RELATIONS OF REMOTE WORKING TO MENTAL HEALTH

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Nia Sarinastiti
Ario Bimo
Jeffrey Cole

Abstract

Since the pandemic, remote work is the only option to keep people’s productivity outside of the workplace. At first, people were happy to not be having to commute, spending more time at home and having extra hours to do interesting activities not related to work. But that did not last long. Approximately six months later indicate changes to mental states.  The aim of this research is to understand the relations of remote work during the Covid-19 pandemic to mental health and identify activities that help to bridge mental health issues.  Conceptual thinking of the research is based on social networks, social relations, lifestyle preference, social media hashtags.  Research method uses social network analytics based on Netlytic.org and Gephi tools to determine the depth of the relations.   The result states that remote work has relations to mental health, in which the wellbeing of individuals relates to the way they work, which hence alter the use of time to do other useful activities.

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How to Cite
Sarinastiti, N., Bimo, A., & Cole, J. (2022). RELATIONS OF REMOTE WORKING TO MENTAL HEALTH. ASPIRATION Journal, 2(2), 204–224. https://doi.org/10.56353/aspiration.v2i2.40

References

  1. Accenture. (2020, August). Covid-19 has changed the consumers: data driven insights into consumer behavior. Retrieved January 30, 2021, from Accenture.com: https://www.accenture.com/id-en/insights/retail/coronavirus-consumer-habits
  2. Arindita, R.; Muchammad N.; Lisa E.P.H.; Nursalsa A.; Shafiyya L. (2021). Construction of Mothers Identity in Online Community: Study of Members of Halo Ibu Community. ASPIRATIONS Journal Vol. 2 (1), July 2021, p. 1-21
  3. Berg, A. v. (2014). The story of the hashtag(#): A practical theological tracing of the hashtag(#) symbol on twitter. Theological Studies, 1-6.
  4. Blondel, V., Guillaume, J., Lambiotte, R., & Lefebvre, E. (2008). “Fast unfolding of communities in large networks. Journal of Statistical Mechanics: Theory and Experiment , vol 10, 1000.
  5. Castells, M. (, 2010 ). The Rise of the Network Society . West Sussex, UK: Wiley & Sons.
  6. Cole, J. (2021, October 13). The two kinds of workers after COVID. Retrieved November 20, 2021, from digitalcenter.org: https://www.digitalcenter.org/columns/two-types-ofworkers/
  7. Downes, S. (2005). Semantic Networks and Social Networks. The Learning Organization Vol. 12 No. 5, 411-417.
  8. Giuffre, K. (2013). Communities and Networks: Using Social Network Analysis to Rethink Urban and Community Studies. Malden, MA: Polity Press
  9. Grandjean, M. (2013, 07 01). Introduction to Network Visualization with GEPHI. Retrieved from martingrandjean.ch: http://www.martingrandjean.ch/introduction-to-networkvisualization-gephi/
  10. Hansen, D., Shneiderman, B., & Smith, M. (2011). Analyzing Social Media Networks with NodeXL. Burlington, MA: Morgan Kaufman.
  11. Lambiotte, R., Delvenne, J., & Barahona, M. (2015). Laplacian Dynamics and Multiscale Modular Structure in Networks. EE Transactions on Network Science and Engineering (Volume:1 , Issue: 2 ), 76-90.
  12. Lee, F. L., Liang, H., Cheng, E. W., Tang, G. K., & Yuen, S. (2021). Affordances, Movement Dynamics, and a Centralized Digital Communication Platform in a Networked Movement. Information, Communication & Society, https://doi.org/10.1080/1369118X.2021.1877772.
  13. Lee, F. S., Heimer, H., Giedd, J. N., Lein, E. .., Šestan, N., Weinberger, D. R., & Casey, B. J. (2014, October 31). Adolescent Mental Health - Opportunity and Obligation. sciencemag.org, Vol 346 Issue 6209, pp. 547-549.
  14. Lizardo, O., & Jilbert, I. (2020, 01 06). Social Networks: An Introduction. Retrieved from Bookdown.org: https://bookdown.org/omarlizardo/_main/1-3-the-two-faces-ofsocial-network-analysis.html
  15. Monge, P., & Contractor, N. (2003). Theories of Communication Networks. New York, NY: Oxford University Press.
  16. Panchal, N., Kamal, R., Orgera, K., Cox, C., Garfield, R., L. Hamel, L. M., & Chidambaram, P. (2021, Feb 10). The Implications of Covid-19 for Mental Health and Substance Use. Retrieved Feb 15, 2021, from https://www.kff.org: https://www.kff.org/coronaviruscovid-19/issue-brief/the-implications-of-covid-19-for-mental-health-and-substanceuse/
  17. Reuter, J., Pereira-Martins, J., & Kalita, J. (2016). Segmenting Twitter Hashtags. International Journal on Natural Language Computing (IJNLC) Vol. 5, No.4, August 2016, 23-36 DOI: 10.5121/ijnlc.2016.5402.
  18. Stuart, T. (2020, 07 03). Community Detection. Retrieved from https://timoast.github.io/: https://timoast.github.io/blog/community-detection/
  19. Willockx, Z. (2016). Social Relations Theory (Fiske). In T. S. T, & V. W.-S. (eds), Encyclopedia of Evolutionary Psychological Science (p. 91). Springer, Cham. Retrieved from [5]Z. Willockx, Social Relations Theory (Fiske). In: Weekes-Shackelford V., Shackelford T., Weekes-Shackelford V. (eds) Encyclopedia of Evolutionary Psychological Science. Springer, Cham. : https://doi.org/10.1007/978-3-319-16999-6_442-1
  20. Zappavigna, M. (2012). Discourse of Twitter and Social Media: How We Use Language to Create Affiliation on the Web. London: Continuum. Retrieved from www.bloomsbury.com: https://www.bloomsbury.com/us/discourse-of-twitterandsocial-media-9781441141866/