Gender Aware PD Care 185 6 Supplement 6. Continued Post-workshop reflective questions Equity Centred Design - Notion & Reflect Phase Reflect on the following principles and think about their role and relevance during the workshop and upcoming data analyses. Principles of Inclusive and Equity Centred Design: Recognize Exclusion Checking personal biases, including those around disabilities and related limitations, to avoid conscious or unconscious exclusionary decisions. Learn from Diversity Letting research insights be driven by the unique perspectives of diverse individuals and the way they adapt to experiences not originally designed for them. Solve for One, Extend to Many Focusing on what’s universally important to all humans and understanding the power of solving along the continuum of permanent disabilities to temporary disabilities (e.g., broken arm) to situational impairments (e.g., loud crowd affecting your hearing). Design at Margins Building for marginalised communities who are most hurt by oppression, and bringing them into the design process. Start with Self Recognizing personal mental models, including how biases and assumptions impact solution design on both a conscious and unconscious level. Cede Power Providing power to underrepresented individuals that are brought into the design process, and making it a safe space for speaking truth to injustices. Make the Invisible Visible Recognizing, explicitly calling out, and actively challenging hegemonic practices that have historically advantaged dominant groups over marginalised groups. Speak to the Future Finding new language to complement the design of a new, equitable future, such as defining an innovation as an increase in equity and reduction of racism. 7 What social demographics should we be aware of in our study population? Who might we be excluding? 8 How could identities within our team (have) influence(d) or impact(ed) data collection and data analysis decisions? What do we need to be aware of moving forward towards the data analysis? 9 During data analysis: How can we ensure we are focussing on an actual need or question that this community has, rather than one we may be incorrectly perceiving they have (implicit/ explicit biases)? 10 During data analysis: How can we ensure we are focussing on an actual need or question that this community has, rather than one we may be incorrectly perceiving they have (implicit/ explicit biases)?
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