Identifying most important predictors for suicidal thoughts and behaviours among healthcare workers active during the Spain COVID-19 pandemic : A machine-learning approach

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URI: https://hdl.handle.net/10641/7543
ISSN: 2045-7960
DOI: 10.1017/S2045796025000198

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2025-05-08

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Publisher Copyright: © 2025 The Author(s).

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attempted suicide, interpretability, machine learning, mental health, suicidal ideation, Epidemiology, Public Health, Environmental and Occupational Health, Psychiatry and Mental health, SDG 3 - Good Health and Well-being, Journal Article, Multicenter Study, Yes, yes

Citation

Alayo, I, Pujol, O, Alonso, J, Ferrer, M, Amigo, F, Portillo-Van Diest, A, Aragonès, E, Aragon Peña, A, Asúnsolo Del Barco, Á, Campos, M, Espuga, M, González-Pinto, A, Haro, J M, López-Fresneña, N, Martínez De Salázar, A D, Molina, J D, Ortí-Lucas, R M, Parellada, M, Pelayo-Terán, J M, Forjaz, M J, Pérez-Zapata, A, Pijoan, J I, Plana, N, Polentinos-Castro, E, Puig, M T, Rius, C, Sanz, F, Serra, C, Urreta-Barallobre, I, Bruffaerts, R, Vieta, E, Pérez-Solá, V, Mortier, P & Vilagut, G 2025, 'Identifying most important predictors for suicidal thoughts and behaviours among healthcare workers active during the Spain COVID-19 pandemic : A machine-learning approach', Epidemiology and Psychiatric Sciences, vol. 34, e28. https://doi.org/10.1017/S2045796025000198

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FACULTAD DE DERECHO, EMPRESA Y GOBIERNO