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Detecting (coronavirus-related) problems among counsellors of the emergency number 113 Suicide Prevention before and after the outbreak of the coronavirus pandemic in the Netherlands using state-of-the-art Natural Language Processing (NLP)

Grant number: 1.043E+13

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Key facts

  • Disease

    COVID-19

  • Start & end year

    2020
    2020

  • Funder

    NWO Netherlands

  • Principal Investigator

    Dr. Renske Gilissen

  • Research Location

    Netherlands

  • Lead Research Institution

    CWI

  • Research Priority Alignment

    N/A

  • Research Category

    Secondary impacts of disease, response & control measures

  • Research Subcategory

    Indirect health impacts

  • Special Interest Tags

    N/A

  • Study Type

    Non-Clinical

  • Clinical Trial Details

    N/A

  • Broad Policy Alignment

    Pending

  • Age Group

    Adults (18 and older)Older adults (65 and older)

  • Vulnerable Population

    Vulnerable populations unspecified

  • Occupations of Interest

    Unspecified

Abstract

This research makes use of a machine-learning technique, natural language processing, in which the text data from each conversation about (corona-related) problems stated by counsellors from 113 Suicide Prevention can be detected. The research will look specifically for changes in the problems stated over the course of time (before and after the beginning of the coronavirus crisis) and specific target groups (young/old, men/women, living alone/living with more people in a household). This research will therefore provide more insights into corona-related problems among very vulnerable groups.