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Presentation summary
An ongoing commitment to evidence-based practice is essential for high-quality care; however, real-world challenges can hinder its implementation (Speech Pathology Australia, 2021).
Appreciative Inquiry (AI) offers a framework for translating research into practice by engaging staff in collaborative reflection, using existing strengths, to integrate evidence in a way that is practical, flexible, and contextually relevant (Bushe, 2011; Richer et al., 2009; Cooperrider et al., 2008).
AI was implemented by NT community-based speech pathologists to facilitate the translation of research to the NT context in two quality improvement projects designed for long-term sustainable change.
This session explores AI for speech pathology. Participants will learn about and experience the four phases of AI —Discovery, Dream, Design, and Destiny— to illustrate how each phase fosters motivation and innovation through collaborative, reflective discussions. It will demonstrate how innovation can be achieved within the existing resources of a workplace.
The session will also reference the experiences, lessons and outcomes from two AI projects driven by NT clinical speech pathologists. It will do this by showing how each phase was used to translate Developmental Language Disorder (DLD) and multilingual research and best practice principles into day-to-day work across NT urban, rural and remote areas, within the existing resources (Bishop et al., 2016; Bishop et al., 2017; The DLD Project, 2021; Speech Pathology Australia, 2016). Deliverables that reflect the shared vision of NT speech pathologists, ensuring practices remain flexible, culturally responsive, and in alignment with strategic direction will be shown.
AI proved to be an invaluable tool for translating research on DLD and multilingualism into practice, while respecting clinical expertise, client values/preferences as well as honouring and centring the cultural and linguistic diversity of the NT. This approach serves as a potential model for other workplaces with similar challenges for translating the evidence for long-term sustainable change. |
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