Study of Character Human Strengths and Cognitive Impairment in Relation to Activity Status and Gender of Aged People
Bibliographic record
Abstract
The present study aimed to study the effect of activity status and gender on character human strengths and cognitive impairment. It also aimed to explore the relationship between character human strengths and cognitive impairment among aged people. Sample consisted of 100 individuals (51 male and 49 females), age ranged between 60-83 years. All the participants belonged to North-Eastern Uttar Pradesh, living with their families. Purposive Sampling was utilized for the selection of sample. Character human strengths was measured using VIA 72 and mild cognitive impairment (MCI) was measured using two measures namely, Montreal Cognitive Assessment (Hindi) (H-MoCA) and Addenbrooke’s Cognitive Examination (ACE III). The findings suggest that practicing professionals are more virtuous and have lower cognitive impairment than those who belong to retired and active and retired and inactive group. Further, the retired and inactive group and the females are more vulnerable to cognitive impairment. Moreover, character human strengths was correlated with cognitive impairment. The attained knowledge has counseling implications. There is a need to encourage aged individuals to cultivate character strengths through structured programs, community engagement, and cognitive training to support cognitive health. Additionally, promoting regular physical and intellectual activities through public health initiatives and social programs would help delay cognitive decline and enhance character human strengths. Policymakers should design initiatives that facilitate lifelong learning, social participation, physical fitness, and mental well-being among aged individuals in order to delay cognitive impairment.
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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 | 0.001 | 0.001 |
| 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) | 0.002 | 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".