Developing a Machine Learning Model for Detecting Job Burnout During the COVID-19 Pandemic Among Front-line Workers in Kuwait
| Main Author: | Waheeda Almayyan |
|---|---|
| Format: | Article Journal |
| Bahasa: | eng |
| Terbitan: |
, 2021
|
| Subjects: | |
| Online Access: |
https://zenodo.org/record/5668889 |
| ctrlnum |
5668889 |
|---|---|
| fullrecord |
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<dc schemaLocation="http://www.openarchives.org/OAI/2.0/oai_dc/ http://www.openarchives.org/OAI/2.0/oai_dc.xsd"><creator>Waheeda Almayyan</creator><date>2021-11-10</date><description>During the COVID-19 pandemic, front-line personnel worldwide had tremendous psychological stress
compared with the general population. High mental stress may lead to job burnout. This paper starts with gathering a job
burnout dataset from medical staff and police officers working in Kuwait during the COVID-19 pandemic using a webbased
Arabic version of the Maslach Burnout Inventory questionnaire. The gathered dataset shows that there an elevated
burnout rates among the front-line personnel dealing with COVID-19 patients. It utilizes machine learning techniques
including AnDE Bayesian, JChaid* decision tree, SVM margin-based, ForestPA Decision forest, and DMLP to predict the
presence of job burnout. Then, we present efficient feature subset selection approaches using several metaheuristic
methods such as Bat, Cuckoo, PSO, GWO, and CGWO to select the most competent features. Experiments showed that
reducing the number of features allows for a better understanding of the underlying model used to make predictions.
Results also support the adaptation of deep learning architectures in social sciences when data is relatively small. These
results may considerably help to screen out the front-line workers at high risk for job burnout.
Keywords— Job Burnout; COVID-19; Chaotic Grey Wolf Optimization; Machine Learning; Data Mining.</description><identifier>https://zenodo.org/record/5668889</identifier><identifier>10.5281/zenodo.5668889</identifier><identifier>oai:zenodo.org:5668889</identifier><language>eng</language><relation>doi:10.5281/zenodo.5668888</relation><rights>info:eu-repo/semantics/openAccess</rights><rights>https://creativecommons.org/licenses/by/4.0/legalcode</rights><subject>Security</subject><subject>Covid-19</subject><subject>Machine Learning</subject><subject>Data mining</subject><subject>Information Systems</subject><subject>Information Technology</subject><subject>Information Communication Technology</subject><title>Developing a Machine Learning Model for Detecting Job Burnout During the COVID-19 Pandemic Among Front-line Workers in Kuwait</title><type>Journal:Article</type><type>Journal:Article</type><recordID>5668889</recordID></dc>
|
| language |
eng |
| format |
Journal:Article Journal Journal:Journal |
| author |
Waheeda Almayyan |
| title |
Developing a Machine Learning Model for Detecting Job Burnout During the COVID-19 Pandemic Among Front-line Workers in Kuwait |
| publishDate |
2021 |
| topic |
Security Covid-19 Machine Learning Data mining Information Systems Information Technology Information Communication Technology |
| url |
https://zenodo.org/record/5668889 |
| contents |
During the COVID-19 pandemic, front-line personnel worldwide had tremendous psychological stress
compared with the general population. High mental stress may lead to job burnout. This paper starts with gathering a job
burnout dataset from medical staff and police officers working in Kuwait during the COVID-19 pandemic using a webbased
Arabic version of the Maslach Burnout Inventory questionnaire. The gathered dataset shows that there an elevated
burnout rates among the front-line personnel dealing with COVID-19 patients. It utilizes machine learning techniques
including AnDE Bayesian, JChaid* decision tree, SVM margin-based, ForestPA Decision forest, and DMLP to predict the
presence of job burnout. Then, we present efficient feature subset selection approaches using several metaheuristic
methods such as Bat, Cuckoo, PSO, GWO, and CGWO to select the most competent features. Experiments showed that
reducing the number of features allows for a better understanding of the underlying model used to make predictions.
Results also support the adaptation of deep learning architectures in social sciences when data is relatively small. These
results may considerably help to screen out the front-line workers at high risk for job burnout.
Keywords— Job Burnout; COVID-19; Chaotic Grey Wolf Optimization; Machine Learning; Data Mining. |
| id |
IOS16997.5668889 |
| institution |
ZAIN Publications |
| institution_id |
7213 |
| institution_type |
library:special library |
| library |
Cognizance Journal of Multidisciplinary Studies |
| library_id |
5267 |
| collection |
Cognizance Journal of Multidisciplinary Studies |
| repository_id |
16997 |
| subject_area |
Multidisciplinary |
| city |
Stockholm |
| province |
INTERNASIONAL |
| shared_to_ipusnas_str |
1 |
| repoId |
IOS16997 |
| first_indexed |
2022-06-06T04:13:23Z |
| last_indexed |
2022-06-06T04:13:23Z |
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dc |
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1734901384616083456 |
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17.538404 |