Art, meditation and cognitive science: a framework for museum-based community meditation programme during the COVID-19 pandemic in Singapore.


Journal

Arts & health
ISSN: 1753-3023
Titre abrégé: Arts Health
Pays: England
ID NLM: 101476585

Informations de publication

Date de publication:
Oct 2024
Historique:
medline: 19 9 2024
pubmed: 19 9 2024
entrez: 19 9 2024
Statut: ppublish

Résumé

In 2020, the COVID-19 pandemic caused mandatory lockdowns worldwide, exacerbating mental health issues created by social isolation. Aimed to improve mental health and maintain engagement, the Singapore Art Museum (SAM) rapidly deployed an online programme called Unwind with SAM to engage the public with the museum's contemporary art collection coupled with meditation. The programme focused on the positive effect of arts on mental health, combined with the cognitive benefits of meditation by 'slow-looking' at artworks. This practice-based report reflects on the processes and explores the potential between art, contemplation and cognitive sciences. The first section outlines the current landscape of art and wellness approaches in museums. The second section explains the philosophical framework that guides the programme, alongside a breakdown of components and design rationale. The final part provides a critical reflection and concludes with recommendations for practitioners, managers and scientists to utilise the framework for designing future programmes.

Identifiants

pubmed: 39295423
doi: 10.1080/17533015.2023.2281911
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

358-371

Auteurs

Russell Weili Chan (RW)

Behavioural, Management and Social Sciences, Department of Learning, Data analytics and Technology, Section Cognition, Data and Education, University of Twente, Enschede, the Netherlands.

Sharon Peiling Chen (SP)

Accessibility Programmes, Singapore Art Museum, Singapore, Singapore.

Dee Yu Teng Chia (DYT)

Department of Sociology, University of Cambridge, Cambridge, UK.

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Classifications MeSH