{"quality_controlled":"1","oa_version":"Published Version","date_created":"2024-02-14T15:11:48Z","title":"A framework for grassroots research collaboration in machine learning and global health","publication_status":"published","publication":"1st Workshop on Machine Learning & Global Health","year":"2023","oa":1,"date_published":"2023-03-02T00:00:00Z","article_processing_charge":"No","_id":"14993","conference":{"end_date":"2023-05-05","name":"ICLR: International Conference on Learning Representations","location":"Kigali, Rwanda","start_date":"2023-05-05"},"main_file_link":[{"url":"https://openreview.net/forum?id=jHY_G91R880","open_access":"1"}],"language":[{"iso":"eng"}],"abstract":[{"lang":"eng","text":"Traditional top-down approaches for global health have historically failed to achieve social progress (Hoffman et al., 2015; Hoffman & Røttingen, 2015). Recently, however, a more holistic, multi-level approach termed One Health (OH) (Osterhaus et al., 2020) is being adopted. Several sets of challenges have been identified for the implementation of OH (dos S. Ribeiro et al., 2019), including policy and funding, education and training, and multi-actor, multi-domain, and multi-level collaborations. These exist despite the increasing accessibility to\r\nknowledge and digital collaborative research tools through the internet. To address some of these challenges, we propose a general framework for grassroots community-based means of participatory research. Additionally, we present a specific roadmap to create a Machine Learning for Global Health community in Africa. The proposed framework aims to enable any small group of individuals with scarce resources to build and sustain an online community within approximately two years. We provide a discussion on the potential impact of the proposed framework for global health research collaborations."}],"month":"03","publisher":"OpenReview","citation":{"ama":"Currin C, Asiedu  MN, Fourie C, et al. A framework for grassroots research collaboration in machine learning and global health. In: <i>1st Workshop on Machine Learning &#38; Global Health</i>. OpenReview; 2023.","ista":"Currin C, Asiedu  MN, Fourie C, Rosman B, Turki H, Lambebo Tonja A, Abbott J, Ajala M, Adedayo SA, Emezue CC, Machangara D. 2023. A framework for grassroots research collaboration in machine learning and global health. 1st Workshop on Machine Learning &#38; Global Health. ICLR: International Conference on Learning Representations.","ieee":"C. Currin <i>et al.</i>, “A framework for grassroots research collaboration in machine learning and global health,” in <i>1st Workshop on Machine Learning &#38; Global Health</i>, Kigali, Rwanda, 2023.","chicago":"Currin, Christopher, Mercy Nyamewaa Asiedu , Chris Fourie, Benjamin Rosman, Houcemeddine Turki, Atnafu Lambebo Tonja, Jade Abbott, et al. “A Framework for Grassroots Research Collaboration in Machine Learning and Global Health.” In <i>1st Workshop on Machine Learning &#38; Global Health</i>. OpenReview, 2023.","short":"C. Currin, M.N. Asiedu , C. Fourie, B. Rosman, H. Turki, A. Lambebo Tonja, J. Abbott, M. Ajala, S.A. Adedayo, C.C. Emezue, D. Machangara, in:, 1st Workshop on Machine Learning &#38; Global Health, OpenReview, 2023.","mla":"Currin, Christopher, et al. “A Framework for Grassroots Research Collaboration in Machine Learning and Global Health.” <i>1st Workshop on Machine Learning &#38; Global Health</i>, OpenReview, 2023.","apa":"Currin, C., Asiedu , M. N., Fourie, C., Rosman, B., Turki, H., Lambebo Tonja, A., … Machangara, D. (2023). A framework for grassroots research collaboration in machine learning and global health. In <i>1st Workshop on Machine Learning &#38; Global Health</i>. Kigali, Rwanda: OpenReview."},"department":[{"_id":"TiVo"}],"status":"public","author":[{"full_name":"Currin, Christopher","first_name":"Christopher","last_name":"Currin","orcid":"0000-0002-4809-5059","id":"e8321fc5-3091-11eb-8a53-83f309a11ac9"},{"full_name":"Asiedu , Mercy Nyamewaa","last_name":"Asiedu ","first_name":"Mercy Nyamewaa"},{"first_name":"Chris","last_name":"Fourie","full_name":"Fourie, Chris"},{"last_name":"Rosman","full_name":"Rosman, Benjamin","first_name":"Benjamin"},{"full_name":"Turki, Houcemeddine","first_name":"Houcemeddine","last_name":"Turki"},{"last_name":"Lambebo Tonja","full_name":"Lambebo Tonja, Atnafu","first_name":"Atnafu"},{"last_name":"Abbott","full_name":"Abbott, Jade","first_name":"Jade"},{"full_name":"Ajala, Marvellous","first_name":"Marvellous","last_name":"Ajala"},{"last_name":"Adedayo","full_name":"Adedayo, Sadiq Adewale","first_name":"Sadiq Adewale"},{"last_name":"Emezue","full_name":"Emezue, Chris Chinenye","first_name":"Chris Chinenye"},{"first_name":"Daphne","full_name":"Machangara, Daphne","last_name":"Machangara"}],"date_updated":"2024-02-28T12:12:00Z","day":"02","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","acknowledgement":"Houcemeddine Turki’s contributions to this final output have been funded through the Adapting\r\nWikidata to support clinical practice using Data Science, Semantic Web and Machine Learning\r\nproject, which is part of the Wikimedia Research Fund maintained by the Wikimedia Foundation in San Francisco, California, United States of America.","type":"conference"}