Browsing by Subject "Neurodevelopmental Disorders"

Now showing 1 - 2 of 2
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    "I Bring Her up with Love': Perspectives of Caregivers of Children with Neurodevelopmental Delays in Western Kenya
    (2023-01) Heng, Yi Yan; Nafiseh, Amira; Oyungu, Eren; Ombitsa, Ananda Roselyne; Cherop, Carolyn; McHenry, Megan S.
    Objective: This study aims to understand the challenges and perspectives of caregivers with neurodevelopmental delays (NDD) in rural Kenya. Methods: Semi-structured interviews and the Affiliate Stigma Scale were administered to the primary caregivers of children with NDDs recruited from the communities near Eldoret, Kenya. Constant comparison and triangulation methods were used to inductively develop relevant themes and concepts. Results: Sixteen caregivers participated. Challenges, which included hardships related to safety and supervision, challenging emotions and financial difficulties, were compounded by a lack of social support and community stigma towards these children. However, caregivers still felt deep love for their children, desired acceptance from the community and found sources of strength from faith and religious institutions. Conclusion: The study uncovered crucial insights into the perspectives of caregivers within this population and revealed a paucity of disability awareness and understanding within the community, possibly informing future programmes and intervention policies.
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    Predicting Traumatic Brain Injury Through Behavioral Pattern Analysis in Youth with Autism
    (2023-08) Battula, Madhuhasa; Neal, Tiffany; Deodhar, Aditi; Swiezy, Naomi
    This practicum, conducted at HANDS in Autism® in collaboration with the Indiana NeuroDiagnostic Institute (NDI), explored the relationship between patient behavioral profiles and the presence of Traumatic Brain Injury (TBI) in individuals with Autism Spectrum Disorder (ASD). Using a combination of pre-admission data from Cerner and post-discharge data from REDCap, a comprehensive dataset of 58 patients was coded and analyzed to identify behavioral patterns. Prediction models, including Logistic Regression and Random Forest, were developed in Python to assess the likelihood of TBI based on specific behavior indicators. Results revealed statistically significant correlations between certain behavioral patterns and the presence of TBI. This work supports the potential for predictive modeling to improve early identification and intervention strategies for patients with ASD and co-occurring neurological conditions.