Grounded in psychological and social constructs, the need for privacy is reflected in human socio-spatial behaviour and in our own home. To discuss housing privacy, this article presents a systematic literature review (SLR) that identified theoretical and methodological aspects relevant to the topic. The research was based on consolidated protocols to identify, select and evaluate articles published between 2000 and 2021 in three databases (Web of Science, Google Scholar and Scielo), with 71 eligible articles identified for synthesis. The results showed a concentration of studies in the American, European and Islamic context, and the increase in this production since 2018. This was guided by the inadequacy of architectural and urban planning projects, by new forms of social interaction and, recently, by the COVID-19 pandemic. From a theoretical point of view, the SLR demonstrated the importance of investigating privacy in housing from a comprehensive perspective, observing its different dimensions (physical, social and psychological) and characterizing the issues involved and the context under analysis. Methodologically, the main instruments identified were: (i) to behavioural analysis, questionnaires, interviews and observations; (ii) to built environment evaluation, in addition to the previous ones, space syntax analysis, architectural design and photographs analysis; (iii) for the general characterization of users, the data collection regarding the socio-demographic and cultural context and the meanings attributed to spatial organizations; (iv) to characterize the participants of the investigations, the analysis of personality traits, the ways to personalize the space, user satisfaction/preferences and the influence of social interactions on these perceptions.
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http://dx.doi.org/10.1007/s10901-022-09939-z | DOI Listing |
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Department of Health and Nursing Science, Faculty of Health and Sport Sciences, University of Agder, Kristiansand, Norway.
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Department of Research and Innovation, Medway NHS Foundation Trust, Gillingham ME7 5NY, United Kingdom; Faculty of Medicine, Health and Social Care, Canterbury Christ Church University, United Kingdom.
This narrative review examines the transformative role of Artificial Intelligence (AI) and Machine Learning (ML) in organ retrieval and transplantation. AI and ML technologies enhance donor-recipient matching by integrating and analyzing complex datasets encompassing clinical, genetic, and demographic information, leading to more precise organ allocation and improved transplant success rates. In surgical planning, AI-driven image analysis automates organ segmentation, identifies critical anatomical features, and predicts surgical outcomes, aiding pre-operative planning and reducing intraoperative risks.
View Article and Find Full Text PDFAnat Sci Educ
January 2025
Medical School, Faculty of Medicine, University of Queensland, Brisbane, Australia.
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View Article and Find Full Text PDFInt J Environ Res Public Health
November 2024
International Centre for Future Health Systems (ICFHS), Faculty of Medicine and Health, University of New South Wales (UNSW), Sydney, NSW 2052, Australia.
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January 2025
Section of Infectious Diseases, Yale School of Medicine, New Haven, Connecticut, USA.
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