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Enregistrement W4405090196 · doi:10.1097/gh9.0000000000000517

Aging population in South Korea: burden or opportunity?

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

Notice bibliographique

RevueInternational Journal of Surgery Global Health · 2024
Typearticle
Langueen
DomaineSocial Sciences
ThématiqueRetirement, Disability, and Employment
Établissements canadiensUniversity of Toronto
Organismes subventionnairesnon disponible
Mots-clésPopulation ageingPopulationGeographyDemographySociology

Résumé

récupéré en direct d'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.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,003
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: Observationnel
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,155
Score d'incertitude au seuil0,996

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0030,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,317
Tête enseignante GPT0,508
Écart entre enseignants0,191 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations2
Publié2024
Routes d'admission1
Résumé présentoui

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