Facing an Aging Society: Taiwan’s Universities in crisis

Hu, Hsiao-Mei

doi:10.1080/02701960.2018.1428576

Cited by 10 publications

(7 citation statements)

References 1 publication

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“…Since then, the U3As have been spreading globally, developing different or adapted approaches from that originally put forward in France (Formosa, 2019). This is in accordance with Hu (2020), whose study highlights the importance of adjustments in the operational models of universities in order to meet the educational needs of older learners in progressively ageing societies.…”

Section: Introductionsupporting

confidence: 58%

Lifelong learning activities for older adults: a scoping review protocol

Flauzino

Gil

Batistoni

et al. 2021

RSD

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Objective: To map lifelong learning activities for the elderly to understand how this paradigm has been incorporated into practical actions. Methodology: A scoping review type survey will be conducted in the literature published in English in five databases between the period 1972 to 2020. The mnemonic PCC (Population, Concept and Context) will guide the search strategy, the selection and extraction process of the studies, which will be conducted by two authors independently. Peer-reviewed articles that present the conceptual notion of lifelong learning as the central theme of the study, the target audience of men and women aged 50 and over and the description of formal educational activities will be included for the analysis, non-formal and informal. The review will include articles with a qualitative, quantitative or mixed methodological approach. The evidence will be organized in a specific form containing bibliometric data and the main characteristics of the studies, as well as specific information to answer the objectives of this scoping review. Results: will be presented in descriptive synthesis accompanied by tables and diagrams.

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Section: Introductionsupporting

confidence: 58%

Lifelong learning activities for older adults: a scoping review protocol

Flauzino

Gil

Batistoni

et al. 2021

RSD

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“…In 2018, 14% of its people were in the aged population and this proportion will reach 20% by 2025, or equivalent to taking only 7 years to switch from an aged society to a super-aged society. Such rapid change causes large amounts of burdens, whether physical, psychological or social (4)(5)(6).…”

Section: Introductionmentioning

confidence: 99%

A model for predicting fall risks of hospitalized elderly in Taiwan-A machine learning approach based on both electronic health records and comprehensive geriatric assessment

et al. 2022

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BackgroundsFalls are currently one of the important safety issues of elderly inpatients. Falls can lead to their injury, reduced mobility and comorbidity. In hospitals, it may cause medical disputes and staff guilty feelings and anxiety. We aimed to predict fall risks among hospitalized elderly patients using an approach of artificial intelligence.Materials and methodsOur working hypothesis was that if hospitalized elderly patients have multiple risk factors, their incidence of falls is higher. Artificial intelligence was then used to predict the incidence of falls of these patients. We enrolled those elderly patients aged >65 years old and were admitted to the geriatric ward during 2018 and 2019, at a single medical center in central Taiwan. We collected 21 physiological and clinical data of these patients from their electronic health records (EHR) with their comprehensive geriatric assessment (CGA). Data included demographic information, vital signs, visual ability, hearing ability, previous medication, and activity of daily living. We separated data from a total of 1,101 patients into 3 datasets: (a) training dataset, (b) testing dataset and (c) validation dataset. To predict incidence of falls, we applied 6 models: (a) Deep neural network (DNN), (b) machine learning algorithm extreme Gradient Boosting (XGBoost), (c) Light Gradient Boosting Machine (LightGBM), (d) Random Forest, (e) Stochastic Gradient Descent (SGD) and (f) logistic regression.ResultsFrom modeling data of 1,101 elderly patients, we found that machine learning algorithm XGBoost, LightGBM, Random forest, SGD and logistic regression were successfully trained. Finally, machine learning algorithm XGBoost achieved 73.2% accuracy.ConclusionThis is the first machine-learning based study using both EHR and CGA to predict fall risks of elderly. Multiple risk factors of falls in hospitalized elderly patients can be put into a machine learning model to predict future falls for early planned actions. Future studies should be focused on the model fitting and accuracy of data analysis.

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“…When this proportion surpasses 14% in a society, it is regarded as an ‘aged society’. Accordingly, the prevalence of older adults ≥65 in Taiwan exceeded 14% in 2018 17. In the last decade, policies on health education by the Ministry of Health and Welfare in Taiwan18 have focused on older adults aged ≥65 years.…”

Section: Methodsmentioning

confidence: 99%

Effects of socioeconomic status on the control of hypertension in patients <65 and ≥65 years of age in Taiwan: a nationwide cross-sectional study

Lin

Kuo

et al. 2022

BMJ Open

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ObjectiveImplementation of National Health Insurance in 1995 improved the control of hypertension due to comprehensive coverage of medical costs for all residents in Taiwan. However, the role of individual socioeconomic status (iSES), namely, education and personal income, in awareness, treatmen, and control of blood pressure, has not been well studied in Taiwan.DesignA nationwide cross-sectional survey was executed in this study.SettingA systematic, multistage sampling scheme from a nationwide cohort in Taiwan was adopted to select adult participants. Hypertension was defined as blood pressure ≥ 140/90 mm Hg or the use of antihypertensive medications.ParticipantsA total of 4599 subjects completed door-to-door household interviews and physical examinations.Primary and secondary outcome measuresA summing z score of iSES was constructed by two domains of the iSES, namely, income and education, through which participants were classified into three SES levels. Logistic regression models were used to assess associations of awareness, treatment, and control in younger (<65 years) and older (≥65 years) participants with hypertension.ResultsYounger people in the highest tertile of iSES were less likely to be aware of their hypertension (OR: 0.48, 95% CI: 0.37 to 0.64, p<0.0001) or to have it treated (OR: 0.49, 95% CI 0.37 to 0.64, p<0.0001) but more likely to have their hypertension controlled (OR: 1.52, 95% CI 1.19 to 1.94, p=0.0009). We did not detect an association in people aged >65 years.ConclusionHealth education to improve awareness and treatment of hypertension should focus on younger people with higher iSES in Taiwan.

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Facing an Aging Society: Taiwan’s Universities in crisis

Cited by 10 publications

References 1 publication

Lifelong learning activities for older adults: a scoping review protocol

Lifelong learning activities for older adults: a scoping review protocol

A model for predicting fall risks of hospitalized elderly in Taiwan-A machine learning approach based on both electronic health records and comprehensive geriatric assessment

Effects of socioeconomic status on the control of hypertension in patients <65 and ≥65 years of age in Taiwan: a nationwide cross-sectional study

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