Using Machine Learning in Burnout Prediction: A Survey.
Accurate prediction provides a number of important benefits for research and decision-making. Occupational burnout is intertwined with individual, cultural, and social factors, the resolution of which requires methods that can deal with large amounts of data. The application of such methods capable...
| Publicado en: | Child & Adolescent Social Work Journal Vol. 38; no. 2; pp. 175 - 181 |
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| Formato: | Artículo |
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Springer Nature
Apr2021
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ssf&AN=149497625&site=ehost-live header: @attributes: shortDbName: ssf uiTerm: 149497625 longDbName: Social Sciences Full Text (H.W. Wilson) uiTag: AN controlInfo: bkinfo: jinfo: jid: 07380151 COK jtl: Child & Adolescent Social Work Journal issn: 07380151 maglogo: N pubinfo: dt: Apr2021 vid: 38 iid: 2 pid: 237 pub: Springer Nature artinfo: ui: 149497625 10.1007/s10560-020-00733-w ppf: 175 ppct: 6 formats: fmt: – @attributes: type: T – @attributes: type: P size: 568KB tig: atl: Using Machine Learning in Burnout Prediction: A Survey. aug: au: Grządzielewska, Małgorzata affil: Centre for Family Research, Nicolaus Copernicus University, ul. Lwowska 1, 87-100, Toruń, Poland su: Psychological burnout Psychometrics Forecasting Employees' workload Social case work Machine learning sug: subj: Psychological burnout Psychometrics Forecasting Employees' workload Social case work Machine learning keyword: Burnout Prediction Social work Supervised machine learning Burnout Prediction Social work Supervised machine learning ab: Accurate prediction provides a number of important benefits for research and decision-making. Occupational burnout is intertwined with individual, cultural, and social factors, the resolution of which requires methods that can deal with large amounts of data. The application of such methods capable of dealing with large datasets is a relatively novel research area in social science. For this purpose, this article presents insights into machine learning methods, mainly related to prediction tasks. A brief review of these techniques in burnout domain was applied. It is shown that the choice of a method depends on the presence of certain dependent variables. This paper also presents a comparison between novel and traditional approaches, which shows that the appropriateness of a technique depends on the aim of the research. The theoretical and practical implications of using machine learning methods in this context is also presented in the paper. It is found that a gap in the study of burnout exists which requires the attention of social work researchers. Through machine learning techniques, new theoretical models of burnout can be created. These algorithms can also provide new approaches to create data-driven interventions. Burnout monitoring systems supported by machine-learning algorithms can also be used in recruitment processes and to supervise employees. Applying machine learning methods in reducing burnout can also provide socio-economic benefits such as help to reduce employee turnover and improve general working conditions. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: N holdings: @attributes: islocal: N |
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