test (CDT), Verbal Fluency Test (VFT), Digit Span Test (DST), Stroop Color and Word Test (SCWT), Beck's Depression Inventory (BDI), and Hospital Anxiety and Depression Scale (HADS). The intervention consisted of CD-CC 6-month complex therapy. In the SG, compared to the CG, the scores on the: MMSE, CDT, VFT, DST, and SCWT were significantly lower (p<0,05), and BDI was significantly higher (p<0,05). After intervention, the SG and the CG, did not show substantial differences in their scores on the: MMSE, CDT, and BDI. In the SG, a significant improvement (p<0,05) was reported on the: VFT, BDI, and HADS scores. The CD-CC complex therapy can be helpful for the cognitive and emotional elderly functioning.
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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 | 0.000 |
| 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 | 0.000 | 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".