Machine learning-ready mental health datasets for evaluating psychological effects and system needs in Mexico city during the first year of the COVID-19 pandemic

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dc.contributor.affiliationGraduate School of Informatics, Kyoto University, Yoshidahonmachi, Sakyo Ward, Kyoto 606-8317, Japan
dc.contributor.emailgaribay.rodrigo.42s@st.kyoto-u.ac.jp (C.R.GaribayRubio)
dc.creatorGaribay Rubio, Carlos Rodrigo
dc.creatorYamori, Katsuya
dc.creatorNakano, Genta
dc.creatorPeralta Gutiérrez, Astrid Renneé
dc.creatorMorales Chainé, Silvia Morales
dc.creatorRobles García , Rebeca
dc.creatorLanda-Ramírez , Edgar
dc.creatorBojorge Estrada , Alexis
dc.creatorBosch Maldonado, Alejandro
dc.creatorTejadilla Orozco, Diana Iris
dc.date2024
dc.date.accessioned2026-06-09T21:20:49Z
dc.date.issued2024
dc.date.published2024
dc.descriptionThe prevalence of mental health problems constitutes an open challenge for modern societies, particularly for low and middle-income countries with wide gaps in mental health support. With this in mind, five datasets were analyzed to track mental health trends in Mexico City during the pandemic's first year. This included 33,234 responses to an online mental health risk questionnaire, 349,202 emergency calls, and city epidemiological, mobility, and online trend data. The COVID-19 mental health risk questionnaire collects information on socioeconomic status, health conditions, bereavement, lockdown status, and symptoms of acute stress, sadness, avoidance, distancing, anger, and anxiety, along with binge drinking and abuse experiences. The lifeline service dataset includes daily call statistics, such as total, connected, and abandoned calls, average quit time, wait time, and call duration. Epidemiological, mobility, and trend data provide a daily overview of the city's situation. The integration of the datasets, as well as the preprocessing, optimization, and machine learning algorithms applied to them, evidence the usefulness of a combined analytic approach and the high reuse potential of the data set, particularly as a machine learning training set for evaluating and predicting anxiety, depression, and post-traumatic stress disorder, as well as general psychological support needs and possible system loads.
dc.formatPDF
dc.identifierJC56DIEP24
dc.identifier.doi10.1016/j.dib.2024.110877
dc.identifier.eissn2352-3409
dc.identifier.organizacionInstituto Nacional de Psiquiatría Ramón de la Fuente Muñiz
dc.identifier.placePaíses Bajos
dc.identifier.urihttps://repositorio.inprf.gob.mx/handle/123456789/80
dc.identifier.urihttps://doi.org/10.1016/j.dib.2024.110877
dc.language.isoeng
dc.publisherElsevier
dc.relation57:110877
dc.relation.jnabreviadoDATA BRIEF
dc.relation.journalData in Brief
dc.rightsAcceso Cerrado
dc.subject.kwDisaster recovery curve
dc.subject.kwPsychological response in disasters
dc.subject.kwMental health support systems
dc.subject.kwStress response in emergencies
dc.subject.kwAcute stress response
dc.subject.kwEarthquake early warning
dc.subject.kwPandemic mental health effects
dc.titleMachine learning-ready mental health datasets for evaluating psychological effects and system needs in Mexico city during the first year of the COVID-19 pandemic
dc.typeArtículo

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