Lune, Jelmer van (2021) Effect of COVID-19 on Mental Well-being: an Unsupervised Learning Analysis. Bachelor's Thesis, Artificial Intelligence.
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Abstract
Just as seen during previous virus outbreaks, the COVID-19 pandemic can have a negative impact on the mental well-being of individuals. However, it is likely that not everyone responds mentally the same to the pandemic. In this thesis I investigated whether there are groups of people that respond different with respect to mental well-being to the pandemic, by performing unsupervised learning on a large-scale questionnaire study performed during the pandemic. Moreover, I examined what other factors differ between groups of individuals responding adaptively and maladaptively to the pandemic and how these groups evolved during the pandemic. Indeed, a K-Means clustering and to a lesser extent a Hierarchical Agglomerative clustering analysis indicated that there were two groups of people, one with a better average mental well-being, one with a worse average mental well-being. Other factors that differed between these group were age, gender, employment, financial worries, social contact, frequency of leaving the house, knowledge about the virus, confidence in government, being infected and knowing infected people. In both groups the mental well-being improved slightly as the pandemic progressed.
| Item Type: | Thesis (Bachelor's Thesis) |
|---|---|
| Supervisor name: | Vugt, M.K. van |
| Degree programme: | Artificial Intelligence |
| Thesis type: | Bachelor's Thesis |
| Language: | English |
| Date Deposited: | 02 Mar 2021 13:38 |
| Last Modified: | 02 Mar 2021 13:38 |
| URI: | https://fse.studenttheses.ub.rug.nl/id/eprint/24023 |
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