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A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
Published on: January 11, 2020
«Dar más tiempo a las personas y tomarse el tiempo para explicar las cosas adecuadamente»: Experiencias de cribado
Iva Strnadová1,2,3, Julie Loblinzk Refalo1,3, Rosalie Power4
1School of Education, Faculty of Arts, Design and Architecture, University of New South Wales, Sydney, New South Wales, Australia.
Background:
People with intellectual disability have 70% lower cervical screening participation rates. Limited research has captured their experiences, particularly in Australia.
Methods:
This qualitative study used inclusive co-production and trauma-informed approaches. Eleven people with intellectual disability (aged 26-65) from New South Wales participated in semi-structured interviews and arts-based body mapping, analysed using reflective thematic analysis.
Findings:
Four themes emerged: (1) healthcare interactions ranged from empowering to traumatic, with communication failures and rushed procedures; (2) tensions around informed consent and support roles; (3) gaps in accessible information and systematic reminders and (4) how past trauma and competing health priorities influenced screening. Participants recommended Easy Read materials, adequate appointment time, trauma-informed care and genuine choice in healthcare decisions.
Conclusions:
Women with intellectual disability encounter systematic barriers to cervical screening encompassing communication failures, inadequate consent and lack of trauma-informed care. Their recommendations provide actionable solutions for healthcare reform.
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