The influence of daily stress and resilience on successful ageing
Bibliographic record
Abstract
AIM: The aim of this study was to identify the effects of daily stress and resilience on successful ageing among community-dwelling older adults. BACKGROUND: Ageing can be a positive experience if there is good adaptation to ageing processes. Positive ageing needs to be a basis of nursing care, health promotion and education within community settings. METHODS: Data were collected in March and April of 2014 from 262 older adults living in Seoul and Jeju, South Korea. We used a four-part survey consisting of demographic data, daily stress, resilience and successful ageing scales, in total 91 items. Data were analysed using descriptive statistics, t-test, one-way ANOVA, Tukey HSD test, Pearson's correlation coefficient and hierarchical multiple regression analysis to identify the influence of variables on successful ageing. FINDINGS: Successful ageing had a significant negative correlation with daily stress and a positive correlation with resilience. Daily stress had a negative correlation with resilience. Findings of hierarchical multiple regression analysis indicated that resilience and subjective economic status had an effect on successful ageing. Furthermore, these variables accounted for 41.6% of the variance in successful ageing. LIMITATIONS: Data were collected in only two cities of Korea based on convenience sampling. CONCLUSION: The findings of the study suggest that daily stress and resilience have a statistically significant relationship with successful ageing. Furthermore, resilience is an important influential factor and a much-needed personal characteristic for one's successful ageing. IMPLICATIONS FOR NURSING AND HEALTH POLICIES: Nurses can advocate joining with health and social policy makers to implement policies on healthy ageing, including evaluation of stress, education programmes and implementation of self-help groups to enhance resilience in older people.
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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 | 0.005 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".