How do aging, socioeconomic status, and gender affect verbal fluency, cognitive skills, depression, and daily living activities in older people?
Bibliographic record
Abstract
Introduction: Old age is a period associated with health risks, leading to losses in various skills. Aim: This study aims to examine the verbal fluency and cognitive skills of older individuals, their levels of depression, and daily living activities as well as to identify the correlations between these and some other variables. Method: 77 participants (48 F; 29 M) aged 65 and over who had not received a diagnosis were administered a "ParticipantInformation Form", "Verbal Fluency Test", "Montreal Cognitive Assessment Turkish Version (MOBID)", "Geriatric Depression Scale (GDS)", and "Barthel Index of Basic Activities of Daily Living (ADL)". Statistical analysis of the research data was performed using IBM SPSS 24.0 software. Results: There was a moderate, significant positive correlation between verbal fluency scores and both MOBID (r = 0.542, r = 0.604, r = 0.343) and ADL scores (r = 0.365, r = 0.323, r = 0.254), whereas a moderate, significant negative correlation was found between verbal fluency scores and GDS scores (r = -0.551, r = -0.422, r = -0.493). s (Verbal fluency and MOBID scores showed significant differences across age groups, whereas ADL and depression scores did not; additionally, verbal fluency, MOBID, and GDS varied significantly by education and income levels while ADL scores remained unaffected; notably, only verbal fluency scores differed by gender, with female participants demonstrating higher performance. Conclusion: Significant correlations were observed between verbal fluency, cognition, depression, and daily living activity skills. As the cognitive and daily living activity skills of participants increase or their levels of depression decrease, their verbal fluency skills improve. The findings underscore the importance of supporting all variables and highlight the significance of interdisciplinary collaboration.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".