cris.boxmetadata.label.title
Level of Depression of College Students with Binary Logistic Regression Model Approximation in Covid-19 times
cris.boxmetadata.label.dateissued
01 browse.startsWith.months.january 2021
cris.boxmetadata.label.accesslevel
metadata only access
cris.boxmetadata.label.resourcetype
conference paper
cris.boxmetadata.label.authors
Roque-Tito E.
Bautista-Lopez L.
Gutierrez-Aguilar M.D.
cris.boxmetadata.label.publisher
Institute of Electrical and Electronics Engineers Inc.
cris.boxmetadata.label.abstract
The objective of this research is to detect the level of depression that university students have because of Covid-19 using the binary logistic regression model and comparing with the linear model, it is of non-experimental design, cross-sectional descriptive type, with quantitative approach, The 9-item Patient Health Questionnaire (PHQ-9) was used with a population of 2185 respondents carried out during the month of May, the population is made up of university students from the Piura region chosen randomly, voluntarily and anonymously, resulting that the PHQ-9 measurement instrument is very good with a Cronbach's Alpha = 0. 885 and McDonald's W = 0.886, with 69.9% depression in university students, concentrated in 4 levels of depression, mild 39.7%, moderate 17.8%, severe 7% and very severe 5.3%. It was concluded that the PHQ-9 depression measurement instrument is adequate to measure depression in university students. In the discussion, it was possible to model the equation with the binomial logistic regression model, which results in better approximations than the linear model; this model is adequate to measure the level of depression in university students in the Piura region.
cris.boxmetadata.label.language
Spanish
cris.boxmetadata.label.ocdeknowledgeArea
Psicología (incluye terapias de aprendizaje, habla, visual y otras discapacidades físicas y mentales) Sociología
cris.boxmetadata.label.doi
cris.boxmetadata.label.scopusidentifier
2-s2.0-85125313529
cris.boxmetadata.label.partofresource
Proceedings of the 2021 IEEE 1st International Conference on Advanced Learning Technologies on Education and Research, ICALTER 2021
cris.boxmetadata.label.containerisbn
978-166540705-2
cris.boxmetadata.label.conference
1st IEEE International Conference on Advanced Learning Technologies on Education and Research, ICALTER 2021
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