Health Inc: Corporations, capitalism, and commercial determinants of health - Seminar 6
April 19 | 12:00 PM – 1:00 PM EDT

Seminar six: A critical examination of Food Industry Partnerships and Practices

The corporation is arguably the most powerful social and economic institution globally, with unprecedented power to shape scientific evidence, public policy, and lifestyles such as dietary behaviours. This seminar will feature two speakers who will 1) critically interrogate the positioning of the food industry as a legitimate partner for the public sector and the resulting impacts; and, 2) explore the challenges with voluntary commitments, which maximize flexibility for the food industry, but that can impede the creation of a healthier food supply and food environment.

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An introduction to Bayesian Meta-​Analysis
April 20 | 12:00 PM – 1:00 PM EDT

Join us for the last DLSPH Health Data Working Group event with Juan Pablo Diaz Martinez on Bayesian Meta-Analysis.

Meta-analysis (MA), the pooling of evidence from independent sources, especially randomised controlled trials (RCTs) is now common in the medical research literature. Software advances have contributed to the widespread use of meta-analytic techniques. In this workshop, we will show how MA models can be seen as generalized linear models. We will also see how to fit these models under a Bayesian perspective.

Juan Pablo Diaz Martinez works as a biostatistician at UHN and PhD Candidate at the University of Toronto.

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Conversations in Data Science: Framework for Responsible Machine Learning
April 20 | 4:00 PM – 5:00 PM EDT

How can machine learning improve population health effectiveness, enhance health system efficiency, and support public health decision-making? What can we do to address the impact, governance, ethics, and accountability of these automated decision-making technologies? To explore these questions, the Data Science Interdisciplinary Research Cluster at the University of Toronto’s Dalla Lana School of Public Health invites Statistics Canada to discuss the responsible development of automated processes and provide guidance on the ethical use and implementation of machine learning.

Statistics Canada will present its responsible machine learning framework, followed by a panel discussion on what this framework means for machine learning technologies and applications in population health and health system research. Register to join the conversation.

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