“Do It by Myself” or Autonomy, Participation, and Assistive Devices and Technology Needs of Children and Youth With Disabilities: Text Mining Analysis of a National Survey in France - Summary - MDSpire
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Autonomy, Engagement, and the Requirements for Assistive Technologies Among Disabled Children and Adolescents: A Text Mining Study of a National Survey in France
To uncover actionable knowledge related to difficult life situations for which innovative solutions could be useful, complementing previous human-led qualitative research.
Approach:
Survey Development: Developed the 'Innovation for Participation' survey targeting children and youth with disabilities, their relatives, and professionals involved in their care to identify activity limitations and participation restrictions.
Data Analysis: Utilized French-language text mining techniques to analyze survey responses, aiming to reveal insights and compare perspectives among children, relatives, and professionals.
Key Findings:
Difficulties in participation were frequently highlighted, particularly in recreational activities, mobility, and social interactions, as revealed through text mining analysis.
Proposed solutions included personal assistive devices, high-tech devices, and adaptations to the environment, reflecting the needs expressed by participants.
Human assistance in daily life was emphasized as a necessity, indicating a gap in available support.
Interpretation:
The study aimed to enrich understanding of the needs of children and youth with disabilities through a combined qualitative and text mining approach, highlighting the value of integrating user perspectives.
Limitations:
The study was based on a French-language survey, which may limit the applicability of findings to non-French speaking contexts.
Challenges included ensuring comprehensive engagement across diverse disability types and the potential biases in self-reported data.
Conclusion:
The findings highlight the importance of user engagement in the development of assistive technologies and demonstrate the potential of text mining to uncover actionable insights.
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