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. 2022 Feb 4;12:1932. doi: 10.1038/s41598-022-05331-7

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© The Author(s) 2022, corrected publication 2022

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

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Clustering analysis reveals three distinct student profiles. (a) Biplot shows both the maximal separation among the clusters, displayed as dots colored by cluster, and the original variables used in the analysis, shown as standardized basis vectors on the two dimensions. The length of the vectors represents standardized regression coefficients. The arrows for Interaction and Status Stress are almost fully overlapping, and the vector for Sleep Regularity is almost exactly at (0, 0). (b) Bar plots show between-person means of each clustering variable separately by cluster. Error bars represent standard errors of the mean. (c) Boxplots show participants’ cumulative grade point average (GPA) their first year of university (top) and mean Global Clinical symptom severity (middle). Asterisks represent statistically significant differences between cluster pairs (two-tailed t-tests; **p < 0.01, ***p < 0.001). Bottom bar plot shows the self-reported sex breakdown of the three clusters.