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The Emotional Job-Stress of COVID-19 on Nurses Working in Isolation Centres: A Machine Learning Approach

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Intelligent Systems Design and Applications (ISDA 2022)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 715))

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Abstract

Motivated by the present global coronavirus pandemic with its multiple mental health and adverse psychosocial effects on frontline nurses, enormous research on the ecology and epidemiology of COVID-19 has been carried out. However, there is the sparsity of work done in the psychosocial context and the associated mental health impact of COVID-19. This article provides an overview of the scientific evidence and predicts the emotional health impact of the disease. Qualitative and quantitative data were collected from 543 frontline nurses working in quarantine facilities and were analyzed using machine learning platforms. Two different classifiers (Multinomial Naïve Bayes algorithm and Support Vector Machine) were compared with three human coders for text analysis. The Multinomial Naïve Bayes algorithm was inferior to the Support Vector Machine though both performed better in predicting emotions than humans in this study. The result suggests an increase in levels of fear, anxiety, worry and sadness during the periods of quarantine. Sadness is the most profound emotional impact. The studies promote machine learning to be used to predict social phenomenon. Impliedly, developing a better and more robust digital psychiatry intervention model mechanism to support existing psychological first aid will positively impact on the emotional and mental health disorders.

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Author information

Authors and Affiliations

  1. Department of Health Administration and Education, University of Education Winneba, Kumasi, Ghana

    Richard Osei Agjei

  2. School of Business, JAMK University of Applied Sciences, Rajakatu 35, 40100, Jyväskylä, Finland

    Sunday Adewale Olaleye

  3. Institute of Public Health and Clinical Nutrition, University of Eastern Finland, Kuopio, Finland

    Frank Adusei-Mensah

  4. School of Pharmacy, University of Eastern, Kuopio, Finland

    Oluwafemi Samson Balogun

Authors
  1. Richard Osei Agjei
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  2. Sunday Adewale Olaleye
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  3. Frank Adusei-Mensah
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  4. Oluwafemi Samson Balogun
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Corresponding author

Correspondence to Richard Osei Agjei .

Editor information

Editors and Affiliations

  1. Faculty of Computing and Data Science, FLAME University, Pune, Maharashtra, India

    Ajith Abraham

  2. Center for Smart Computing Continuum, Burgenland, Austria

    Sabri Pllana

  3. University of Bari, Bari, Italy

    Gabriella Casalino

  4. University of Jinan, Jinan, Shandong, China

    Kun Ma

  5. Department of Computer Science and Engineering, Thapar Institute of Engineering and Technology, Patiala, Punjab, India

    Anu Bajaj

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Agjei, R.O., Olaleye, S.A., Adusei-Mensah, F., Balogun, O.S. (2023). The Emotional Job-Stress of COVID-19 on Nurses Working in Isolation Centres: A Machine Learning Approach. In: Abraham, A., Pllana, S., Casalino, G., Ma, K., Bajaj, A. (eds) Intelligent Systems Design and Applications. ISDA 2022. Lecture Notes in Networks and Systems, vol 715. Springer, Cham. https://doi.org/10.1007/978-3-031-35507-3_18

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