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

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

Abstract Aims Studies conducted during the COVID-19 pandemic found high occurrence of suicidal thoughts and behaviours (STBs) among healthcare workers (HCWs). The current study aimed to (1) develop a machine learning-based prediction model for future STBs using data from a large prospective cohort o...

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Main Authors: Itxaso Alayo, Oriol Pujol, Jordi Alonso, Montse Ferrer, Franco Amigo, Ana Portillo-Van Diest, Enric Aragonès, Andrés Aragon Peña, Ángel Asúnsolo Del Barco, Mireia Campos, Meritxell Espuga, Ana González-Pinto, Josep Maria Haro, Nieves López-Fresneña, Alma D. Martínez de Salázar, Juan D. Molina, Rafael M. Ortí-Lucas, Mara Parellada, José Maria Pelayo-Terán, Maria João Forjaz, Aurora Pérez-Zapata, José Ignacio Pijoan, Nieves Plana, Elena Polentinos-Castro, Maria Teresa Puig, Cristina Rius, Ferran Sanz, Cònsol Serra, Iratxe Urreta-Barallobre, Ronny Bruffaerts, Eduard Vieta, Víctor Pérez-Solá, Philippe Mortier, Gemma Vilagut
Format: Article
Language:English
Published: Cambridge University Press 2025-01-01
Series:Epidemiology and Psychiatric Sciences
Subjects:
attempted suicide
interpretability
machine learning
mental health
suicidal ideation
Online Access:https://www.cambridge.org/core/product/identifier/S2045796025000198/type/journal_article
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https://www.cambridge.org/core/product/identifier/S2045796025000198/type/journal_article

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