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Predicting Subjective Well-being in a High-risk Sample of Russian Mental Health App Users

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Predicting Subjective Well-being in a High-risk Sample of Russian Mental Health App Users

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Panicheva, P.; Mararitsa, L.; Sorokin, S.; Koltsova, O.; Rosso, P. (2022). Predicting Subjective Well-being in a High-risk Sample of Russian Mental Health App Users. EPJ Data Science. 11(1):1-43. https://doi.org/10.1140/epjds/s13688-022-00333-x

Por favor, use este identificador para citar o enlazar este ítem: http://hdl.handle.net/10251/200296

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Título: Predicting Subjective Well-being in a High-risk Sample of Russian Mental Health App Users
Autor: Panicheva, Polina Mararitsa, Larisa Sorokin, Semen Koltsova, Olessia Rosso, Paolo
Entidad UPV: Universitat Politècnica de València. Escola Tècnica Superior d'Enginyeria Informàtica
Fecha difusión:
Resumen:
[EN] Despite recent achievements in predicting personality traits and some other human psychological features with digital traces, prediction of subjective well-being (SWB) appears to be a relatively new task with few ...[+]
Palabras clave: Digital traces , Subjective well-being , Mental health prediction
Derechos de uso: Reconocimiento (by)
Fuente:
EPJ Data Science. (eissn: 2193-1127 )
DOI: 10.1140/epjds/s13688-022-00333-x
Editorial:
SpringerOpen
Versión del editor: https://doi.org/10.1140/epjds/s13688-022-00333-x
Tipo: Artículo

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