Transforming Public Health: IPPH Strategic Plan, 2022-2026
Equity Science

Transforming Public Health: IPPH Strategic Plan, 2022-2026
Equity Science

Overarching goal

Develop next-generation data-driven approaches that address the full range of factors that perpetuate experiences of disadvantage and discrimination and affect population health.

IPPH's Equitable AI priority area sought to build capacity at the intersection of AI and population and public health research. After accomplishing much of what we set out to do, the priority area needed to evolve to tackle the limitations of current data science approaches. An expanded scope would allow us to account for the complex factors that shape our lives and experiences and lead to health inequities.

Our Equity Science priority area will strengthen the health research ecosystem by leveraging social justice and intersectionality frameworks in the development of data-driven methods for new solutions that are anti-colonial, anti-racist, and anti-ableist. Through strategic partnerships, IPPH aims to activate capacity-building opportunities in equity science that centre community voices across the population and public health research spectrum, catalyzing larger shifts over time.

Equity Science Objective Strategies Embedding equity Impact
Research excellence Advance the development and use of data-driven approaches that meaningfully centre perspectives of people facing conditions of marginalization

Drive the invention and use of community-led and community-based data-driven approaches for health solutions

Develop clear standards and best practices for doing data-driven, community-centred research

Intentionally elevate perspectives of people with lived and living experience to co-develop new approaches for how knowledge and solutions are produced Data-driven research approaches and solutions created in active partnership with communities for self-determined priorities
Capacity-building Equip a cadre of researchers with next-generation tools to unpack the root drivers of structural discrimination and disadvantage and identify solutions to address them

Support transdisciplinary training and mentorship opportunities for data-driven research grounded in critical theoretical frameworks with an eye to long-term partnership-driven mechanisms

Leverage previous Equitable AI investments that focus on AI, big data and public health equity

Train researchers to address equity, power, intersectionality and social justice using inclusive data-driven approaches Many researchers equipped to address structural drivers of inequities
Knowledge mobilization Mobilize a coalition of partners to catalyze, support, scale-up and mainstream the use of data-driven approaches for equity

Champion the value of equity science and marshal the elements needed in the research ecosystem to support it

Link community-led tables with decision-makers and researchers to enable data-driven research that can inform policy-making

Expand awareness of how to design and apply data-driven approaches in research and research policy for equitable outcomes Widespread use of data-driven equity approaches and a transformed research culture that supports and incentivizes researchers to use them
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