Comparing Assistive Technology Funding and Participation Outcomes in Disabled Youth Families

Authors

  • Natalie Yim Department of Psychology, Faculty of Social Sciences, Lingnan University, Hong Kong, Hong Kong SAR, China Author

Keywords:

Assistive Technology, Disability Registry, Social Participation, Funding Disparities, Participation Outcomes

Abstract

This study utilizes a longitudinal registry analysis to investigate the relationship between assistive technology funding pathways and multidimensional participation outcomes among families with disabled youth. Drawing on data from a comprehensive regional registry of two thousand four hundred and fifty youth-parent dyads over a five-year period, we examine how different funding mechanisms, including public programs, private insurance, charitable donations, and out-of-pocket payments, influence both the timeline of technology acquisition and subsequent social, academic, and community participation outcomes. Our findings reveal a critical paradox: while public funding systems provide essential financial protection, they are characterized by severe administrative delays that significantly compromise the developmental window for optimal technology integration. Conversely, private and out-of-pocket funding pathways facilitate rapid procurement but exacerbate socioeconomic disparities and place immense financial strain on low-income families. Multivariable regression analyses demonstrate that timely technology acquisition is a stronger predictor of academic and social participation than the specific type of technology received. These results highlight the urgent need for systemic policy reforms, including streamlined administrative workflows, universal basic technology provisions, and integrated funding models, to ensure equitable and prompt access to life-changing assistive interventions for all disabled youth.

References

1. Dispennette, A.K.; Schafer, M.A.; Shake, M.; Clark, B.; Macy, G.B.; Vanover, S.; Crandall, K.J. Effects of a Game-Centered Health Promotion Program on Fall Risk, Health Knowledge, and Quality of Life in Community-Dwelling Older Adults. Int. J. Exerc. Sci. 2019, 12, 1149–1160.

2. World Health Organization. Global Strategy on Digital Health 2020–2025, 1st ed.; World Health Organization: Geneva, Switzerland, 2021.

3. Park, S.-Y., & Loo, B. T. (2022). The use of crowdfunding and social media platforms in strategic start-up communication: A big-data analysis. International Journal of Strategic Communication, 16(2), 313–331.

4. Men, L. R. (2021). The impact of startup CEO communication on employee relational and behavioral outcomes: Responsiveness, assertiveness, and authenticity. Public Relations Review, 47(4), 102078.

5. Zhou, F. (2024). Utilizing social media platforms to foster entrepreneurial communication among start-up communities. El Profesional de La Información, 33(5), e330525.

6. Nigam, N., Benetti, C., & Johan, S. A. (2020). Digital start-up access to venture capital financing: What signals quality? Emerging Markets Review, 45, 100743.

7. Kaihlanen, A.M.; Gluschkoff, K.; Laukka, E.; Heponiemi, T. The information system stress, informatics competence and well-being of newly graduated and experienced nurses: A cross-sectional study. BMC Health Serv. Res. 2021, 15, 1096.

8. Karatzas, S.; Lazari, V.; Fouseki, K.; Pracchi, V.N.; Balaskas, E. Digital Twins-Enabled Heritage Buildings Management through Social Dynamics. J. Cult. Herit. Manag. Sustain. Dev. 2026, 16, 150–166.

9. Xu, P.; Sarris, G.; Jones, R.; Huthwaite, P. A Digital Twin-Based Framework for Reliability Estimation in Ultrasonic Guided Wave Structural Health Monitoring Systems with Temperature Variations. Mech. Syst. Signal Process. 2025, 235, 112848.

10. Madni, A.M.; Madni, C.C.; Lucero, S.D. Leveraging Digital Twin Technology in Model-Based Systems Engineering. Systems 2019, 7, 7.

11. Córdova, J. C., Victoria-Mas, J. S., & Altamirano Benítez, V. (2022). Strategic communication for startups: Analysis of its intervention in the use of social networks. Journal of Positive Psychology & Wellbeing, 6(1), 795–803.

12. Miles, M.B.; Huberman, A.M.; Saldaña, J. Qualitative Data Analysis: A Methods Sourcebook, 3rd ed.; SAGE Publications, Inc: Thousand Oaks, CA, USA, 2014.

13. Sousa, V.L.P.; Dourado Júnior, F.W.; Anjos, S.D.J.S.B.D.; Moreira, A.C.A. Nursing Teleconsultation in Primary Health Care: Scoping Review. Rev. Lat. Am. Enferm. 2024, 32, e4329.

14. Jirawattanasomkul, T.; Hang, L.; Srivaranun, S.; Likitlersuang, S.; Jongvivatsakul, P.; Yodsudjai, W.; Thammarak, P. Digital Twin-Based Structural Health Monitoring and Measurements of Dynamic Characteristics in Balanced Cantilever Bridge. Resilient Cities Struct. 2025, 4, 48–66.

15. Men, L. R., Chen, Z. F., & Ji, Y. G. (2018). Walking the talk: An exploratory examination of executive leadership communication at startups in China. Journal of Public Relations Research, 30(1/2), 35–56.

16. Li, H.; Tao, S.; Sun, S.; Xiao, Y.; Liu, Y. The relationship between health literacy and health-related quality of life in Chinese older adults: A cross-sectional study. Front. Public Health 2024, 12, 1288906.

17. Buchanan, C.; Howitt, M.L.; Wilson, R.; Booth, R.G.; Risling, T.; Bamford, M. Predicted Influences of Artificial Intelligence on the Domains of Nursing: Scoping Review. JMIR Nurs. 2020, 3, e23939.

18. Chiachío, M.; Megía, M.; Chiachío, J.; Fernandez, J.; Jalón, M.L. Structural Digital Twin Framework: Formulation and Technology Integration. Autom. Constr. 2022, 140, 104333.

19. Bado, M.F.; Tonelli, D.; Poli, F.; Zonta, D.; Casas, J.R. Digital Twin for Civil Engineering Systems: An Exploratory Review for Distributed Sensing Updating. Sensors 2022, 22, 3168.

20. Cohen, J. Statistical Power Analysis for the Behavioral Sciences; Lawrence Erlbaum Associates: Hillsdale, NJ, USA, 1988.

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Published

2026-01-20

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Articles