Factors influencing Successful Aging among Older Adults
Bibliographic record
Abstract
BACKGROUND: The rapid growth of older population has enhanced the number of people at lifetime risk of enduring from chronic diseases. Successful aging is a significant phenomenon for achieving a healthy and happy life for all elderly and aging society. METHODS: A descriptive cross sectional study was carried out with the objective of assessing the factors influencing successful aging. Structured interview questionnaire using Successful aging scale and Self-esteem scale was used for data collection and obtained data was analyzed using descriptive statistics and inferential statistics at 0.05 level of significance. Participants of the research were considered in this study using purposive sampling technique. RESULTS: The findings revealed that majority of elderly respondents (73.8%) had successful aging. Successful aging was significantly associated with marital status (p-value= 0.040), family type (p-value=0.002), family annual income (p-value=0.009), presence of children as support system (p-value=0.034), negative life events in last 12 months (p-value<0.001), subjective perception of health (p-value=0.001) and ability to remember things without difficulties (p-value<0.001). CONCLUSIONS: Thus, it can be concluded that successful aging is associated with several factors. So, individual factors must be taken into consideration while implementing intervention programs in order to bring about positive aging experience among elderly.
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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 | 0.003 |
| 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) | 0.002 | 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".