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Prediction of bullying at work: A data-driven analysis of the Finnish public sector cohort study

Author

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  • Ervasti, Jenni
  • Pentti, Jaana
  • Seppälä, Piia
  • Ropponen, Annina
  • Virtanen, Marianna
  • Elovainio, Marko
  • Chandola, Tarani
  • Kivimäki, Mika
  • Airaksinen, Jaakko

Abstract

To determine the extent to which change in (i.e., start and end of) workplace bullying can be predicted by employee responses to standard workplace surveys.

Suggested Citation

  • Ervasti, Jenni & Pentti, Jaana & Seppälä, Piia & Ropponen, Annina & Virtanen, Marianna & Elovainio, Marko & Chandola, Tarani & Kivimäki, Mika & Airaksinen, Jaakko, 2023. "Prediction of bullying at work: A data-driven analysis of the Finnish public sector cohort study," Social Science & Medicine, Elsevier, vol. 317(C).
  • Handle: RePEc:eee:socmed:v:317:y:2023:i:c:s0277953622008966
    DOI: 10.1016/j.socscimed.2022.115590
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    1. Katriina Heikkilä & Solja T Nyberg & Eleonor I Fransson & Lars Alfredsson & Dirk De Bacquer & Jakob B Bjorner & Sébastien Bonenfant & Marianne Borritz & Hermann Burr & Els Clays & Annalisa Casini & Ni, 2012. "Job Strain and Tobacco Smoking: An Individual-Participant Data Meta-Analysis of 166 130 Adults in 15 European Studies," PLOS ONE, Public Library of Science, vol. 7(7), pages 1-7, July.
    2. Yuchen Chen & Yuhong Yang, 2021. "The One Standard Error Rule for Model Selection: Does It Work?," Stats, MDPI, vol. 4(4), pages 1-25, November.
    3. Lotta K. Harju & Janne Kaltiainen & Jari J. Hakanen, 2021. "The double‐edged sword of job crafting : The effects of job crafting on changes in job demands and employee well‐being," Post-Print hal-03188199, HAL.
    4. Gigi F Stark & Gregory R Hart & Bradley J Nartowt & Jun Deng, 2019. "Predicting breast cancer risk using personal health data and machine learning models," PLOS ONE, Public Library of Science, vol. 14(12), pages 1-17, December.
    5. Ahmed M Alaa & Thomas Bolton & Emanuele Di Angelantonio & James H F Rudd & Mihaela van der Schaar, 2019. "Cardiovascular disease risk prediction using automated machine learning: A prospective study of 423,604 UK Biobank participants," PLOS ONE, Public Library of Science, vol. 14(5), pages 1-17, May.
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