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Record W3022758142 · doi:10.1177/0733464820917295

Mental Health Among the Korean Older Population: How is it Related to Asset-based Welfare?

2020· article· en· W3022758142 on OpenAlexaff
Gum‐Ryeong Park, Bo Kyong Seo

Bibliographic record

VenueJournal of Applied Gerontology · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicHealth disparities and outcomes
Canadian institutionsMcMaster University
Fundersnot available
KeywordsMental healthDepression (economics)WelfareHousing tenureGerontologyLogistic regressionMediationPsychologyLife satisfactionHousehold incomePopulationMedicineEnvironmental healthDemographic economicsPsychiatryEconomicsGeographyPolitical science

Abstract

fetched live from OpenAlex

Objective: This article aims to investigate how older adults’ income and housing tenure are concurrently associated with depression in Korea. Methods: Using the 2017 Survey of Living Conditions and Welfare Needs of Korean Older Persons, logistic regression was implemented to examine the association of housing tenure and income status with depression among 6,624 older adults. Also, the older adults’ satisfaction with their economic conditions was added to examine its mediation effects on this association. Results: Homeownership lowered the likelihood of depression, whereas lower income increased the likelihood of depression among older adults. Also, lack of income security was associated with depression even among older homeowners, being partially mediated through their satisfaction with economic conditions. Conclusion: This study contributes to articulating the mechanisms linking housing tenure, income insecurity, and mental health of the older population in Korea. Future studies are needed to investigate the social determinants of health among older adults.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.781
Threshold uncertainty score0.662

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.040
GPT teacher head0.351
Teacher spread0.310 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
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

Citations16
Published2020
Admission routes1
Has abstractyes

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