RESILIENCE AND MENTAL HEALTH AMONG OLDER KOREANS: FOCUSING ON DEPRESSION AND MENTAL WELL-BEING
Bibliographic record
Abstract
With the development of positive psychology perspective in recent years, a number of research on mental health are targeting on examining both pathology and positive constructs such as mental well-being. However, this has not been well investigated among Korean older adults. Therefore, this study was aimed to examine the association between resilience and mental health (both negative and positive aspects) among individuals aged 65 and older in South Korea. The study sample was drawn from a community-based survey including 2,004 older adults. The dependent variables were measured with depression and mental well-being. Depression was measured by the Center for Epidemiological Studies-Dpression (CES-D) 10 items (Cronbach’s alpha= 0.874). Mental well-being was measured by the Korean version of the Mental Health Continuum-Short Form (K-MHC-SF; Cronbach’s alpha= 0.937). The independent variable, resilience, was measured with the Connor-Davidson Resilience Scale (C-DRS; Cronbach’s alpha= 0.948). Adjusted for age, gender, region, education, living arrangement, religion, employment, income, and self-rated health, resilience was negatively associated with depression among older adults (p<.001), while it was positively associated with mental well-being among Korean older adults (p<.001). The study findings suggest that resilience can promote mental health in later life. Implications for older adults suffering from mental health problems are also discussed.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".