Utilizing regression model to characterize the impact of urban green space features on the subjective well-being of older adults
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PROVIDER: S-EPMC11336737 | biostudies-literature | 2024 Aug
REPOSITORIES: biostudies-literature
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Project description:A growing number of articles have identified and reported the benefits and importance of urban green spaces for improving human well-being, but there is a significant knowledge gap regarding the impact of urban green spaces on the subjective well-being of older adults. The literature search (August 2015-August 2022) was derived from two major scientific databases, Google Scholar, and Web of Science. As a result, 2558 articles were found, 1527 of which were retrieved from WOS and the rest from Google Scholar. Bibliometric methods and VOSviewer software were used to screen and organize the articles in the relevant fields. Finally, 65 articles met the review criteria. The included studies aim to capture the benefits of various features of urban green spaces in meeting or enhancing the subjective well-being needs of older adults. The results of our review further support the existence of a strong link between older adults' subjective well-being and various features of urban green spaces, providing new insights for future in-depth reexamination and policy development. Furthermore, the relationship between urban green spaces and older adults' subjective well-being depends not only on the urban green spaces themselves but also on the characteristics of the older adult population that uses them.
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Project description:Business leaders and policymakers within service economies are placing greater emphasis on well-being, given the role of workers in such settings. Whilst people's well-being can lead to economic growth, it can also have the opposite effect if overlooked. Therefore, enhancing subjective well-being (SWB) is pertinent for all organisations for the sustainable development of an economy. While health conditions were previously deemed the most reliable predictors, the availability of data on people's personal lifestyles now offers a new dimension into well-being for organisations. Using open data available from the national Annual Population Survey in the UK, which measures SWB, this research uncovered that among several independent variables to predict varying levels of people's perceived well-being, long-term health conditions, one's marital status, and age played a key role in SWB. The proposed model provides the key indicators of measuring SWB for organisations using big data.
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