IDEAS home Printed from https://ideas.repec.org/a/eee/socmed/v317y2023ics0277953622008966.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0277953622008966
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.socscimed.2022.115590?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Seppälä, Piia & Olin, Nina & Kalavainen, Susanna & Clottes Heikkilä, Heli & Kivimäki, Mika & Remes, Jouko & Ervasti, Jenni, 2023. "Effectiveness of a workshop-based intervention to reduce bullying and violence at work: A 2-year quasi-experimental intervention study," Social Science & Medicine, Elsevier, vol. 338(C).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Menteş, Nurettin & Çakmak, Mehmet Aziz & Kurt, Mehmet Emin, 2023. "Estimation of service length with the machine learning algorithms and neural networks for patients who receiving home health care," Evaluation and Program Planning, Elsevier, vol. 100(C).
    2. Sanchez Rico, Marina & Plessz, Marie & Airagnes, Guillaume & Wiernik, Emmanuel & Hoertel, Nicolas & Goldberg, Marcel & Zins, Marie & Meneton, Pierre, 2024. "Lifetime exposure to unemployment and prior working conditions are associated with retiree's health: A retrospective study in a large population-based French cohort," Social Science & Medicine, Elsevier, vol. 341(C).
    3. Victor Olsavszky & Mihnea Dosius & Cristian Vladescu & Johannes Benecke, 2020. "Time Series Analysis and Forecasting with Automated Machine Learning on a National ICD-10 Database," IJERPH, MDPI, vol. 17(14), pages 1-17, July.
    4. Shelda Sajeev & Stephanie Champion & Alline Beleigoli & Derek Chew & Richard L. Reed & Dianna J. Magliano & Jonathan E. Shaw & Roger L. Milne & Sarah Appleton & Tiffany K. Gill & Anthony Maeder, 2021. "Predicting Australian Adults at High Risk of Cardiovascular Disease Mortality Using Standard Risk Factors and Machine Learning," IJERPH, MDPI, vol. 18(6), pages 1-14, March.
    5. Linda L Magnusson Hanson & Paraskevi Peristera & Holendro Singh Chungkham & Hugo Westerlund, 2016. "Longitudinal Mediation Modeling of Unhealthy Behaviors as Mediators between Workplace Demands/Support and Depressive Symptoms," PLOS ONE, Public Library of Science, vol. 11(12), pages 1-14, December.
    6. Solja T Nyberg & Eleonor I Fransson & Katriina Heikkilä & Lars Alfredsson & Annalisa Casini & Els Clays & Dirk De Bacquer & Nico Dragano & Raimund Erbel & Jane E Ferrie & Mark Hamer & Karl-Heinz Jöcke, 2013. "Job Strain and Cardiovascular Disease Risk Factors: Meta-Analysis of Individual-Participant Data from 47,000 Men and Women," PLOS ONE, Public Library of Science, vol. 8(6), pages 1-6, June.
    7. Mira Kim & Kyunghee Chae & Seungwoo Lee & Hong-Jun Jang & Sukil Kim, 2020. "Automated Classification of Online Sources for Infectious Disease Occurrences Using Machine-Learning-Based Natural Language Processing Approaches," IJERPH, MDPI, vol. 17(24), pages 1-13, December.
    8. Katriina Heikkilä & Ida E H Madsen & Solja T Nyberg & Eleonor I Fransson & Kirsi Ahola & Lars Alfredsson & Jakob B Bjorner & Marianne Borritz & Hermann Burr & Nico Dragano & Jane E Ferrie & Anders Knu, 2014. "Job Strain and the Risk of Inflammatory Bowel Diseases: Individual-Participant Meta-Analysis of 95 000 Men and Women," PLOS ONE, Public Library of Science, vol. 9(2), pages 1-7, February.
    9. Bruning, Patrick F. & Hsin-Chen Lin, & Hsu, Ching-Yi, 2022. "Crafting solutions to leadership demands for well-being and effectiveness," Business Horizons, Elsevier, vol. 65(5), pages 603-615.
    10. Jooss, Stefan & Conroy, Kieran M. & McDonnell, Anthony, 2022. "From travel to virtual work: The transitional experiences of global workers during Covid-19," International Business Review, Elsevier, vol. 31(6).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:socmed:v:317:y:2023:i:c:s0277953622008966. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/wps/find/journaldescription.cws_home/315/description#description .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.