Community perspectives on barriers to injury care in Northern Malawi: a three delays framed assessment using focus groups and photovoice

Autor: John Whitaker, Ella Togun, Levie Gondwe, Donaria Zgambo, Abena S. Amoah, Albert Dube, Rory Rickard, Andrew JM Leather, Justine Davies

Publikováno v: BMC Health Services Research, Vol 24, Iss 1, Pp 1-17 (2024)

Abstract Introduction The global burden of injury is huge, falling disproportionately on poorer populations. The benefits of qualitative research in injury care are recognised and its application is growing. We used a novel application of focus group

Externí odkaz: https://doaj.org/article/84b9633b63224e6db969bb0958be2077

Generalization, Expressivity, and Universality of Graph Neural Networks on Attributed Graphs

Autor: Rauchwerger, Levi, Jegelka, Stefanie, Levie, Ron

We analyze the universality and generalization of graph neural networks (GNNs) on attributed graphs, i.e., with node attributes. To this end, we propose pseudometrics over the space of all attributed graphs that describe the fine-grained expressivity

Externí odkaz: http://arxiv.org/abs/2411.05464

Targeting food parenting practices to prevent early child obesity risk requires a different approach in families with a lower socioeconomic position

Autor: Junilla K. Larsen, Levie T. Karssen, Shelley M. C. van der Veek

Publikováno v: Frontiers in Public Health, Vol 10 (2022)

Externí odkaz: https://doaj.org/article/ba7f259e9ee846c7890e7f2ad7a2aeba

Transforming Obesity Prevention for CHILDren (TOPCHILD) Collaboration: protocol for a systematic review with individual participant data meta-analysis of behavioural interventions for the prevention of early childhood obesity

Autor: Finn Rasmussen, Hein Raat, Cristina Palacios, Barry J Taylor, Lisa Askie, Alison Hayes, Cindy-Lee Dennis, Karen Campbell, Wendy Smith, Luke Wolfenden, Sharleen O’Reilly, Eva Corpeleijn, Maria Bryant, Chris Rissel, Denise O’Connor, Paul Chadwick, Jessica Thomson, Anna Lene Seidler, Kylie E Hunter, Rachael W Taylor, Angie Barba, Kristy Robledo, Ken Ong, Carolina González Acero, Ana Pérez-Expósito, Kylie D Hesketh, Rebecca K Golley, David Espinoza, Ken K Ong, Sarah Taki, Rachael Taylor, Louise A Baur, Li Ming Wen, Seema Mihrshahi, Emily Oken, Barry Taylor, Ian Marschner, Junilla K Larsen, Kylie Hesketh, Rajalakshmi Lakshman, Amanda L Thompson, Sharleen L O'Reilly, Jonathan Williams, Charles Wood, Alison J Hayes, Kaumudi Joshipura, Hongping Xia, Lynne Daniels, Rebecca Byrne, Alison Karasz, Rebecca Golley, Kaumudi J Joshipura, Angela Webster, Nina Cecilie Øverby, Brittany J Johnson, Mason Aberoumand, Sol Libesman, Kristy P Robledo, Charles T Wood, Lukas P Staub, Michelle Sue-See, Ian C Marschner, Jessica L Thomson, Vera Verbestel, Sarah-Jeanne Salvy, Levie T Karssen, Finn E Rasmussen, Mary Jo Messito, Rachel S Gross, Ian M Paul, Heather M Wasser, Claudio Maffeis, Ata Ghaderi, Jinan C Banna, Maribel Campos Rivera, Ana B Pérez-Expósito, Jennifer S Savage, Margrethe Røed, Michael Goran, Kayla de la Haye, Stephanie Anzman-Frasca, Kylie Hunter, Brittany Johnson, Louise Baur, Lukas Staub, Shonna Yin, Lee Sanders, Amanda Thompson, Ana Maria Linares, Cathleen Odar Stough, Christine Helle, Eliana Perrin, Heather Wasser, Jinan Banna, Kayla dela Haye, Levie Karssen, Nina Øverby, Rachel Gross, Russell Rothman, Wendy A Smith, Alexander Fiks, Deborah Jacobvitz, Jennifer Savage Williams, Márcia Regina Vitolo, Elizabeth Widen

Publikováno v: BMJ Open, Vol 12, Iss 1 (2022)

Externí odkaz: https://doaj.org/article/091bcf02122241f490982c3cf8dab1fd

Equivariant Machine Learning on Graphs with Nonlinear Spectral Filters

Autor: Lin, Ya-Wei Eileen, Talmon, Ronen, Levie, Ron

Equivariant machine learning is an approach for designing deep learning models that respect the symmetries of the problem, with the aim of reducing model complexity and improving generalization. In this paper, we focus on an extension of shift equiva

Externí odkaz: http://arxiv.org/abs/2406.01249