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Record W2978877713 · doi:10.1093/geroni/igy031.3676

RESILIENCE AND MENTAL HEALTH AMONG OLDER KOREANS: FOCUSING ON DEPRESSION AND MENTAL WELL-BEING

2018· article· en· W2978877713 on OpenAlexaff
Choi Woong Yong, Jing Lyu

Bibliographic record

VenueInnovation in Aging · 2018
Typearticle
Languageen
FieldHealth Professions
TopicHealth and Wellbeing Research
Canadian institutionsInstitute of Aging
Fundersnot available
KeywordsMental healthDepression (economics)Resilience (materials science)PsychologyPsychological resiliencePsychiatryClinical psychologyGerontologyMedicinePsychotherapist

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.031
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.030
GPT teacher head0.428
Teacher spread0.398 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations2
Published2018
Admission routes1
Has abstractyes

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