Redes de síntomas en docentes que sufren síndrome de desgaste ocupacional
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La salud psicológica de los docentes es una pieza fundamental del sistema educativo. El burnout es considerado como una de las dolencias más destructivas que afecta a docentes. Este trabajo tomó una muestra de 257 docentes (186 mujeres) con edades comprendidas entre los 24 y los 64 años (M = 42.58, DT = 9.8) que fue sometida a un análisis cluster para identificar grupos que diferían en su patrón de burnout. Se estimaron redes de síntomas (no-dirigidas y dirigidas) para estrés, ansiedad y depresión segmentadas por conglomerado. Los resultados para los docentes con burnout ponen de manifiesto que el autodesprecio y la devaluación de la vida son síntomas centrales en la depresión, mientras que la experiencia subjetiva de ansiedad y la irritabilidad/sobre-reactividad lo son para la ansiedad y el estrés, respectivamente. Estos resultados son útiles para comprender el burnout en docentes y para intervenir al respecto. Se sugiere que se diseñen programas para prevenir el burnout en docentes que muestran alto riesgo de sufrir agotamiento ocupacional. También se sugiere que se incrementen los niveles de compasión para reducir la depresión entre docentes que puntúan alto en burnout. Se propone que las intervenciones basadas en terapia cognitivo-conductual o mindfulness podrían ser efectivas en estos casos.
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Agyapong, B., Obuobi-Donkor, G., Burback, L., & Wei, Y. (2022). Stress, burnout, anxiety and depression among teachers: A scoping review. International Journal of Environmental Research and Public Health, 19(17), Article 10706. https://doi.org/10.3390/ijerph191710706 DOI: https://doi.org/10.3390/ijerph191710706
Álvarez-Díaz, M., Gallego-Acedo, C., Fernández-Alonso, R., Muñiz, J., & Fonseca-Pedrero, E. (2022). Análisis de redes: Una alternativa a los enfoques clásicos de evaluación de los sistemas educativos. Psicología Educativa, 28(2), 165-173. https://doi.org/10.5093/psed2021a16 DOI: https://doi.org/10.5093/psed2021a16
American Psychological Association. (2017). Ethical principles of psychologists and code of conduct (2002, amended effective June 1, 2010, and January 1, 2017). https://www.apa.org/ethics/code/
Bados, A., Solanas, A., & Andrés, R. (2005). Psychometric properties of the Spanish version of Depression, Anxiety and Stress Scales (DASS). Psicothema, 17(4), 679-683.
Battams, S., Roche, A. M., Fischer, J. A., Lee, N. K., Cameron, J., & Kostadinov, V. (2014). Workplace risk factors for anxiety and depression in male-dominated industries: A systematic review. Health Psychology and Behavioral Medicine, 2(1), 983-1008. https://doi.org/10.1080/21642850.2014.954579 DOI: https://doi.org/10.1080/21642850.2014.954579
Bianchi, R., Schonfeld, I. S., & Laurent, E. (2015). Burnout–depression overlap: A review. Clinical Psychology Review, 36, 28-41. https://doi.org/10.1016/j.cpr.2015.01.004 DOI: https://doi.org/10.1016/j.cpr.2015.01.004
Bollobás, B. (2013). Modern graph theory. Springer. https://doi.org/10.1007/978-1-4612-0619-4 DOI: https://doi.org/10.1007/978-1-4612-0619-4
Borsboom, D. (2017). A network theory of mental disorders. World Psychiatry, 16(1), 5-13. https://doi.org/10.1002/wps.20375 DOI: https://doi.org/10.1002/wps.20375
Borsboom, D., & Cramer, A. O. J. (2013). Network analysis: An integrative approach to the structure of psychopathology. Annual Review of Clinical Psychology, 9, 91-121. http://doi.org/10.1146/annurev-clinpsy-050212-185608 DOI: https://doi.org/10.1146/annurev-clinpsy-050212-185608
Borsboom, D., Cramer, A. O. J., Schmittmann, V. D., Epskamp, S., & Waldrop, L. J. (2011). The small world of psychopathology. PLoS ONE, 6(11), Article e27407. https://doi.org/10.1371/journal.pone.0027407 DOI: https://doi.org/10.1371/journal.pone.0027407
Briganti, G., Scutari, M., & McNally, R. J. (2023-2024). A tutorial on bayesian networks for psychopathology researchers. Psychological Methods, 28(4), 947-961. https://doi.org/10.1037/met0000479 DOI: https://doi.org/10.1037/met0000479
Cao, C. H., Liao, X. L., Jiang, X. Y., Li, X. D., Chen, I. H., & Lin, C. Y. (2023). Psychometric evaluation of the depression, anxiety, and stress scale-21 (DASS-21) among Chinese primary and middle school teachers. BMC Psychology, 11(1), Article 209. https://doi.org/10.1186/s40359-023-01242-y DOI: https://doi.org/10.1186/s40359-023-01242-y
Capone, V., & Petrillo, G. (2020). Mental health in teachers: Relationships with job satisfaction, efficacy beliefs, burnout and depression. Current Psychology, 39, 1757-1766. https://doi.org/10.1007/s12144-018-9878-7 DOI: https://doi.org/10.1007/s12144-018-9878-7
Charrad, M., Ghazzali, N., Boiteau, V., & Niknafs, A. (2014). NbClust: An R package for determining the relevant number of clusters in a data set. Journal of Statistical Software, 61(6), 1-36. https://doi.org/10.18637/jss.v061.i06 DOI: https://doi.org/10.18637/jss.v061.i06
Cramer, A. O. J., Waldrop, L. J., Van der Maas, H. L. J., & Borsboom, D. (2010). Comorbidity: A network perspective. Behavioral and Brain Sciences, 33(2-3), 137-510. https://doi.org/10.1017/s0140525x09991567 DOI: https://doi.org/10.1017/S0140525X09991567
Daza, P., Novy, D. M., Stanley, M. A., & Averill, P. (2002). The Depression Anxiety Stress Scale-21: Spanish translation and validation with a Hispanic sample. Journal of Psychopathology and Behavioral Assessment, 24(3), 195-205. https://doi.org/10.1023/A:1016014818163 DOI: https://doi.org/10.1023/A:1016014818163
Diestel, R. (2025). Graph theory (6th ed.). Springer. https://doi.org/10.1007/978-3-662-70107-2 DOI: https://doi.org/10.1007/978-3-662-70107-2
Epskamp, S., Cramer, A. O. J., Waldorp, L. J., Schmittmann, V. D., & Borsboom, D. (2012). qgraph: Network visualizations of relationships in psychometric data. Journal of Statistical Software, 48(4), 1-18. https://doi.org/10.18637/jss.v048.i04 DOI: https://doi.org/10.18637/jss.v048.i04
Fruchterman, T. M. J., & Reingold, E. M. (1991). Graph drawing by force-directed placement. Journal of Software: Practice and Experience, 21(11), 1129-1164. https://doi.org/10.1002/spe.4380211102 DOI: https://doi.org/10.1002/spe.4380211102
Gil-Monte, P. R. (2019). CESQT. Cuestionario para la Evaluación del Síndrome de Quemarse por el Trabajo: manual. (2nd ed.). TEA/Hogrefe.
Gil-Monte, P. R., Unda Rojas, S., & Sandoval Ocaña, J. I. (2009). Validez factorial del “Cuestionario para la Evaluación del Síndrome de Quemarse por el Trabajo” (CESQT) en una muestra de maestros mexicanos. Salud Mental, 32(3), 205-214.
Glymour, C., Zhang, K., & Spirtes, P. (2019). Review of causal discovery methods based on graphical models. Frontiers in Genetics, 10, Article 524. https://doi.org/10.3389/fgene.2019.00524 DOI: https://doi.org/10.3389/fgene.2019.00524
Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2014). Multivariate data analysis (7th ed.). Pearson.
Harris, C. S., Dodd, M., Kober, K. M., Dhruva, A. A., Hammer, M. J., Conley, Y. P., & Miaskowski, C. A. (2022). Advances in conceptual and methodological issues in symptom cluster research. Advances in Nursing Science, 45(4), 309-322. https://doi.org/10.1097/ans.0000000000000423 DOI: https://doi.org/10.1097/ANS.0000000000000423
Hartigan, J. A., & Wong, M. A. (1979). Algorithm AS 136: A k-means clustering algorithm. Journal of the Royal Statistical Society Series C: Applied Statistics, 28(1), 100-108. https://doi.org/10.2307/2346830 DOI: https://doi.org/10.2307/2346830
Hester, O. R., Bridges, S. A., & Rollins, L. H. (2020). ‘Overworked and underappreciated’: Special education teachers describe stress and attrition. Teacher Development, 24(3), 348-365. https://doi.org/10.1080/13664530.2020.1767189 DOI: https://doi.org/10.1080/13664530.2020.1767189
Kidger, J., Brockman, R., Tilling, K., Campbell, R., Ford, T., Araya, R., King, M., & Gunnell, D. (2016). Teachers’ wellbeing and depressive symptoms, and associated risk factors: A large cross sectional study in English secondary schools. Journal of Affective Disorders, 192, 76-82. https://doi.org/10.1016/j.jad.2015.11.054 DOI: https://doi.org/10.1016/j.jad.2015.11.054
Kolaczyk, E. D., & Csárdi G. (2020). Statistical analysis of network data with R (2nd ed.). Springer. https://doi.org/10.1007/978-3-030-44129-6 DOI: https://doi.org/10.1007/978-3-030-44129-6
Koutsimani, P., Montgomery, A., & Georganta, K. (2019). The relationship between burnout, depression, and anxiety: A systematic review and meta-analysis. Frontiers in Psychology, 10, Article 284. https://doi.org/10.3389/fpsyg.2019.00284 DOI: https://doi.org/10.3389/fpsyg.2019.00284
Kumar Yadav, S. (2023). Advanced graph theory. Springer. https://doi.org/10.1007/978-3-031-22562-8 DOI: https://doi.org/10.1007/978-3-031-22562-8
Ladyman, J., Lambert, J., & Wiesner, K. (2012). What is a complex system? European Journal of Philosophy of Science, 3(1), 33-67. https://doi.org/10.1007/s13194-012-0056-8 DOI: https://doi.org/10.1007/s13194-012-0056-8
Liu, W., Wang, Y., & Yao, H. (2024). Relationship between teachers’ workaholic characteristics and emotional exhaustion – The mediating role of work-family conflict and work efficacy and the moderating role of teaching age. Current Psychology, 43(28), 23793- 23814. https://doi.org/10.1007/s12144-024-06090-6 DOI: https://doi.org/10.1007/s12144-024-06090-6
Lovibond, P. F., & Lovibond, S. H. (1995). The structure of negative emotional states: Comparison of the Depression Anxiety Stress Scales (DASS) with the Beck Depression and Anxiety Inventories. Behaviour Research and Therapy, 33(3), 335-343. https://doi.org/10.1016/0005-7967(94)00075-U DOI: https://doi.org/10.1016/0005-7967(94)00075-U
Mahan, P. L., Mahan, M. P., Park, N.-J., Shelton, C., Brown, K. C., & Weaver, M. T. (2010). Work environment stressors, social support, anxiety, and depression among secondary school teachers. American Association of Occupational Health Nurses Journal, 58(5), 197-205. https://doi.org/10.1177/216507991005800504 DOI: https://doi.org/10.3928/08910162-20100416-01
Manley, H., Tu, E. N., Reardon, T., & Creswell, C. (2023). The relationship between teachers’ day-to-day classroom management practices and anxiety in primary school children: A systematic review. Review of Education, 11(11), Article e3385. https://doi.org/10.1002/rev3.3385 DOI: https://doi.org/10.1002/rev3.3385
Martínez-Monteagudo, M. C., Inglés, C., Granados, L., Aparisi, D., & García-Fernández, J. M. (2019). Trait emotional intelligence profiles, burnout, anxiety, depression, and stress in secondary education teachers. Personality and Individual Differences, 142, 53-61. https://doi.org/10.1016/j.paid.2019.01.036 DOI: https://doi.org/10.1016/j.paid.2019.01.036
Maslach, C. (2017). Finding solutions to the problem of burnout. Consulting Psychology Journal, 69(2), 143-152. https://doi.org/10.1037/cpb0000090 DOI: https://doi.org/10.1037/cpb0000090
Maslach, C., & Leiter, M. P. (2016). Understanding the burnout experience: Recent research and its implications for psychiatry. World Psychiatry, 15(2), 103-111. https://doi.org/10.1002/wps.20311 DOI: https://doi.org/10.1002/wps.20311
Maslach, C., & Leiter, M. P. (2017). Understanding burnout: New models. In C. L. Cooper & J. C. Quick (Eds.), The handbook of stress and health. A guide to research and practise (pp. 36-56). Wiley. https://doi.org/10.1002/9781118993811.ch3 DOI: https://doi.org/10.1002/9781118993811.ch3
Medvedev, O. N. (2023). Depression Anxiety Stress Scales (DASS-21) in international contexts. In C. U. Krägeloh, M. Alyami, & O. N. Medvedev (Eds.), International handbook of behavioral health assessment (pp. 1-15). Springer. https://doi.org/10.1007/978-3-030-89738-3_15-1 DOI: https://doi.org/10.1007/978-3-030-89738-3_15-1
Mohzana, M., Tawil, M. R., Sakti, B. P., Ramli, A., & Lubis, F. M. (2023). The influence of workload, demographic factors and hardiness on teachers’ work stress. Journal on Education, 5(4), 15631-15636.
Nosek, B. A., Ebersole, C. R., DeHaven, A. C., & Mellor, D. T. (2018). The preregistration revolution. Proceedings of the National Academy of Sciences, 115(11), 2600-2606. https://doi.org/10.1073/pnas.1708274114 DOI: https://doi.org/10.1073/pnas.1708274114
Oliver, E., Lazariuk, L., Archambault, I., & Morin, A. J. S. (2024). Teacher emotional exhaustion: The synergistic roles of self-efficacy and student–teacher relationships. Social Psychology of Education, 27(1), 22-27. https://doi.org/10.1007/s11218-023-09826-7 DOI: https://doi.org/10.1007/s11218-023-09826-7
Puga, J. L., Krzywinski, M., & Altman, N. (2015). Bayesian networks. Nature Methods, 12(9), 799-800. https://doi.org/10.1038/nmeth.3550 DOI: https://doi.org/10.1038/nmeth.3550
Quintana, R. (2022). The ecology of human behavior: A network perspective. Methodological Innovations, 15(1), 42-61. https://doi.org/10.1177/20597991221077911 DOI: https://doi.org/10.1177/20597991221077911
Rocco, C. M., Barker, K., Moronta, J., & González, A. D. (2024). A psychological network analysis of the relationship among component importance measures. Applied Network Science, 9(1), Article 20. https://doi.org/10.1007/s41109-024-00631-5 DOI: https://doi.org/10.1007/s41109-024-00631-5
Ruiz-Ruano, A., & Puga, J. L. (2020). Modelos gráficos y redes en psicología. Revista de Historia de la Psicología, 41(4), 24-33. https://doi.org/10.5093/rhp2020a18 DOI: https://doi.org/10.5093/rhp2020a18
Ruiz-Ruano, A. M., Blaya Sánchez, M. Ángel, López Morales, J. L., Peinado Portero, A. I., Giner Alegría, C. A., López Puga, J., & Moya-Faz, F. J. (2023). Psychosocial risks factors and burnout in police officers: A network analysis. Annals of Psychology, 39(3), 478-486. https://doi.org/10.6018/analesps.522361 DOI: https://doi.org/10.6018/analesps.522361
Schaufeli, W. B. (2017). Burnout: A short socio-cultural history. In S. Neckel, A. Schaffner & G. Wagner (Eds.), Burnout, fatigue, exhaustion (pp. 105-127). Springer. https://doi.org/10.1007/978-3-319-52887-8_5 DOI: https://doi.org/10.1007/978-3-319-52887-8_5
Schmittmann, V. D., Cramer, A. O. J., Waldorp, L. J., Epskamp, S., Kievit, R. A., & Borsboom, D. (2013). New ideas in psychology. New Ideas in Psychology, 31(1), 43-53. http://doi.org/10.1016/j.newideapsych.2011.02.007 DOI: https://doi.org/10.1016/j.newideapsych.2011.02.007
Schoeps, K., Tamarit, A., Peris-Hernández, M., & Montoya-Castilla, I. (2021). Impact of emotional intelligence on burnout among Spanish teachers: A mediation study. Psicología Educativa, 27(2), 135-143. https://doi.org/10.5093/psed2021a10 DOI: https://doi.org/10.5093/psed2021a10
Scutari, M. (2010). Learning bayesian networks with the bnlearn R package. Journal of Statistical Software, 35(3), 1-22. https://doi.org/10.18637/jss.v035.i03 DOI: https://doi.org/10.18637/jss.v035.i03
Serdar, C. C., Cihan, M., Yücel, D., & Serdar, M. A. (2021). Sample size, power and effect size revisited: Simplified and practical approaches in pre-clinical, clinical and laboratory studies. Biochemia Medica, 31(1), Article 010502. https://doi.org/10.11613/BM.2021.010502 DOI: https://doi.org/10.11613/BM.2021.010502
Turner, K., & Garvis, S. (2023). Teacher educator wellbeing, stress and burnout: A scoping review. Education Sciences, 13(4), Article 351. https://doi.org/10.3390/educsci13040351 DOI: https://doi.org/10.3390/educsci13040351
Wang, H., Sun, Y., Wang, W., & Liang, H. (2025). Exploring the relationship between teachers’ perceived workload, challenge-hindrance stress, and work engagement: A person-centered approach. BMC Psychology, 13(1), Article 201. https://doi.org/10.1186/s40359-025-02537-y DOI: https://doi.org/10.1186/s40359-025-02537-y
Watts, D., & Strogatz, S. (1998, 4 de June). Collective dynamics of ‘small-world’ networks. Nature, 393, 440-442. https://doi.org/10.1038/30918 DOI: https://doi.org/10.1038/30918
World Health Organization. (2022). ICD-11: International Classification of Diseases (11th revision). https://icd.who.int
World Medical Association. (2013). World Medical Association declaration of Helsinki ethical principles for medical research involving human subjects. The Journal of the American Medical Association, 310(20), 2191-2194. https://doi.org/10.1001/jama.2013.281053 DOI: https://doi.org/10.1001/jama.2013.281053
Wu, Q., Cao, H., & Du, H. (2023). Work stress, work-related rumination, and depressive symptoms in university teachers: Buffering effect of self-compassion. Psychology Research and Behavior Management, 16, 1557-1569. https://doi.org/10.2147/PRBM.S403744 DOI: https://doi.org/10.2147/PRBM.S403744