[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-128728-en":3,"doc-seo-128728-105":31,"detail-sidebar-cat-0-en-105":92},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},128728,1099523882182,"Eliana","https://ap-avatar.wpscdn.com/davatar_6f874abed73319feea01a86fa6f0fab8",8,"Research & Report","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 - Research dataset and system needs evaluation","Five machine learning-ready datasets track mental health trends in Mexico City during the first year of the COVID-19 pandemic, addressing mental health support gaps faced by low- and middle-income countries. The study integrates 33,234 questionnaire responses, online mental health risk assessment, lifeline emergency call statistics, and daily epidemiological, mobility, and online trend data. Preprocessing and machine-learning workflows support evaluation and prediction of anxiety, depression, post-traumatic stress disorder, and general psychological support needs, including potential system load.","Data in Brief 57 (2024) 110877  \nContents lists available at ScienceDirect  \nData in Brief  \njournal [homepage:](homepage: www.elsevier.com/locate/dib)[ www.elsevier.com/locate/dib](homepage: www.elsevier.com/locate/dib)  \nData Article  \nMachine learning-ready mental health datasets for evaluating psychological effects and system needs in Mexico city during the first year of the COVID-19 pandemic  \nCarlos Rodrigo Garibay Rubio a,∗, Katsuya Yamorib, Genta Nakano b, Astrid Renneé Peralta Gutiérrez c, Silvia Morales Chainé d, Rebeca Robles Garcíae, Edgar Landa-Ramírez f, Alexis Bojorge  \nEstrada g, Alejandro Bosch Maldonado h, Diana Iris Tejadilla Orozcoia Graduate School of Informatics, Kyoto University, Yoshidahonmachi, Sakyo Ward, Kyoto 606-8317, Japan b Disaster Prevention Research Institute, Gokasho, Uji, Kyoto 611-0011, Japan  \nc Independent researcher, Gokasho Sanbanwari 37, Obaku shiei jutaku, Uji, Kyoto 611-0011, Japan  \nd Faculty of Psychology, National University of Mexico, Circuito Ciudad Universitaria Avenida, C. U., 04510 Ciudad de México, Mexico  \ne Research Center for Global Mental Health, National Institute of Psychiatry “Ramón de la Fuente Muñiz”, Calz México-Xochimilco 101, Colonia, Huipulco, Tlalpan, 14370 Ciudad de México, CDMX, Mexico  \nf Ministry of Health, “Hospital General Dr. Manuel Gea González”, [Calz. de](Calz. de) Tlalpan 4800, Belisario Domínguez Secc 16, Tlalpan, 14050 Ciudad de México, CDMX, Mexico  \ng Ministry of Health, Psychiatric Services, Av. Marina Nacional 60, Tacuba, Miguel Hidalgo, 11410 Ciudad de México, CDMX, Mexico  \nh General Directorate of Community Attention, National Autonomous University of México, 04510 Mexico City, CDMX, Mexico  \ni Ministry of Health, Child Psychiatric Hospital “Juan N Navarro” Av. San Fernando 86, Belisario Domínguez Secc 16, Tlalpan, 14080 Ciudad de México, CDMX, Mexico  \na r t i c l e i n f o  \nArticle history:  \nReceived 9 April 2024  \nRevised 7 June 2024  \nAccepted 19 August 2024  \nAvailable online 28 August 2024  \n∗ Corresponding author.  \na b s t r a c t  \nThe 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  \nE-mail address: [garibay.rodrigo.42s@st.kyoto-u.ac.jp](garibay.rodrigo.42s@st.kyoto-u.ac.jp) (C.R. Garibay Rubio).  \n[https://doi.org/10.1016/j.dib.2024.110877](https://doi.org/10.1016/j.dib.2024.110877)  \n2352-3409/© 2024 The Author(s). Published by Elsevier Inc. This is an open access article under the CC BY-NC-ND license ([http://creativecommons.org/licenses/by-nc-nd/4.0/](http://creativecommons.org/licenses/by-nc-nd/4.0/))  \n2 C.R. Garibay Rubio, K. Yamori and G. Nakano et al. /Data in Brief 57 (2024) 110877  \nDataset link: Microtrends of mental health reactions for better decision making Generals (Original data)  \nDataset link: Mental_health_cdmx_2020 (Original data)  \nKeywords:  \nDisaster recovery curve Psychological response in disasters Mental health support systems Stress response in emergencies Acute stress response Earthquake early warning Pandemic mental health effects  \nonline mental health risk questionnaire, 349,202 emergency calls, and city epidemiological, mobility, and online trend data.  \nThe 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.  \nThe integration of the datasets, as well as t","cbCaiomNqa1VOgcR","https://ap.wps.com/l/cbCaiomNqa1VOgcR","pdf",1323557,3,1,13,"English","en",105,"# Data overview\n## Sources and sample coverage\n## Variables and measures\n# Data integration and modeling use\n## Preprocessing and optimization\n## Machine learning applications and validation\n# Data collection and availability\n## Online questionnaire and translation\n## Lifeline call service and time windows","[{\"question\":\"这些数据集主要用于评估哪些心理健康结果？\",\"answer\":\"可用于评估与预测焦虑、抑郁、创伤后应激障碍，以及一般心理支持需求，并推测潜在的系统负荷。\"},{\"question\":\"数据来源包括哪些类型的数据？\",\"answer\":\"包含在线心理健康风险问卷响应、应急热线呼叫统计，以及用于城市层面日常概况的流行病学、流动性和在线趋势数据。\"},{\"question\":\"数据收集的时间范围如何覆盖疫情早期？\",\"answer\":\"在线问卷覆盖 2020 年 4 月 13 日至 12 月 7 日；热线呼叫数据覆盖 2020 年 5 月 24 日至 12 月 31 日，并在地震事件周围加入了约 72 小时的时间窗口。\"}]","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 - Research dataset and system needs evaluation | PDF",1786002907,33,{"code":4,"msg":32,"data":33},"ok",{"site_id":25,"language":24,"slug":34,"title":13,"keywords":35,"description":14,"schema_data":36,"social_meta":87,"head_meta":89,"extra_data":91,"updated_unix":29},"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-research-dataset-and-system-needs-evaluation","",{"@graph":37,"@context":86},[38,54,69],{"@type":39,"itemListElement":40},"BreadcrumbList",[41,45,49,51],{"item":42,"name":43,"@type":44,"position":21},"https://docshare.wps.com","Home","ListItem",{"item":46,"name":47,"@type":44,"position":48},"https://docshare.wps.com/document/","Document",2,{"item":50,"name":12,"@type":44,"position":20},"https://docshare.wps.com/document/research-report/",{"item":52,"name":13,"@type":44,"position":53},"https://docshare.wps.com/document/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-research-dataset-and-system-needs-evaluation/128728/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":24,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":42,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-23","2026-08-06",true,{"@type":66,"interactionType":67,"userInteractionCount":20},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72,78,82],{"name":73,"@type":74,"acceptedAnswer":75},"这些数据集主要用于评估哪些心理健康结果？","Question",{"text":76,"@type":77},"可用于评估与预测焦虑、抑郁、创伤后应激障碍，以及一般心理支持需求，并推测潜在的系统负荷。","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"数据来源包括哪些类型的数据？",{"text":81,"@type":77},"包含在线心理健康风险问卷响应、应急热线呼叫统计，以及用于城市层面日常概况的流行病学、流动性和在线趋势数据。",{"name":83,"@type":74,"acceptedAnswer":84},"数据收集的时间范围如何覆盖疫情早期？",{"text":85,"@type":77},"在线问卷覆盖 2020 年 4 月 13 日至 12 月 7 日；热线呼叫数据覆盖 2020 年 5 月 24 日至 12 月 31 日，并在地震事件周围加入了约 72 小时的时间窗口。","https://schema.org",{"og:url":52,"og:type":88,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":90,"canonical":52},"index,follow",{"doc_id":7,"site_id":25},{"code":4,"msg":5,"data":93},[94,98,102,106,111,116,121,124,129,132,136],{"id":21,"doc_module":4,"doc_module_name":47,"category_name":95,"show_sort_weight":96,"slug":97},"Story & Novel",90,"story-novel",{"id":48,"doc_module":4,"doc_module_name":47,"category_name":99,"show_sort_weight":100,"slug":101},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":47,"category_name":103,"show_sort_weight":104,"slug":105},"Exam",70,"exam",{"id":107,"doc_module":4,"doc_module_name":47,"category_name":108,"show_sort_weight":109,"slug":110},5,"Comic",60,"comic",{"id":112,"doc_module":4,"doc_module_name":47,"category_name":113,"show_sort_weight":114,"slug":115},6,"Technology",50,"technology",{"id":117,"doc_module":4,"doc_module_name":47,"category_name":118,"show_sort_weight":119,"slug":120},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":47,"category_name":12,"show_sort_weight":122,"slug":123},30,"research-report",{"id":125,"doc_module":4,"doc_module_name":47,"category_name":126,"show_sort_weight":127,"slug":128},9,"Religion & Spirituality",20,"religion-spirituality",{"id":127,"doc_module":4,"doc_module_name":47,"category_name":130,"show_sort_weight":127,"slug":131},"World Cup","world-cup",{"id":133,"doc_module":4,"doc_module_name":47,"category_name":134,"show_sort_weight":133,"slug":135},10,"Lifestyle","lifestyle",{"id":137,"doc_module":4,"doc_module_name":47,"category_name":138,"show_sort_weight":107,"slug":139},19,"General","general"]