Browsing by Author "Neal , Tiffany"

Now showing 1 - 6 of 6
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    Evaluating HANDS Para-Training attendees satisfaction (2024-2025)
    (2025-05-09) Singamsetty, Archita; Neal , Tiffany; Gottipati, Mounika; Swiezy, Naomi
    This project evaluated participant engagement and satisfaction across intensive training programs at HANDS in Autism® through systematic tracking and analysis of REDCap survey data. Responsibilities included monitoring participation across multiple platforms (REDCap, Canvas, Excel), calculating module scores, issuing completion certificates, and creating Excel-based dashboards to visualize satisfaction data. A highlight of the project was the HANDS Para-Training, which received a 95% favorable rating, with 80% of participants indicating they “Completely Liked” the sessions. The intern also maintained COI/CITI compliance logs, supported in-person training logistics, and collaborated with team members to ensure data accuracy and documentation integrity. The experience strengthened technical and organizational skills while providing actionable insights for improving training evaluation processes and future intern onboarding.
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    Evaluating the Autism Knowledge Gains, Retention, and Differences Across Participant Roles in HANDS in Autism Summer Intensive Trainings (2013–2024)
    (2025-05-09) Simhambhatla, Aruna Prasanna; Neal , Tiffany; Gottipati, Mounika; Swiezy, Naomi
    This project analyzed over a decade’s worth of Autism Knowledge Survey – Revised (AKS-R) data collected during HANDS in Autism® Summer Intensive Trainings from 2013 to 2024. Using REDCap, Python, and Power BI, the study assessed knowledge improvements across pre-training, post-training, and follow-up periods. The results showed statistically significant increases in autism-related knowledge immediately following training, with positive retention trends over time. The analysis also revealed variations in knowledge gains based on participant roles, experience levels, and training years. Notably, 2014 and 2023 demonstrated the highest improvements, highlighting peak training effectiveness. Through structured scoring, statistical testing, and predictive modeling, the project reinforced the long-term impact of targeted, interdisciplinary autism education and provided actionable insights for enhancing future training strategies.
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    Identifying Behavioral Severity Trends and Intervention Targets in Autism Care
    (2025-05-09) Kuta, Priyanka Reddy; Neal , Tiffany; Devarapalli , Baby Amulya; Swiezy, Naomi
    This project analyzed behavioral severity scores of adolescents with Autism Spectrum Disorder (ASD) as part of the Coordinated Care Project led by HANDS in Autism®, in partnership with NDI and Damar Services. Using Python and Power BI, the practicum examined pre-admission and post-discharge data to assess the impact of evidence-based interventions. Results indicated a reduction in average behavioral severity scores from 6 to 4.3 over six months post-discharge. Correlation analysis revealed strong behavior clusters, particularly around self-care tasks like getting dressed and bathing, highlighting them as critical targets for intervention. Through statistical testing and interactive dashboard development, this project supported data-informed decision-making and strengthened the intern’s skills in behavioral health analytics, interdisciplinary research, and clinical outcome evaluation.
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    Measuring the Effectiveness of Stakeholder Participation in State-Led Efforts to Improve Autism Awareness in Indiana​
    (2025-05-09) Sravanam, Naga Hemasree; Neal , Tiffany; Ogunmola, Botiwuoluwa; Smith, Julie Burk; Swiezy, Naomi
    This project analyzed stakeholder engagement and satisfaction trends from the Indiana Interagency Autism Coordinating Council (IIACC) meetings between 2018 and 2024. Conducted through HANDS in Autism®, the study involved cleaning and analyzing multi-year survey data using Python, Excel, and Power BI. Key findings revealed a decline in meeting participation—dropping 75% over the six-year period—despite a recent 8% increase in satisfaction scores for workgroup activities in 2024. The analysis suggests that while content satisfaction improved, overall engagement and leadership participation decreased significantly after 2022. These insights informed recommendations for enhancing stakeholder feedback loops, improving meeting structures, and establishing consistent attendance tracking systems. This project strengthened competencies in data storytelling, visualization, and strategic evaluation for public health and community engagement efforts in autism services.
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    Tracking Caregiver Strain Across Intervention Stages: A Data-Driven Approach to Emotional and Logistical Burden in Autism Support
    (2025-05-09) Vontimitta, Mahitha; Neal , Tiffany; Devarapalli, Baby Amulya; Swiezy, Naomi
    This project analyzed caregiver burden using longitudinal data from the Caregiver Strain Questionnaire (CSQ), collected across seven post-discharge timepoints as part of the Coordinated Care Team at HANDS in Autism®. Data from REDCap was cleaned and preprocessed using Python, then visualized with Power BI to uncover trends in objective, internalized, and externalized strain. Results showed peak caregiver burden during the preadmission and early post-discharge phases, with gradual improvement over time. However, emotional strain persisted longer than practical burdens. Visual dashboards allowed for real-time comparison of severity types and helped identify intervention windows. The project emphasized the value of data-driven tools in understanding healthcare challenges and reinforced the need for early support strategies, long-term emotional care, and improved caregiver retention in follow-up studies.
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    Unveiling Connections: Exploring Patient Behaviors and Traumatic Brain InjuryMadhuhasa Battula in Autistic Youth at the Indiana NDI Exploratory Project
    (2023-09) Battula, Madhuhasa; Neal , Tiffany; Deodhar , Aditi; Darsanapu, Archana; Swiezy, Naomi
    The Indiana NDI (Neurodevelopmental Institute) Exploratory Project is an initiative that embodies a cooperative effort involving multidisciplinary experts under the auspices of the Indiana Family and Social Services Administration (FSSA), specifically the Division of Mental Health and Addiction (DMHA). In our study, a total of 58 NDI clients were examined. Data was extracted from the RedCap database (Patridge & Bardyn, 2018). This enabled us to assess the potential of various patient behaviors to indicate the presence of Traumatic Brain Injury (TBI) using predictive models, establishing a statistically significant correlation between certain behaviors and the occurrence of TBI. A Heat map exhibiting a positive correlation between patient behaviors and TBI was shown. The data analysis indicates a statistically significant positive correlation between multiple patient behaviors and the presence of traumatic brain injury (TBI), as supported by high model accuracies. This suggests that these patient behaviors may serve as indicators of the presence of TBI and warrant further investigation. Examining the long-term consequences of TBI on behavior is essential to gain a comprehensive understanding of the dynamics involved.