A machine learning approach to predict resilience and sickness absence in the healthcare workforce during the COVID-19 pandemic

Johannes Lieslehto; Noora Rantanen; Lotta-Maria A. H. Oksanen; Sampo A. Oksanen; Anne Kivimäki; Susanna Paju; Milla Pietiäinen; Laura Lahdentausta; Pirkko Pussinen; Veli-Jukka Anttila; Lasse Lehtonen; Tea Lallukka; Ahmed Geneid; Enni Sanmark

doi:10.1038/s41598-022-12107-6

A machine learning approach to predict resilience and sickness absence in the healthcare workforce during the COVID-19 pandemic

Johannes Lieslehto, Noora Rantanen, Lotta-Maria A. H. Oksanen, Sampo A. Oksanen, Anne Kivimäki, Susanna Paju, Milla Pietiäinen, Laura Lahdentausta, Pirkko Pussinen, Veli-Jukka Anttila, Lasse Lehtonen, Tea Lallukka, Ahmed Geneid, Enni Sanmark

Research output: Contribution to journal › Article › Scientific › peer-review

Original language	English
Article number	8055
Journal	Scientific Reports
Volume	12
Issue number	1
Number of pages	9
ISSN	2045-2322
DOIs	https://doi.org/10.1038/s41598-022-12107-6
Publication status	Published - 16 May 2022
MoE publication type	A1 Journal article-refereed

Fields of Science

MENTAL-HEALTH
BURNOUT
STRESS
3142 Public health care science, environmental and occupational health

Access to Document

10.1038/s41598-022-12107-6

s41598-022-12107-6Final published version, 3.5 MBLicence: CC BY

Cite this

Lieslehto, J., Rantanen, N., Oksanen, L.-M. A. H., Oksanen, S. A., Kivimäki, A., Paju, S., Pietiäinen, M., Lahdentausta, L., Pussinen, P., Anttila, V.-J., Lehtonen, L., Lallukka, T., Geneid, A., & Sanmark, E. (2022). A machine learning approach to predict resilience and sickness absence in the healthcare workforce during the COVID-19 pandemic. Scientific Reports, 12(1), Article 8055. https://doi.org/10.1038/s41598-022-12107-6

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author = "Johannes Lieslehto and Noora Rantanen and Oksanen, {Lotta-Maria A. H.} and Oksanen, {Sampo A.} and Anne Kivim{\"a}ki and Susanna Paju and Milla Pieti{\"a}inen and Laura Lahdentausta and Pirkko Pussinen and Veli-Jukka Anttila and Lasse Lehtonen and Tea Lallukka and Ahmed Geneid and Enni Sanmark",

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Lieslehto, J, Rantanen, N , Oksanen, L-MAH, Oksanen, SA, Kivimäki, A, Paju, S , Pietiäinen, M, Lahdentausta, L, Pussinen, P , Anttila, V-J , Lehtonen, L , Lallukka, T , Geneid, A & Sanmark, E 2022, 'A machine learning approach to predict resilience and sickness absence in the healthcare workforce during the COVID-19 pandemic', Scientific Reports, vol. 12, no. 1, 8055. https://doi.org/10.1038/s41598-022-12107-6

TY - JOUR

T1 - A machine learning approach to predict resilience and sickness absence in the healthcare workforce during the COVID-19 pandemic

AU - Lieslehto, Johannes

AU - Rantanen, Noora

AU - Oksanen, Lotta-Maria A. H.

AU - Oksanen, Sampo A.

AU - Kivimäki, Anne

AU - Paju, Susanna

AU - Pietiäinen, Milla

AU - Lahdentausta, Laura

AU - Pussinen, Pirkko

AU - Anttila, Veli-Jukka

AU - Lehtonen, Lasse

AU - Lallukka, Tea

AU - Geneid, Ahmed

AU - Sanmark, Enni

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KW - STRESS

KW - 3142 Public health care science, environmental and occupational health

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DO - 10.1038/s41598-022-12107-6

M3 - Article

SN - 2045-2322

VL - 12

JO - Scientific Reports

JF - Scientific Reports

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