Analysis of Risk Factors and Symptoms of Burnout Syndrome in Colombian School Teachers under Statutes 2277 and 1278 Using Machine Learning Interpretation
Abstract
:1. Introduction
2. Materials and Methods
2.1. Statistical Analysis
2.2. Machine Learning Classification Analysis
2.2.1. Decision Trees (DT)
2.2.2. Support Vector Machines
2.3. Performance Evaluation of Machine Learning Models
2.4. Machine Learning Interpretability
2.4.1. SHAP Summary Plots
2.4.2. Predictor importance in Decision Trees
3. Results
4. Discussion
5. Conclusions
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
References
- Aceves, Gutiérrez, Guillermo Axayacalt, Miguel Ángel Celis López, Sergio Moreno Jiménez, Felipe Farias Serratos, and José de Jesús Suárez Campos. 2006. Síndrome de Burnout. Archivos de Neurociencias 11: 305–9. [Google Scholar]
- Aragüez Valenzuela, Lucía. 2017. El Impacto de Las Tecnologías de La Información y de La Comunicación En La Salud de Los Trabajadores: El Tecnoestrés. Revista Internacional de La Protección Social 2: 169–90. [Google Scholar] [CrossRef]
- Araujo Lugo, Martha Viviana, and Karina Elizabeth Solís Marín. 2017. Habilidades de comunicación efectiva y su relación, con la presencia de síndrome de burnout en médicos postgradistas de áreas clínicas y quirúrgicas de la Pontificia Universidad Católica del Ecuador, en el período 2012–2016. Available online: http://repositorio.puce.edu.ec:80/xmlui/handle/22000/13731 (accessed on 15 January 2020).
- Arnold, Steven E., Sharon X. Xie, Yuk Yee Leung, Li-San Wang, Mitchel A. Kling, Xiaosi Han, Eunsun J. Kim, David A. Wolk, David A. Bennett, Alice Chen-Plotkin, and et al. 2012. Plasma Biomarkers of Depressive Symptoms in Older Adults. Translational Psychiatry 2: e65. [Google Scholar] [CrossRef] [PubMed]
- Bambula, Fátima Díaz, Ana María López Sánchez, and María Teresa Varela Arévalo. 2012. Factores Asociados al Síndrome de Burnout En Docentes de Colegios de La Ciudad de Cali, Colombia. Universitas Psychologica 11: 217–27. [Google Scholar] [CrossRef]
- Bautista Macia, Marcela. 2009. La Profesionalización Docente En Colombia. Revista Colombiana de Sociología 32: 111–31. [Google Scholar]
- Breiman, Leo. 1996. Bagging Predictors. Machine Learning 24: 123–40. [Google Scholar] [CrossRef] [Green Version]
- Breiman, Leo. 2017. Classification and Regression Trees. Abindon: Routledge. [Google Scholar]
- Cabrera, Javier Herruzo, and Juan Antonio Moriana Elvira. 2004. Estrés y Burnout En Profesores. International Journal of Clinical and Health Psychology 4: 597–621. [Google Scholar]
- Chauhan, Nagesh Singh. 2020. Decision Tree Algorithm—Explained. Medium. January 15. Available online: https://towardsdatascience.com/decision-tree-algorithm-explained-83beb6e78ef4 (accessed on 15 January 2020).
- Cifuentes Cubillos, Carolina. 2013. Estatuto de Profesionalización Docente En Colombia: Análisis de Los Dos Estatutos Vigentes (Decreto 2277 de 1979 y Decreto 1278 de 2002), Diferencias En La Labor Docente. Ph.D. thesis, Universidad Nacional de Colombia, Bogotá, Colombia. [Google Scholar]
- Definition of ARTIFICIAL INTELLIGENCE. 2020. Available online: https://www.merriam-webster.com/dictionary/artificial+intelligence (accessed on 15 January 2020).
- Delgado, B. Domenech. 1995. Introduccion al Sindrome “Burnout” En Profesores y Maestros y Su Abordaje Terapeutico. Psicología Educativa 1: 63–78. [Google Scholar]
- DelPozo-Banos, Marcos, Ann John, Nicolai Petkov, Damon Mark Berridge, Kate Southern, Keith LLoyd, Caroline Jones, Sarah Spencer, and Carlos Manuel Travieso. 2018. Using Neural Networks with Routine Health Records to Identify Suicide Risk: Feasibility Study. JMIR Mental Health 5: e10144. [Google Scholar] [CrossRef] [Green Version]
- Doshi-Velez, Finale, and Been Kim. 2017. Towards a Rigorous Science of Interpretable Machine Learning. arXiv arXiv:1702.08608. [Google Scholar]
- Egbuna, Oluebube Princess. 2019. Artificial Intelligence, Machine Learning, Deep Learning and Data Science! What’s the Difference? Medium. April 3. Available online: https://medium.com/fbdevclagos/artificial-intelligence-machine-learning-deep-learning-and-data-science-whats-the-difference-e82f9e7094a (accessed on 15 January 2020).
- Estimates of Predictor Importance for Classification Ensemble of Decision Trees—MATLAB. 2020. Available online: https://www.mathworks.com/help/stats/compactclassificationensemble.predictorimportance.html (accessed on 15 January 2020).
- Extremera, Natalio, PABLO Fernández-Berrocal, and Auxiliadora Durán. 2003. Inteligencia Emocional y Burnout En Profesores. Encuentros En Psicología Social 1: 260–65. [Google Scholar]
- Friedman, Jerome H., Jon Louis Bentley, and Raphael Ari Finkel. 1976. An Algorithm for Finding Best Matches in Logarithmic Time. ACM Transactions on Mathematical Software 3: 209–26. [Google Scholar] [CrossRef]
- Gibbons, Jean Dickinson, and Subhabrata Chakraborti. 2011. Nonparametric Statistical Inference. In International Encyclopedia of Statistical Science. Berlin/Heidelberg: Springer, pp. 977–79. [Google Scholar]
- James, Gareth, Daniela Witten, Trevor Hastie, and Robert Tibshirani. 2017. An Introduction to Statistical Learning: with Applications in R, 1st ed. 2013, Corr. 7th Printing 2017 Edition. New York: Springer. [Google Scholar]
- Jost, Lou. 2006. Entropy and Diversity. Oikos 113: 363–75. [Google Scholar] [CrossRef]
- Knight-Wegenstein, A. G. 1973. Die Arbeitszeit Der Lehrer in Der Bundesrepublik Deutschland. Band I: Analyse 2. [Google Scholar]
- León, C., O. Matsui, and C. Beltrán. 2008. Factores Psicosociales y Síndrome de Burnout En Docentes de Educación Básica Del Estado de Jalisco. Investigación En Salud 1: 1–10. [Google Scholar]
- López-Elizalde, Carlos. 2004. Síndrome de Burnout. Revista Mexicana de Anestesiología 27: 131–33. [Google Scholar]
- Lundberg, Scott M, and Su-In Lee. 2017. A Unified Approach to Interpreting Model Predictions. In Advances in Neural Information Processing Systems 30. Edited by I. Guyon, U. V. Luxburg, S. Bengio, H. Wallach, R. Fergus, S. Vishwanathan and R. Garnett. Red Hook: Curran Associates, Inc., pp. 4765–74. Available online: http://papers.nips.cc/paper/7062-a-unified-approach-to-interpreting-model-predictions.pdf (accessed on 15 January 2020).
- Massey, Frank J., Jr. 1951. The Kolmogorov-Smirnov Test for Goodness of Fit. Journal of the American Statistical Association 46: 68–78. [Google Scholar] [CrossRef]
- Miller, Leslie H. 1956. Table of Percentage Points of Kolmogorov Statistics. Journal of the American Statistical Association 51: 111–21. [Google Scholar] [CrossRef]
- Minprotección Publica Instrumentos Para Evaluar Factores de Riesgo Psicosocial. 2011. Available online: https://www.minsalud.gov.co/Paginas/Minprotecci%C3%B3npublicainstrumentosparaevaluarfactoresderiesgopsicosocial.aspx (accessed on 15 January 2020).
- Molnar, Christoph. 2019. Interpretable Machine Learning. Morrisville: Lulu. [Google Scholar]
- Palmer, Yusvisaret, Antonio Gómez-Vera, Carlos Cabrera-Pivaral, Roberto Prince-Vélez, and Roberto Searcy. 2005. Factores de Riesgo Organizacionales Asociados al Síndrome de Burnout En Médicos Anestesiólogos. Salud Mental 28: 82–91. [Google Scholar]
- Pandey, A., N. A. Davis, B. C. White, Nicholas M. Pajewski, Jonathan B. Savitz, Wayne C. Drevets, and Brett A. McKinney. 2012. Epistasis Network Centrality Analysis Yields Pathway Replication across Two GWAS Cohorts for Bipolar Disorder. Translational Psychiatry 2: e154. [Google Scholar] [CrossRef] [Green Version]
- Pérez, Anabella Martínez. 2010. El Síndrome de Burnout. Evolución Conceptual y Estado Actual de La Cuestión. Vivat Academia 112: 42–80. [Google Scholar] [CrossRef] [Green Version]
- Posada Quintero, Jorge Iván, Paula Natalia Molano Vergara, Ronald Mauricio Parra Hernández, Francy Yaneth Brito Osorio, and Edward Andrey Rubio Orozco. 2018. Prevalencia Del Síndrome de Burnout En Docentes: Factores Asociados al Estatuto de Vinculación Laboral En Colombia. Revista Interamericana de Psicología Ocupacional 37: 119–33. [Google Scholar] [CrossRef]
- Posada-Quintero, Hugo F., and Jeffrey B. Bolkhovsky. 2019. Machine Learning Models for the Identification of Cognitive Tasks Using Autonomic Reactions from Heart Rate Variability and Electrodermal Activity. Behavioral Sciences 9: 45. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Protecting Workers’ Health. 2017. Available online: https://www.who.int/news-room/fact-sheets/detail/protecting-workers’-health (accessed on 15 January 2020).
- Ramasubbu, Rajamannar, Matthew R. G. Brown, Filmeno Cortese, Ismael Gaxiola, Bradley Goodyear, Andrew J. Greenshaw, Serdar M. Dursun, and Russell Greiner. 2016. Accuracy of Automated Classification of Major Depressive Disorder as a Function of Symptom Severity. NeuroImage: Clinical 12: 320–31. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Redó, Nuria Arís. 2009. El Síndrome de Burnout En Los Docentes. Electronic Journal of Research in Educational Psychology 7: 829–48. [Google Scholar]
- Rodríguez, Luz Adriana Medina. 2015. Innovación y Gestión Del Conocimiento; Síndromes Laborales Necesarios de Extiguir. RIDE Revista Iberoamericana Para La Investigación y El Desarrollo Educativo 6. [Google Scholar] [CrossRef] [Green Version]
- Roman, Victor. 2019. How to Develop a Machine Learning Model from Scratch. Medium. April 2. Available online: https://towardsdatascience.com/machine-learning-general-process-8f1b510bd8af (accessed on 15 January 2020).
- Saeb, Sohrab, Luca Lonini, Arun Jayaraman, David C. Mohr, and Konrad P. Kording. 2017. The Need to Approximate the Use-Case in Clinical Machine Learning. GigaScience 6. [Google Scholar] [CrossRef] [Green Version]
- Saquero, A., G. L. Jaime, and F. Ortín. 2018. Relación Entre el Síndrome de Burnout, Niveles de Optimismo, Edad y Experiencia Profesional en Entrenadoras y Entrenadores de Gimnasia Rítmica. Journal of Sport & Health Research 10: 79–90. [Google Scholar]
- Sauter, Steven L., Lawrence R. Murphy, Joseph J. Hurrell, and Lennart Levi. 2001. Factores Psicosociales y de Organización. In Enciclopedia de Salud y Seguridad En El Trabajo, 3rd ed. Madrid: Organización Internacional Del Trabajo y Ministerio de Trabajo y Asuntos Sociales, Capítulo 34. [Google Scholar]
- Sehra, Chirag. 2018. Decision Trees Explained Easily. Medium. January 19. Available online: https://medium.com/@chiragsehra42/decision-trees-explained-easily-28f23241248 (accessed on 15 January 2020).
- Seiffert, Chris, Taghi M. Khoshgoftaar, Jason Van Hulse, and Amri Napolitano. 2008. RUSBoost: Improving Classification Performance When Training Data Is Skewed. Paper presented at the 2008 19th International Conference on Pattern Recognition, Tampa, FL, USA, December 8–11; pp. 1–4. [Google Scholar]
- Shaikh, Raheel. 2018. Cross Validation Explained: Evaluating Estimator Performance. Medium. November 26. Available online: https://towardsdatascience.com/cross-validation-explained-evaluating-estimator-performance-e51e5430ff85 (accessed on 15 January 2020).
- Shapley, Lloyd S. 1952. A Value for n-Person Games. RAND-P-295. Santa Monica: RAND Corp., Available online: https://apps.dtic.mil/docs/citations/AD0604084 (accessed on 15 January 2020).
- Shawe-Taylor, John, and Nello Cristianini. 2000. An Introduction to Support Vector Machines and Other Kernel-Based Learning Methods. Cambridge: Cambridge University Press, vol. 204. [Google Scholar]
- Stake, Robert E. 1994. Case Studies. In Handbook of Qualitative Researh. Thousand Oaks: Sage Publications, pp. 236–47. [Google Scholar]
- Stake, Robert E. 1995. The Art of Case Study Research. Thousand Oaks: Sage. [Google Scholar]
- Stake, Robert E. 2005. Qualitative Case Studies. In The Sage Handbook of Qualitative Research. Thousand Oaks: Sage Publications Ltd. [Google Scholar]
- Tight, Malcolm. 2010. The Curious Case of Case Study: A Viewpoint. International Journal of Social Research Methodology 13: 329–39. [Google Scholar] [CrossRef]
- Tight, Malcolm. 2012. Researching Higher Education. London: McGraw-Hill Education (UK). [Google Scholar]
- Velásquez, Néstor Raúl Porras. 2017. Análisis de La Relación Entre La Salud Mental y El Malestar Humano En El Trabajo. Equidad & Desarrollo, 161–78. [Google Scholar] [CrossRef]
- Wang, Jingbo, Wai Wan Tsang, and George Marsaglia. 2003. Evaluating Kolmogorov’s Distribution. Journal of Statistical Software 8. [Google Scholar] [CrossRef]
Item | Question | Type | Associated Feature |
---|---|---|---|
Which statute of professionalization do you belong to? | |||
1 | Do the tasks you do in the Educational Institution agree with your abilities and skills? | Risk factor | PROPER_TASKS |
2 | Are the channels and spaces of communication with your bosses good? | Risk factor | APPROPRIATE_COMMUNICATION |
3 | Do you get along with your coworkers? | Risk factor | INTERPERSONAL_RELATIONSHIPS |
4 | During the work day, do you have at least two breaks or pauses? | Risk factor | PAUSES |
5 | At the end of the day, do you feel fatigued? | Symptom | FATIGUE |
6 | When facing difficulties with parents or students, do you get support from your bosses? | Risk factor | HEAD_SUPPORT |
Do you feel physical alterations associated with stressful situations? | |||
Frequent headaches | Symptom | HEADACHE | |
7 | Easily irritated | Symptom | IRRITABILITY |
8 | Feel like crying | Symptom | WISH_TO_CRY |
9 | He has lost your appetite | Symptom | LOSS_OF_APPETITE |
10 | Irregular sleep | Symptom | IRREGULAR_SLEEP |
11 | You feel depressed | Symptom | DEPRESSION |
12 | You feel despair | Symptom | DESPAIR |
13 | Communication difficulty | Symptom | COMMUNICATION_DIFFICULTY |
You have consulted the doctor for: | |||
14 | Mental health | Symptom | MENTAL_ILLNESS |
15 | Musculoskeletal Injuries | Symptom | SKELETAL_MUSCLE_INJURIES |
16 | Voice injuries | Symptom | VOICE_INJURIES |
17 | Gastric problems | Symptom | GASTRIC_PROBLEMS |
During your professional performance, have you ever: | |||
18 | Received training opportunities? | Risk factor | CAPACITATION |
19 | Received the salary required for you to meet your needs? | Risk factor | APPROPRIATE_SALARY |
20 | Been recognized for your work performance and contributions? | Risk factor | PERFORMANCE_APPRECIATION |
21 | Been given permission to join a union | Risk factor | UNION_AFILIATION |
22 | Felt that your bosses have a tendency to look for failures and apply sanctions? | Risk factor | SANCTIONS_TO_FAILS |
23 | Felt that your bosses are too strict about complying with working hours? | Risk factor | FULFILL_WORK_SCHEDULE |
24 | Do you usually work overtime? | Risk factor | OVERTIME |
25 | Do you think you need social recognition? | Risk factor | RECOGNITION |
Statute 1278 | Statute 2277 | p-Value | ||
---|---|---|---|---|
RISK FACTORS | PROPER_TASKS | 2.56 ± 0.561 | 2.71 ± 0.588 | 0.277 |
APPROPRIATE_COMMUNICATION | 2.03 ± 0.499 | 2.47 ± 0.8 * | 0.003 | |
INTERPERSONAL_RELATIONSHIPS | 2.57 ± 0.502 | 2.76 ± 0.437 | 0.171 | |
PAUSES | 1.7 ± 1.2 | 1.53 ± 1.37 | 0.640 | |
HEAD_SUPPORT | 2.27 ± 0.608 | 2.65 ± 0.493 * | 0.033 | |
CAPACITATION | 1.68 ± 0.915 | 1.65 ± 0.931 | 0.934 | |
APPROPRIATE_SALARY | 1.69 ± 0.856 | 2.53 ± 0.717 * | 0.001 | |
PERFORMANCE_APPRECIATION | 1.72 ± 0.701 | 2 ± 0.73 | 0.136 | |
UNION_AFILIATION | 2.83 ± 0.561 | 2.76 ± 0.752 | 0.931 | |
SANCTIONS_TO_FAILS | 1.42 ± 1.08 | 0.765 ± 1.03 * | 0.042 | |
FULFILL_WORK_SCHEDULE | 2.61 ± 0.803 | 2.65 ± 0.862 | 0.639 | |
OVERTIME | 1.8 ± 0.833 | 1.18 ± 1.07 * | 0.043 | |
RECOGNITION | 1.58 ± 1 | 1.24 ± 1.25 | 0.377 | |
SYMPTOMS | FATIGUE | 2.39 ± 0.549 | 2.12 ± 0.781 | 0.253 |
HEADACHE | 1.76 ± 0.76 | 1.24 ± 0.903 * | 0.033 | |
IRRITABILITY | 1.49 ± 0.818 | 1.47 ± 0.8 | 0.992 | |
WISH_TO_CRY | 0.943 ± 0.906 | 0.882 ± 0.928 | 0.835 | |
LOSS_OF_APPETITE | 0.912 ± 0.965 | 0.563 ± 0.892 | 0.173 | |
IRREGULAR_SLEEP | 1.46 ± 0.869 | 1.24 ± 0.903 | 0.430 | |
DEPRESSION | 1.17 ± 0.954 | 1.06 ± 0.929 | 0.705 | |
DESPAIR | 1.41 ± 0.865 | 1.29 ± 0.92 | 0.799 | |
COMMUNICATION_DIFFICULTY | 1.11 ± 0.774 | 0.706 ± 0.92 | 0.099 | |
MENTAL_ILLNESS | 0.371 ± 0.843 | 0.313 ± 0.704 | 1.000 | |
SKELETAL_MUSCLE_INJURIES | 0.853 ± 0.857 | 1 ± 1.06 | 0.707 | |
VOICE_INJURIES | 1.05 ± 1.08 | 1 ± 1.06 | 0.880 | |
GASTRIC_PROBLEMS | 1.16 ± 1.07 | 1.47 ± 0.874 | 0.307 |
Machine Learning Model | Risk Factors | Symptoms |
---|---|---|
Single DT | 92% | 71% |
Bag DT | 84% | 61% |
RUSBoost DT | 80% | 82% |
Linear SVM | 96% | 67% |
Gaussian SVM | 92% | 73% |
Symptoms of Burnout | PI |
---|---|
FATIGUE | 0.0055 |
HEADACHE | 0.0050 |
IRRITABILITY | 0.0031 |
WISH_TO_CRY | 0.0008 |
LOSS_OF_APPETITE | 0.0040 |
IRREGULAR_SLEEP | 0 |
DEPRESSION | 0.0012 |
DESPAIR | 0 |
COMMUNICATION_DIFFICULTY | 0.0044 |
MENTAL_ILLNESS | 0.0018 |
SKELETAL_MUSCLE_INJURIES | 0.0026 |
VOICE_INJURIES | 0 |
GASTRIC_PROBLEMS | 0.0036 |
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Posada-Quintero, H.F.; Molano-Vergara, P.N.; Parra-Hernández, R.M.; Posada-Quintero, J.I. Analysis of Risk Factors and Symptoms of Burnout Syndrome in Colombian School Teachers under Statutes 2277 and 1278 Using Machine Learning Interpretation. Soc. Sci. 2020, 9, 30. https://doi.org/10.3390/socsci9030030
Posada-Quintero HF, Molano-Vergara PN, Parra-Hernández RM, Posada-Quintero JI. Analysis of Risk Factors and Symptoms of Burnout Syndrome in Colombian School Teachers under Statutes 2277 and 1278 Using Machine Learning Interpretation. Social Sciences. 2020; 9(3):30. https://doi.org/10.3390/socsci9030030
Chicago/Turabian StylePosada-Quintero, Hugo F., Paula N. Molano-Vergara, Ronald M. Parra-Hernández, and Jorge I. Posada-Quintero. 2020. "Analysis of Risk Factors and Symptoms of Burnout Syndrome in Colombian School Teachers under Statutes 2277 and 1278 Using Machine Learning Interpretation" Social Sciences 9, no. 3: 30. https://doi.org/10.3390/socsci9030030