Smart Model To Reduce Employment Barriers For Women With Visual Impairments
DOI:
https://doi.org/10.29284/qtkfh321Keywords:
Job, barriers, multidimensional factor, smart model, visual impairmentAbstract
This research examines the labor market marginalization of women with visual impairments (VI), who experience a 57% inactivity rate compared to 40% among men. Disparity is exacerbated by structural disability biases, employer indifference, discrimination, inadequate accessibility, and limitations in personal mobility and transportation. Standard recruitment and evaluation processes frequently overlook candidates with visual impairments, while physical workspaces are often poorly designed. The central research question addresses how to effectively connect women with VI to employment despite these pervasive barriers. A literature review identifies the historical legacy of the religious model of disability and persistent employer indifference as key contributing challenges. In response, this study proposes a four-stage methodological framework: first, to identify multidimensional factors by age cohort; second, to perform data analysis; third, to conduct clustering; and fourth, to design a smart process model. The results yield 60 belief dimensions, 3 multifactor associations, and 3 distinct clusters. Smart model consists of three integrated processes: 1) creating a labor profile for a woman with VI, paired with personalized job recommendations; 2) proposing reasonable adjustments to both job requirements and the company's physical workspace; and 3) facilitating the application process. The model's effectiveness is tested in two ways, first, a comparative analysis with current free AI tools (Copilot and Gemini) demonstrates its superior utility and benefits. Second, testing personal characteristics of women with VI provides a realistic assessment for profile generation. The study underscores the importance of fostering cultural awareness within companies, providing a foundation for future initiatives.
References
1. Wang, J.; Wang, S. and Zhang, Y. (2023). Artificial intelligence for visually impaired. School of Computing and Mathematical Sciences, University of Leicester, Leicester LE1 7RH, UK b Department of Information Systems, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah 21589, Saudi Arabia. https://www.sciencedirect.com/science/article/pii/S0141938223000240
2. UN WOMEN (2023). No woman left behind: Closing the gender and inclusion gap in eye health: Why improving women’s eye health is essential to achieving the SDGs
4. Coffey, M., Coufopoulus, A. y Kinghorn, K. (2014). Barriers to employment for visually impaired women. International Journal of Workplace Health Management, DOI: 10.1108/IJWHM-06-2013-0022. https://www.researchgate.net/publication/265733053_Barriers_to_employment_for_visually_impaired_women
5. Cmar, J. and Steverson, A. (2021). Job-Search Activities, Job-Seeking Barriers, and Work Experiences of Transition-Age Youths With Visual Impairments. Journal of Visual Impairment and Blindness, Article2021.
DOI: 10.1177/0145482X211059182Copy to clipboard. The National Research and Training Center on Blindness and Low Vision, Mississippi State University, Mississippi State, MS, United States
6. Ortega, A. y Ramirez, J. (2025). Inclusión laboral de personas con discapacidad: desafíos y oportunidades. Revista de estudios contemporáneos del Sur Global. ISSN 2697-3677Vol. 6 No. 17
7. Yaqoob, U.; Rana, Z. and Nazeer, B. (2025). P-ISSN 2664-0422Pakistan Social Sciences ReviewJuly-September2025, Vol. 9, No.3O-ISSN 2664-0430
8. Banco Mundial. 2021. Inclusión de las personas con discapacidad en América Latina y el Caribe: Un camino hacia el desarrollo sostenible. Resumen ejecutivo. Washington, DC: Banco Mundial.
9. UNICEF (2025). Personas con discapacidad en Perú: Costos que enfrentan para participar en igualdad de condiciones.
10. Ananian, S., Dellaferrera, G. (2024). A study on the employment and wage outcomes of people with disabilities, ILO Working Paper 124 (Geneva, ILO).
https://doi.org/10.54394/YRCN8597
11. Ministerio de Derechos Sociales (2024). MPULSA IGUALDAD: Análisis comparativo de la Asistencia Personal en Europoa.
12. INEI (2019). Perfil sociodemográfico de la población con discapacidad, 2017. Perú. https://www.inei.gob.pe/media/MenuRecursivo/publicaciones_digitales/Est/Lib1675/libro.pdf
13. Castle CL (2024) Attitudes of employers towards people with visual impairment: a scoping review. Front. Rehabil. Sci. 5:1383984. doi: 10.3389/fresc.2024.1383984.
14. UNESCO. (2020). Resumen y recomendaciones para generar condiciones favorables para la inclusión laboral de personas con discapacidad con énfasis en las mujeres. Primera edición, 2020. Organización de las Naciones Unidas para la Educación, la Ciencia y la Cultura (UNESCO).
15. UNOPS (2020). Infrastructure for gender equality and the empowerment of women. https://content.unops.org/publications/UNOPS-Infrastructure-for-Gender-Equality-and-the-Empowerment-of-women.pdf.
16. Adnin, R. and Das, M. (2024). I look at it as the king of knowledge: How Blind People Use and Understand Generative AI Tools. The 26th International ACM SIGACCESS Conference on Computers and Accessi bility (ASSETS ’24), October 27–30, 2024, St. John’s, NL, Canada. ACM, New York, NY, USA, 14 pages.
ttps://doi.org/10.1145/3663548.3675631
17. Elshaer, I.; AlNajdi, S.; Salem, M. (2025). Sustainable AI Solutions for Empowering Visually Impaired Students: The Role of Assistive Technologies in Academic Success. Sustainability 2025, 17, 5609. https:// doi.org/10.3390/su17125609.
18. Naayini, P.; Myakala, P. and Bura, C. (2025). AI-powered assistive technologies for visual impairment. arXiv:2503.15494v1 [cs.HC] 14 Jan 2025.
https://arxiv.org/pdf/2503.15494.
19. Soni, M.; Gomathi, S.; and Adhyaru, Y. (2020). “Natural language processing for the job portal enhancement,” 2020, doi: 10.1109/ICSSS49621.2020.9202046.
20. Mishra, R.; and Rathi, S. (2021). “Enhanced DSSM (deep semantic structure modelling) technique for job recommendation,” J. King Saud Univ. Comput. Inf. Sci., Jul. 2021, doi: 10.1016/J.JKSUCI.2021.07.018.
21. Azimah A. (2022). Feninferina Azman ,Masyura Ahmad Faudzi , Hasventhran Baskaran, fiza abdul rahim (2022). job opportunities recommendation for visually impaired people using natural language processing.
https://www.jatit.org/volumes/Vol100No2/20Vol100No2.pdf
22. Bellini et al.(2020). “GUapp : A Conversational Agent for Job Recommendation for the Italian Public Administration”
23. Giabelli, A.; Malandri, L.; Mercorio, F.; and Mezzanzanica, M. (2021). “Skills2Job : A recommender system that encodes job offer embeddings on graph databases,” Appl. Soft Comput. J., vol. 101, p. 107049, 2021, doi: 10.1016/j.asoc.2020.107049
24. US Department Justice (2022). Algorithms, Artificial Intelligence, and Disability Discrimination in Hiring. Civil Rights Division. https://www.ada.gov/resources/ai-guidance/
25. Chaflekar, S.; Pawade, S.; Thakur S.; Vaidya, A.; Gajapure, A. and Chaudhari M. (2023) Assistant Professor, Department of Information Technology1 Students, Department of Information Technology Priyadarshini Bhagwati College of Engineering, Nagpur, Maharashtra, India.
26. Abid, M. ; Ben-Salha, O. & Kanetaki, Z. (2024). Does the Impact of Artificial Intelligence on Unemployment Among People With Disabilities Differ by Educational Level? A Dynamic Panel Threshold Approach. Digital Object Identifier
10.1109/ACCESS.2024.3456962. https://unesdoc.unesco.org/ark:/48223/pf0000376162
27. Ip, E. (2025). Fair AI in hiring: Experimentalevidence on how biased hiringalgorithms and differentdebiasing methods affect thequality and diversity of applicants. Behavioral Science & Policy2025, Vol. 11(1) 44–54© Behavioral Science& Policy Association 2025 Article reuse guidelines: sagepub.com/journals-permissions DOI: 10.1177/23794607251353585
28. Voutsakelis, G.; Dimkaros, I.; Tzimos, N.; Kokkonis, G.; Kontogiannis, S. (2025). Development and Evaluation of a Tool for Blind Users Utilizing AI Object Detection and Haptic Feedback. Machines 2025, 13, 398. https://doi.org/10.3390/
machines13050398
29. Santana, F. y Ortega, M. (2025). Análisis de la inclusión laboral de personas con discapacidad DOI: https://doi.org/10.23857/dc.v11i1.4259.
https://dominiodelasciencias.com/ojs/index.php/es/article/view/4259
30. Joshi, A. and Pavel, B. (2020). Data Preprocessing: the Techniques for Preparing Cleanand Quality Data for Data Analytics Process. Oriental Journal of Computer Science and Technology. SSN: 0974-6471, Vol. 13, No. (2-3) 2020, Pg. 78-81
31. Gratos, K., Ougiaroglou, S. (2023). A Web Tool for K-means Clustering, DOI:10.1007/978-3-031-44097-7_9. In book: Novel & Intelligent Digital Systems: Proceedings of the 3rd International Conference (NiDS 2023) (pp.91-101)
32. Saqr, M., Lopez-Pernas, S. (2024). Learning Analytics Methods and Tutorials. ISBN 978-3-031-54463-7 ISBN 978-3-031-54464-4 (eBook) University of Eastern Finland. Creative Commons Attribution 4.0 International License
(http://creativecommons.org/licenses/by/4.0/). https://doi.org/10.1007/978-3-031-54464-4.
33. UNESCO (2021). Understanding the impact of artificial intelligence on skills development International Centre for Technical and Vocational Education and Training.ISBN: 978-92-3-100446-9.
https://unevoc.unesco.org/pub/understanding_the_impact_of_ai_on_skills_development.pdf
34. Lukman, L. (2025). The Use of Artificial Intelligence in The Development of Vision Aids for The Visually Impaired. Riwayat Educational Journal of History and Humanities 8(1):251-268. DOI:10.24815/jr.v8i1.43491
35. Pacheco, J. (2026). Trends and Advances in Labor Inclusion: A Bibliometric Analysis of the Last Vicennium. European Public and Social Innovation Review Articl eOpen Access 2026. DOI: 10.31637/epsir-2026-1632Copy to clipboard.
https://www.scopus.com/pages/publications/105017052728?origin=resultslist
36. Hossen, M. M. (2024). A Descriptive Exploration of Selected Employment Barriers and Supports for Individuals With Visual Impairments in Bangladesh [Doctoral dissertation, The University of Texas Rio Grande Valley].
https://scholarworks.utrgv.edu/etd/1524
37. Foulds, M., Duguid. B, and Yu, Y. (2025). Understanding International Approaches to Achieving Equality of Blind and Partially Sighted People. Thomas Pocklington Trust. https://www.pocklington.org.uk/wp-content/uploads/2025/07/Achieving-Equality-Report-DIGITAL-1.pdf
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