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Record W4390425963 · doi:10.4235/agmr.23.0199

Geriatric Medicine in South Korea: A Stagnant Reality amidst an Aging Population

2023· article· en· W4390425963 on OpenAlexaboutno aff
Sunghwan Ji, Hee‐Won Jung, Ji Yeon Baek, Il‐Young Jang, Eun‐Ju Lee

Bibliographic record

VenueAnnals of Geriatric Medicine and Research · 2023
Typearticle
Languageen
FieldMedicine
TopicChronic Disease Management Strategies
Canadian institutionsnot available
Fundersnot available
KeywordsPopulation ageingGovernment (linguistics)GeriatricsHealth careMedicineHealthcare systemComorbidityPopulationGerontologyEconomic growthPsychiatryEnvironmental health

Abstract

fetched live from OpenAlex

In the face of an ever-increasing wave of an aging population, this paper provides an update on the current status of geriatric medicine in Korea, comparing it with global initiatives and suggesting future directions. Older adults require a multifaceted approach, addressing not only comorbidity management but also unmet complex medical needs, nutrition, and exercise to prevent functional decline. In this regard, the World Health Organization's Integrated Care for Older People guidelines underscore the importance of patient-centered primary care in preventing a decline in intrinsic capacity. Despite these societal needs and the ongoing aging process, the healthcare system in Korea has yet to show significant movement or a shift toward geriatric medicine, further complicated by the absence of a primary care system. We further explore global efforts in establishing age-integrative patient-centered medical systems in Singapore, Australia, Canada, the United Kingdom, and Japan. Additionally, we review the unmet needs and social issues that Korean society is currently facing, and local efforts by both government and a private tertiary hospital in Korea. In conclusion, considering the current situation, we propose that the framework of geriatric medicine should form the foundation of the future healthcare system.

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)0.009
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.544
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0090.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0030.005
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.321
GPT teacher head0.502
Teacher spread0.181 · 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 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

Citations11
Published2023
Admission routes1
Has abstractyes

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