Influencing Variables on Life Satisfaction of Korean Elders in Institutions
Bibliographic record
Abstract
PURPOSE: The number of elders in institutions has increased as family supporting systems have changed in Korea. The purpose of this study were to understand the life satisfaction among elders in institutions and to identify the factors influencing on life satisfaction. METHODS: The instruments used were Yun(1982)'s scale modified Memorial University of Newfoundland Scale for Happiness(MUNSH) in life satisfaction, ADL and IADL in activity level, Self-rating Depression Scale(SDS) in depression and Norbeck Social Support Questionnaire(NSSQ) scale in social support. Also, Perceived health status was measured by Visual Graphic Rating Scale. The subject of this study is 107 cognitively intact and ambulatory elders in 7 institutions in Daegu city and Kyungpook province. The data have been collected from May 1 to June 30, 2001. For the analysis of collected data, frequency analysis, mean, standard deviation, Pearson's correlation and stepwise multiple regression analysis were used for statistical analysis by SPSSwin(version 9.0) program. RESULTS: Life satisfaction for the elders in institutions showed negative correlation with SDS, and positive correlation with activity level. The regression form of the stepwise multiple regression analysis to investigate the influencing factors of life satisfaction for the elders in institutions was expressed by y = 90.988-0.733x1-0.188x2-0.069x3-0.565x4 (x1: SDS x2: Social support x3: Activity level x4: Monthly pocket Money) and 57.9% of variance in life satisfaction was explained by the model. CONCLUSION: The factors influencing on life satisfaction among the elders in institutions were SDS, social support, activity level and monthly pocket money. According to the results of this study, depression, social support and activity level are considered the prime causal factors for life satisfaction.
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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 | 0.002 |
| 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".