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Record W4405090196 · doi:10.1097/gh9.0000000000000517

Aging population in South Korea: burden or opportunity?

2024· article· en· W4405090196 on OpenAlexaff
Bibek Giri, Vijay Kumar Chattu

Bibliographic record

VenueInternational Journal of Surgery Global Health · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicRetirement, Disability, and Employment
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsPopulation ageingPopulationGeographyDemographySociology

Abstract

fetched live from OpenAlex

The aging of the population in Korea has reached an unparalleled level. No other country has experienced such a rapid and profound demographic shift as Korea, where the proportion of older people has increased dramatically in recent decades[1]. Among Organization for Economic Cooperation and Development (OECD) countries, Korea has the highest rate of population aging. The proportion of older people, who are 65 and over, surpassed 14% in 2018, making South Korea an “aged society.” This is the criterion for eligibility for various government programs, such as pension and long-term care services. The aging process has not slowed down since then. In fact, by 2025, Korea is projected to become a “super-aged society”, with more than one-fifth of its population being 65 and over. This is due to the aging of the first baby boomer cohort (born between 1955 and 1963), who reached 65 in 2020[2]. According to the report in statistics Korea, this share is expected to increase rapidly reaching 12.98 million in 2023, 17.22 million in 2040 and 43.9% of the population by 2050[3]. Furthermore, the country’s population is projected to drop by 27% from 2020 to 2070, reaching 37.65 million. The report also indicated that the share of people aged 65 and over will increase from 17.5% to 46.4% in the same period. This is one of the most rapid and extreme aging trends in the world[4]. The main factors behind this demographic change are low birth rates and high life expectancy, which have led to a declining and aging population. The low fertility rate in South Korea is influenced by various factors, such as the expensive living and education costs, the insufficient support for childcare and parental leave, the unequal and unfair treatment of women in the labor market, and the shifting attitudes and choices of the younger generation. These factors discourage many people from getting married and having children, resulting in a declining and ageing population. In 2021, the average life expectancy at birth was 83.3 years, ranking fourth in the world[3]. The main reason for the increase in life expectancy is the progress in medical care, public health, and living standards. However, this does not imply that older people are healthier. Many of them face chronic diseases, disabilities, and cognitive impairments, which need long-term care and medical services. The aging population will increase the demand for health care and social welfare, creating fiscal and health system challenges. Moreover, the aging population affects the labor market and the economic growth in South Korea. The working-age population (15–64 years old) decreases, causing the labor force participation rate and the productivity to drop, which slows down the economic output. The dependency ratio, which shows the number of dependents (children and elderly) for every 100 working-age people, is expected to rise from 38.2 in 2020 to 97.5 in 2050[5]. This implies that more dependents will rely on fewer workers, lowering the savings and consumption rates. The aging population in South Korea is not only a burden, but an opportunity for positive change in its society and economy. The aging population can provide human capital, as older people have knowledge, skills, and experience that can be applied in various sectors. The government can support the active ageing of older people by giving them more chances for education, training, employment, and entrepreneurship[6]. For instant, the government can raise the mandatory retirement age, offer flexible work options, and fund lifelong learning programs for older workers. The government can also encourage the social involvement and integration of older people by making more spaces and platforms for them to do volunteer work, community service, and cultural activities. These actions can improve the well-being, dignity, and empowerment of older people, and strengthen social cohesion and intergenerational solidarity. Moreover, the aging population can stimulate innovation and growth, as it creates new needs and markets for products and services that suit the needs and preferences of older consumers. The government can back the growth of the silver industry, which includes various sectors such as health care, tourism, leisure, education, finance, and technology. The government can also boost the innovation ecosystem for the silver industry by giving incentives, funding, and infrastructure for research and development, start-ups, and social enterprises that focus on solving the problems and enhancing the quality of life of older people[7]. For instance, the government can invest in the development and diffusion of smart technologies, such as artificial intelligence, big data, and the internet of things, that can help older people in their daily activities, health management, and social interaction. In conclusion, the aging population in South Korea is a complex and multifaceted phenomenon that has both challenges and opportunities for the country. The government needs to take a comprehensive and proactive approach to deal with the issues and use the potentials of the aging population. The government needs to implement policies and strategies that can balance the fiscal sustainability and the social equity, that can improve the productivity and the well-being of older people, and that can foster the innovation and the cooperation among different stakeholders. The aging population is not a problem to be solved, but a reality to be accepted and an opportunity to be taken.

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)0.003
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.155
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.317
GPT teacher head0.508
Teacher spread0.191 · 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".

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Citations2
Published2024
Admission routes1
Has abstractyes

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