A machine learning model to predict the risk factors causing feelings of burnout and emotional exhaustion amongst nursing staff in South Africa
Abstract Background The demand for quality healthcare is rising worldwide, and nurses in South Africa are under pressure to provide care with limited resources. This demanding work environment leads to burnout and exhaustion among nurses. Understanding the specific factors leading to these issues is...
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BMC
2024-12-01
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| Series: | BMC Health Services Research |
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| Online Access: | https://doi.org/10.1186/s12913-024-12184-5 |
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| author | Maria Magdalena Van Zyl-Cillié Jacoba H. Bührmann Alwiena J. Blignaut Derya Demirtas Siedine K. Coetzee |
| author_facet | Maria Magdalena Van Zyl-Cillié Jacoba H. Bührmann Alwiena J. Blignaut Derya Demirtas Siedine K. Coetzee |
| author_sort | Maria Magdalena Van Zyl-Cillié |
| collection | DOAJ |
| description | Abstract Background The demand for quality healthcare is rising worldwide, and nurses in South Africa are under pressure to provide care with limited resources. This demanding work environment leads to burnout and exhaustion among nurses. Understanding the specific factors leading to these issues is critical for adequately supporting nurses and informing policymakers. Currently, little is known about the unique factors associated with burnout and emotional exhaustion among nurses in South Africa. Furthermore, whether these factors can be predicted using demographic data alone is unclear. Machine learning has recently been proven to solve complex problems and accurately predict outcomes in medical settings. In this study, supervised machine learning models were developed to identify the factors that most strongly predict nurses reporting feelings of burnout and experiencing emotional exhaustion. Methods The PyCaret 3.3 package was used to develop classification machine learning models on 1165 collected survey responses from nurses across South Africa in medical-surgical units. The models were evaluated on their accuracy score, Area Under the Curve (AUC) score and confusion matrix performance. Additionally, the accuracy score of models using demographic data alone was compared to the full survey data models. The features with the highest predictive power were extracted from both the full survey data and demographic data models for comparison. Descriptive statistical analysis was used to analyse survey data according to the highest predictive factors. Results The gradient booster classifier (GBC) model had the highest accuracy score for predicting both self-reported feelings of burnout (75.8%) and emotional exhaustion (76.8%) from full survey data. For demographic data alone, the accuracy score was 60.4% and 68.5%, respectively, for predicting self-reported feelings of burnout and emotional exhaustion. Fatigue was the factor with the highest predictive power for self-reported feelings of burnout and emotional exhaustion. Nursing staff’s confidence in management was the second highest predictor for feelings of burnout whereas management who listens to employees was the second highest predictor for emotional exhaustion. Conclusions Supervised machine learning models can accurately predict self-reported feelings of burnout or emotional exhaustion among nurses in South Africa from full survey data but not from demographic data alone. The models identified fatigue rating, confidence in management and management who listens to employees as the most important factors to address to prevent these issues among nurses in South Africa. |
| format | Article |
| id | doaj-art-b63ea315ebae4177b3444e621171391b |
| institution | Kabale University |
| issn | 1472-6963 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Health Services Research |
| spelling | doaj-art-b63ea315ebae4177b3444e621171391b2025-01-05T12:12:43ZengBMCBMC Health Services Research1472-69632024-12-0124112010.1186/s12913-024-12184-5A machine learning model to predict the risk factors causing feelings of burnout and emotional exhaustion amongst nursing staff in South AfricaMaria Magdalena Van Zyl-Cillié0Jacoba H. Bührmann1Alwiena J. Blignaut2Derya Demirtas3Siedine K. Coetzee4Faculty of Engineering, North-West UniversityFaculty of Engineering, North-West UniversityNuMIQ Research Focus Area, School of Nursing Science, North-West UniversityFaculty of Behavioural, Management and Social Sciences, University of TwenteNuMIQ Research Focus Area, School of Nursing Science, North-West UniversityAbstract Background The demand for quality healthcare is rising worldwide, and nurses in South Africa are under pressure to provide care with limited resources. This demanding work environment leads to burnout and exhaustion among nurses. Understanding the specific factors leading to these issues is critical for adequately supporting nurses and informing policymakers. Currently, little is known about the unique factors associated with burnout and emotional exhaustion among nurses in South Africa. Furthermore, whether these factors can be predicted using demographic data alone is unclear. Machine learning has recently been proven to solve complex problems and accurately predict outcomes in medical settings. In this study, supervised machine learning models were developed to identify the factors that most strongly predict nurses reporting feelings of burnout and experiencing emotional exhaustion. Methods The PyCaret 3.3 package was used to develop classification machine learning models on 1165 collected survey responses from nurses across South Africa in medical-surgical units. The models were evaluated on their accuracy score, Area Under the Curve (AUC) score and confusion matrix performance. Additionally, the accuracy score of models using demographic data alone was compared to the full survey data models. The features with the highest predictive power were extracted from both the full survey data and demographic data models for comparison. Descriptive statistical analysis was used to analyse survey data according to the highest predictive factors. Results The gradient booster classifier (GBC) model had the highest accuracy score for predicting both self-reported feelings of burnout (75.8%) and emotional exhaustion (76.8%) from full survey data. For demographic data alone, the accuracy score was 60.4% and 68.5%, respectively, for predicting self-reported feelings of burnout and emotional exhaustion. Fatigue was the factor with the highest predictive power for self-reported feelings of burnout and emotional exhaustion. Nursing staff’s confidence in management was the second highest predictor for feelings of burnout whereas management who listens to employees was the second highest predictor for emotional exhaustion. Conclusions Supervised machine learning models can accurately predict self-reported feelings of burnout or emotional exhaustion among nurses in South Africa from full survey data but not from demographic data alone. The models identified fatigue rating, confidence in management and management who listens to employees as the most important factors to address to prevent these issues among nurses in South Africa.https://doi.org/10.1186/s12913-024-12184-5Supervised machine learning modelNurse burnoutEmotional exhaustionMaslach Burnout Inventory |
| spellingShingle | Maria Magdalena Van Zyl-Cillié Jacoba H. Bührmann Alwiena J. Blignaut Derya Demirtas Siedine K. Coetzee A machine learning model to predict the risk factors causing feelings of burnout and emotional exhaustion amongst nursing staff in South Africa BMC Health Services Research Supervised machine learning model Nurse burnout Emotional exhaustion Maslach Burnout Inventory |
| title | A machine learning model to predict the risk factors causing feelings of burnout and emotional exhaustion amongst nursing staff in South Africa |
| title_full | A machine learning model to predict the risk factors causing feelings of burnout and emotional exhaustion amongst nursing staff in South Africa |
| title_fullStr | A machine learning model to predict the risk factors causing feelings of burnout and emotional exhaustion amongst nursing staff in South Africa |
| title_full_unstemmed | A machine learning model to predict the risk factors causing feelings of burnout and emotional exhaustion amongst nursing staff in South Africa |
| title_short | A machine learning model to predict the risk factors causing feelings of burnout and emotional exhaustion amongst nursing staff in South Africa |
| title_sort | machine learning model to predict the risk factors causing feelings of burnout and emotional exhaustion amongst nursing staff in south africa |
| topic | Supervised machine learning model Nurse burnout Emotional exhaustion Maslach Burnout Inventory |
| url | https://doi.org/10.1186/s12913-024-12184-5 |
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