Empowering Vulnerable Populations through Technology: Innovations and Challenges in Social Work
Abstract
Purpose: This study aims to explore the efficacy of integrating digital solutions and technological aids in social work practices, specifically for assisting vulnerable populations. It investigates the historical and current relevance of technology in social work, with a focus on overcoming the digital divide that restricts access for these groups.
Methodology: Utilizing a mixed-methods approach, this research combines qualitative and quantitative methodologies, including surveys, interviews, and observational studies, to examine the advantages and limitations of digital interventions like telehealth platforms compared to traditional face-to-face services. Additionally, it delves into the potential of Artificial Intelligence (AI) and related technologies to foster independence among vulnerable populations.
Findings: The study reveals that while digital solutions offer significant benefits, including increased accessibility and potential for personalization through AI, they also present challenges, notably the digital divide due to economic, cognitive, and socio-cultural barriers. It suggests that hybrid models incorporating both digital and traditional methods could enhance social work practices. The research underscores the importance of addressing ethical considerations in the deployment of AI technologies.
Originality: This research contributes original insights into the integration of technology within social work, highlighting the complexities of the digital divide and proposing a strategic framework for incorporating technological aids. It offers a foundational understanding of how digital tools can complement traditional social work practices, ensuring ethical considerations are prioritized. Furthermore, it opens avenues for future research on the dynamic interplay between technology and social work, aiming for a more inclusive and effective approach to supporting vulnerable populations.
References
[1] Aloulou, H. et al. (2013). Deployment of assistive living technology in a nursing home environment: methods and lessons learned. BMC Med. Inform. Decis. Mak., 13(1): 42. DOI: 10.1186/1472-6947-13-42
[2] Angrosino, M. V. (2016). Naturalistic Observation. New York: Routledge, 2016. DOI:10.4324/9781315423616
[3] Bashshur, R. L. et al. (2014). The empirical foundations of telemedicine interventions for chronic disease management. Telemed. J. E-Health Off. J. Am. Telemed. Assoc., 20(9): 769–800. DOI:10.1089/tmj.2014.9981
[4] Berryhill, M. B. et al. (2019). Videoconferencing Psychotherapy and Depression: A Systematic Review. Telemed. J. E-Health Off. J. Am. Telemed. Assoc., 25(6): 435–446. DOI: 10.1089/tmj.2018.0058
[5] Bødker, S. and M. Kyng (2018). Participatory Design that Matters - Facing the Big Issues. ACM Trans. Comput.-Hum. Interact., 25(1): 4:1-4:31, Feb. 2018. DOI: 10.1145/3152421
[6] Boynton, P. M. and T. Greenhalgh (2004). Selecting, designing, and developing your questionnaire. BMJ, 328(7451): 1312–1315. DOI: 10.1136/bmj.328.7451.1312
[7] Brandão, C, .P. Bazeley and K. Jackson (2015). Qualitative Data Analysis with NVivo (2nd ed.). Qual. Res. Psychol., 12(4): 492–494, Oct. 2015, DOI: 10.1080/14780887.2014.992750
[8] Bratteteig, T. and I. Wagner (2012). Disentangling power and decision-making in participatory design, in Proceedings of the 12th Participatory Design Conference: Research Papers - Volume 1, in PDC ’12. New York, NY, USA: Association for Computing Machinery, pp. 41–50. DOI: 10.1145/2347635.2347642
[9] Chan, C. V. and D. R. Kaufman (2011). A framework for characterizing eHealth literacy demands and barriers, J. Med. Internet Res., 13(4): e94. DOI: 10.2196/jmir.1750
[10] Czaja, S. J. and C. C. Lee (2007). The impact of aging on access to technology. Univers. Access Inf. Soc., 5(4): 341–349. DOI: 10.1007/s10209-006-0060-x
[11] D. A. J. A. M. van and E. J. Helsper (2015). The Third-Level Digital Divide: Who Benefits Most from Being Online?, Communication and Information Technologies Annual, vol. 10, in Studies in Media and Communications, 10: 29–52. DOI: 10.1108/S2050-206020150000010002
[12] Davenport, T. and R. Kalakota (2019). The potential for artificial intelligence in healthcare. Future Healthc. J., 6(2): 94–98. DOI: 10.7861/futurehosp.6-2-94
[13] Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Q., 13(3): 319–340. DOI: 10.2307/249008
[14] Demaerschalk, B. M., R. Raman, K. Ernstrom, and B. C. Meyer (2012). Efficacy of Telemedicine for Stroke: Pooled Analysis of the Stroke Team Remote Evaluation Using a Digital Observation Camera (STRokE DOC) and STRokE DOC Arizona Telestroke Trials. Telemed. J. E Health, 18(3): 230–237. DOI:10.1089/tmj.2011.0116
[15] Dicicco-Bloom B. and B. F. Crabtree (2006). The qualitative research interview. Med. Educ., 40(4): 314–321. DOI: 10.1111/j.1365-2929.2006.02418.x
[16] Dorsey, E. R. and E. J. Topol (2016). State of Telehealth. N. Engl. J. Med., 375(2): 154–161. DOI:10.1056/NEJMra1601705
[17] Doshi-Velez, F. and B. Kim (2017). Towards A Rigorous Science of Interpretable Machine Learning. arXiv, Mar. 02, 2017. DOI: 10.48550/arXiv.1702.08608
[18] Farah, M. J. (2012). Neuroethics: The Ethical, Legal, and Societal Impact of Neuroscience. Annu. Rev. Psychol., 63(1): 571–591, Jan. 2012, DOI: 10.1146/annurev.psych.093008.100438
[19] Fiordelli, M., N. Diviani, and P. J. Schulz (2013). Mapping mHealth research: a decade of evolution. J. Med. Internet Res., 15(5): e95. DOI: 10.2196/jmir.2430
[20] Garvin, L. A., J. Hu, C. Slightam, D. K. McInnes, and D. M. Zulman (2021). Use of Video Telehealth Tablets to Increase Access for Veterans Experiencing Homelessness. J. Gen. Intern. Med., 36(8): 2274–2282. DOI:10.1007/s11606-021-06900-8
[21] Green, B. (2019). The Smart Enough City: Putting Technology in Its Place to Reclaim Our Urban Future. The MIT Press, 2019. DOI: 10.7551/mitpress/11555.001.0001
[22] Greenhalgh, T. et al. (2018). Real-World Implementation of Video Outpatient Consultations at Macro, Meso, and Micro Levels: Mixed-Method Study. J. Med. Internet Res., 20(4): e150. DOI: 10.2196/jmir.9897
[23] Greenhalgh, T., J. Wherton, S. Shaw, and C. Morrison (2020). Video consultations for covid-19. BMJ, 368(m998). DOI: 10.1136/bmj.m998
[24] Hassoon A. et al. (2018). Increasing Physical Activity Amongst Overweight and Obese Cancer Survivors Using an Alexa-Based Intelligent Agent for Patient Coaching: Protocol for the Physical Activity by Technology Help (PATH) Trial. JMIR Res. Protoc., 7(2): e27. DOI: 10.2196/resprot.9096
[25] Hilbert, M. (2011). Digital gender divide or technologically empowered women in developing countries? A typical case of lies, damned lies, and statistics. Womens Stud. Int. Forum, 34(6): 479–489. DOI:10.1016/j.wsif.2011.07.001
[26] Holm, S. (2002). Principles of Biomedical Ethics, 5th edn.: Beauchamp T L, Childress J F. Oxford University Press, 2001, £19.95, pp 454. ISBN 0-19-514332-9. J. Med. Ethics, 28(5): 332–332. DOI:10.1136/jme.28.5.332-a
[27] Hoque, R. and G. Sorwar (2017). Understanding factors influencing the adoption of mHealth by the elderly: An extension of the UTAUT model. Int. J. Med. Inf., 101: 75–84. DOI: 10.1016/j.ijmedinf.2017.02.002
[28] Ioannidis, J. P. A. (2018). The Challenge of Reforming Nutritional Epidemiologic Research. JAMA, 320(10): 969–970. DOI: 10.1001/jama.2018.11025
[29] Irani, L., J. Vertesi, P. Dourish, K. Philip, and R. E. Grinter (2010). Postcolonial computing: a lens on design and development, Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, in CHI ’10. New York, NY, USA: Association for Computing Machinery, pp. 1311–1320. DOI:10.1145/1753326.1753522
[30] Jobin, A., M. Ienca, and E. Vayena (2019). The global landscape of AI ethics guidelines. Nat. Mach. Intell., vol. 1, no. 9, Art. no. 9. DOI: 10.1038/s42256-019-0088-2
[31] Koenecke A. et al. (2020). Racial disparities in automated speech recognition. Proc. Natl. Acad. Sci., 117(14): 7684–7689. DOI: 10.1073/pnas.1915768117
[32] Krumpal, I. (2013). Determinants of social desirability bias in sensitive surveys: a literature review. Qual. Quant., 47(4): 2025–2047. DOI: 10.1007/s11135-011-9640-9
[33] Kruse, C. S., N. Krowski, B. Rodriguez, L. Tran, J. Vela, and M. Brooks (2017). Telehealth and patient satisfaction: a systematic review and narrative analysis. BMJ Open, 7(8): e016242. DOI: 10.1136/bmjopen-2017-016242
[34] Kruse, C. S., S. Bouffard, M. Dougherty, and J. S. Parro (2016). Telemedicine Use in Rural Native American Communities in the Era of the ACA: a Systematic Literature Review. J. Med. Syst., 40(6): 145. DOI:10.1007/s10916-016-0503-8
[35] Langarizadeh, M. et al. (2017). Telemental Health Care, an Effective Alternative to Conventional Mental Care: a Systematic Review. Acta Inform. Medica, 25(4): 240–246. DOI: 10.5455/aim.2017.25.240-246
[36] Liamputtong, P. (2007). Researching the Vulnerable. SAGE Publications, Ltd, 2007. DOI:10.4135/9781849209861.
[37] Lucas, G. M., J. Gratch, A. King, and L.-P. Morency (2014). It’s only a computer: Virtual humans increase willingness to disclose. Comput. Hum. Behav., 37: 94–100. DOI: 10.1016/j.chb.2014.04.043
[38] Lungu, M. (2023). The Coding Manual for Qualitative Researchers. Am. J. Qual. Res. Available at: https://www.academia.edu/95636548/The_Coding_Manual_for_Qualitative_Researchers
[39] M. A. and A. Perrin (2017). Technology use among seniors, Pew Research Center: Internet, Science and Tech. Available at: https://www.pewresearch.org/internet/2017/05/17/technology-use-among-seniors/
[40] Man, D. W. K., J. C. C. Chung, and G. Y. Y. Lee (2012). Evaluation of a virtual reality-based memory training programme for Hong Kong Chinese older adults with questionable dementia: a pilot study. Int. J. Geriatr. Psychiatry, 27(5): 513–520. DOI: 10.1002/gps.2746
[41] Mason, M. (2010). Sample Size and Saturation in PhD Studies Using Qualitative Interviews. Forum Qual. Sozialforschung Forum Qual. Soc. Res., vol. 11, no. 3, Art. no. 3, Aug., DOI: 10.17169/fqs-11.3.1428
[42] Mazhar, T. et al. (2022). The Role of ML, AI and 5G Technology in Smart Energy and Smart Building Management,” Electronics, 11(23), Art. no. 23, Jan. 2022. DOI: 10.3390/electronics11233960
[43] Metcalf, J. and K. Crawford (2016). Where are human subjects in Big Data research? The emerging ethics divide. Big Data Soc., 3(1). DOI: 10.1177/2053951716650211
[44] Miner, A. S., A. Milstein, and J. T. Hancock (2017). Talking to Machines About Personal Mental Health Problems. JAMA, 318(13): 1217–1218. DOI: 10.1001/jama.2017.14151
[45] Mishna, F., M. Bogo, and J.-L. Sawyer (2015). Cyber counseling: Illuminating benefits and challenges. Clin. Soc. Work J., 43(2): 169–178. DOI: 10.1007/s10615-013-0470-1
[46] Nemer, D. and G. Freeman (2015). Selfies| Empowering the Marginalized: Rethinking Selfies in the Slums of Brazil. Int. J. Commun., vol. 9, no. 0, Art. no. 0, May 2015.
[47] Norris, P. (2001). Digital Divide: Civic Engagement, Information Poverty, and the Internet Worldwide,” Cambridge Core. Available: https://www.cambridge.org/core/books/digital-divide/20EFED5574695AC79D1BBC6E295B1EC0
[48] Obermeyer, Z. and E. J. Emanuel (2016). Predicting the Future - Big Data, Machine Learning, and Clinical Medicine. N. Engl. J. Med., 375(13): 1216–1219. DOI: 10.1056/NEJMp1606181
[49] Pantelopoulos, A. and N. G. Bourbakis (2010). A Survey on Wearable Sensor-Based Systems for Health Monitoring and Prognosis. IEEE Trans. Syst. Man Cybern. Part C Appl. Rev., 40(1): 1–12. DOI:10.1109/TSMCC.2009.2032660
[50] Parrott, L. and I. Madoc-Jones (2008). Reclaiming Information and Communication Technologies for Empowering Social Work Practice. J. Soc. Work, 8(2): 181–197. DOI: 10.1177/1468017307084739
[51] Piper, A. M., R. Campbell, and J. D. Hollan, (2010). Exploring the accessibility and appeal of surface computing for older adult health care support, Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, in CHI ’10. New York, NY, USA: Association for Computing Machinery, pp. 907–916. DOI: 10.1145/1753326.1753461
[52] Powell, R. E., J. M. Henstenburg, G. Cooper, J. E. Hollander, and K. L. Rising (2017). Patient Perceptions of Telehealth Primary Care Video Visits. Ann. Fam. Med., 15(3): 225–229. DOI: 10.1370/afm.2095
[53] Pu, L., W. Moyle, C. Jones, and M. Todorovic (2019). The Effectiveness of Social Robots for Older Adults: A Systematic Review and Meta-Analysis of Randomized Controlled Studies. The Gerontologist, 59(1): e37–e51. DOI: 10.1093/geront/gny046
[54] Ramsetty, A. and C. Adams (2020). Impact of the digital divide in the age of COVID-19. J. Am. Med. Inform. Assoc. JAMIA, 27(7): 1147–1148. DOI: 10.1093/jamia/ocaa078
[55] Reisdorf B. C. and D. Groselj (2017). Internet (non-)use types and motivational access: Implications for digital inequalities research. New Media Soc., 19(8): 1157–1176. DOI: 10.1177/1461444815621539
[56] Robinson L. et al. (2015). Digital inequalities and why they matter. Inf. Commun. Soc., 18(5): 569–582, May 2015. DOI: 10.1080/1369118X.2015.1012532
[57] Rose, M. L., A. M. Raymer, L. E. Lanyon, and M. C. Attard (2013). A systematic review of gesture treatments for post-stroke aphasia,” in Database of Abstracts of Reviews of Effects (DARE): Quality-assessed Reviews [Internet], Centre for Reviews and Dissemination (UK). Available at: https://www.ncbi.nlm.nih.gov/books/NBK169255/
[58] Shi, S., Zhang, L., and Wang G. (2023). Bridging the Digital Divide: Internet Use of Older People from the Perspective of Peer Effects. Sustainability, 15(15), Art. no. 15, Jan. 2023. DOI: 10.3390/su151512024
[59] Sedgwick, P. (2015). Bias in observational study designs: cross sectional studies. BMJ, 350: h1286. DOI:10.1136/bmj.h1286
[60] Sparrow, R. (2016). Robots in aged care: a dystopian future? AI Soc., 31(4): 445–454. DOI: 10.1007/s00146-015-0625-4
[61] Srinuan, C. and E. Bohlin (2011). Understanding the digital divide: A literature survey and ways forward,” Calgary: International Telecommunications Society (ITS). Available at: https://www.econstor.eu/handle/10419/52191
[62] Story, M. F., J. L. Mueller, and R. L. Mace (1998). The Universal Design File: Designing for People of All Ages and Abilities. Revised Edition, Center for Universal Design, NC State University, Box 8613, Raleigh, NC 27695-8613. Available at: https://eric.ed.gov/?id=ED460554
[63] Stumbo, N. J., J. K. Martin, and B. N. Hedrick (2009). Assistive technology: Impact on education, employment, and independence of individuals with physical disabilities. J. Vocat. Rehabil., 30(2): 99–110. DOI: 10.3233/JVR-2009-0456
[64] Wade, V. A., J. Karnon, A. G. Elshaug, and J. E. Hiller (2010). A systematic review of economic analyses of telehealth services using real time video communication. BMC Health Serv. Res., 10(1): 233. DOI:10.1186/1472-6963-10-233
[65] Wootton, R. (2012). Twenty years of telemedicine in chronic disease management--an evidence synthesis. J. Telemed. Telecare, 18(4): 211–220. DOI: 10.1258/jtt.2012.120219
[66] Wyche, S. P. and L. L. Murphy (2012). ‘Dead China-make’ phones off the grid: investigating and designing for mobile phone use in rural Africa, in Proceedings of the Designing Interactive Systems Conference, Newcastle Upon Tyne United Kingdom: ACM, pp. 186–195. DOI: 10.1145/2317956.2317985
[67] Yeung, A. W. K. et al. (2021). Virtual and Augmented Reality Applications in Medicine: Analysis of the Scientific Literature. J. Med. Internet Res., 23(2): e25499. DOI: 10.2196/25499
[68] Designing and Conducting Mixed Methods Research, SAGE Publications Ltd. Available at: https://uk.sagepub.com/en-gb/asi/designing-and-conducting-mixed-methods-research/book241842
[69] Field, A.P. (2018). Discovering Statistics Using IBM SPSS Statistics. 5th Edition, Sage, Newbury Park. - References - Scientific Research Publishing. Available at: https://www.scirp.org/(S(vtj3fa45qm1ean45wffcz5%205))/reference/referencespapers.aspx?referenceid=3504991
[70] Institute, A. N. “AI Now 2017 Report,” AI Now Institute. Available at: https://ainowinstitute.org/publication/ai-now-2017-report-2
[71] Keep families closer with GrandPad. Available at: https://www.grandpad.net/
[72] Lynne Rienner Publishers. Disability and the Internet Confronting a Digital Divide. Available at: https://www.rienner.com/title/Disability_and_the_Internet_Confronting_a_Digital_Divide
[73] Smartphones help blacks, Hispanics bridge some – but not all – digital gaps with whites, Benton Foundation. Available at: https://www.benton.org/headlines/smartphones-help-blacks-hispanics-bridge-some-%E2%80%93-not-all-%E2%80%93-digital-gaps-whites
[74] Technology and Social Inclusion, MIT Press. Available at: https://mitpress.mit.edu/9780262731737/technology-and-social-inclusion/
[75] Weltgesundheitsorganisation (2012). Ed., Addressing the social determinants of health: the urban dimension and the role of local government. Copenhagen: World Health Organization, Regional Office for Europe.
[2] Angrosino, M. V. (2016). Naturalistic Observation. New York: Routledge, 2016. DOI:10.4324/9781315423616
[3] Bashshur, R. L. et al. (2014). The empirical foundations of telemedicine interventions for chronic disease management. Telemed. J. E-Health Off. J. Am. Telemed. Assoc., 20(9): 769–800. DOI:10.1089/tmj.2014.9981
[4] Berryhill, M. B. et al. (2019). Videoconferencing Psychotherapy and Depression: A Systematic Review. Telemed. J. E-Health Off. J. Am. Telemed. Assoc., 25(6): 435–446. DOI: 10.1089/tmj.2018.0058
[5] Bødker, S. and M. Kyng (2018). Participatory Design that Matters - Facing the Big Issues. ACM Trans. Comput.-Hum. Interact., 25(1): 4:1-4:31, Feb. 2018. DOI: 10.1145/3152421
[6] Boynton, P. M. and T. Greenhalgh (2004). Selecting, designing, and developing your questionnaire. BMJ, 328(7451): 1312–1315. DOI: 10.1136/bmj.328.7451.1312
[7] Brandão, C, .P. Bazeley and K. Jackson (2015). Qualitative Data Analysis with NVivo (2nd ed.). Qual. Res. Psychol., 12(4): 492–494, Oct. 2015, DOI: 10.1080/14780887.2014.992750
[8] Bratteteig, T. and I. Wagner (2012). Disentangling power and decision-making in participatory design, in Proceedings of the 12th Participatory Design Conference: Research Papers - Volume 1, in PDC ’12. New York, NY, USA: Association for Computing Machinery, pp. 41–50. DOI: 10.1145/2347635.2347642
[9] Chan, C. V. and D. R. Kaufman (2011). A framework for characterizing eHealth literacy demands and barriers, J. Med. Internet Res., 13(4): e94. DOI: 10.2196/jmir.1750
[10] Czaja, S. J. and C. C. Lee (2007). The impact of aging on access to technology. Univers. Access Inf. Soc., 5(4): 341–349. DOI: 10.1007/s10209-006-0060-x
[11] D. A. J. A. M. van and E. J. Helsper (2015). The Third-Level Digital Divide: Who Benefits Most from Being Online?, Communication and Information Technologies Annual, vol. 10, in Studies in Media and Communications, 10: 29–52. DOI: 10.1108/S2050-206020150000010002
[12] Davenport, T. and R. Kalakota (2019). The potential for artificial intelligence in healthcare. Future Healthc. J., 6(2): 94–98. DOI: 10.7861/futurehosp.6-2-94
[13] Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Q., 13(3): 319–340. DOI: 10.2307/249008
[14] Demaerschalk, B. M., R. Raman, K. Ernstrom, and B. C. Meyer (2012). Efficacy of Telemedicine for Stroke: Pooled Analysis of the Stroke Team Remote Evaluation Using a Digital Observation Camera (STRokE DOC) and STRokE DOC Arizona Telestroke Trials. Telemed. J. E Health, 18(3): 230–237. DOI:10.1089/tmj.2011.0116
[15] Dicicco-Bloom B. and B. F. Crabtree (2006). The qualitative research interview. Med. Educ., 40(4): 314–321. DOI: 10.1111/j.1365-2929.2006.02418.x
[16] Dorsey, E. R. and E. J. Topol (2016). State of Telehealth. N. Engl. J. Med., 375(2): 154–161. DOI:10.1056/NEJMra1601705
[17] Doshi-Velez, F. and B. Kim (2017). Towards A Rigorous Science of Interpretable Machine Learning. arXiv, Mar. 02, 2017. DOI: 10.48550/arXiv.1702.08608
[18] Farah, M. J. (2012). Neuroethics: The Ethical, Legal, and Societal Impact of Neuroscience. Annu. Rev. Psychol., 63(1): 571–591, Jan. 2012, DOI: 10.1146/annurev.psych.093008.100438
[19] Fiordelli, M., N. Diviani, and P. J. Schulz (2013). Mapping mHealth research: a decade of evolution. J. Med. Internet Res., 15(5): e95. DOI: 10.2196/jmir.2430
[20] Garvin, L. A., J. Hu, C. Slightam, D. K. McInnes, and D. M. Zulman (2021). Use of Video Telehealth Tablets to Increase Access for Veterans Experiencing Homelessness. J. Gen. Intern. Med., 36(8): 2274–2282. DOI:10.1007/s11606-021-06900-8
[21] Green, B. (2019). The Smart Enough City: Putting Technology in Its Place to Reclaim Our Urban Future. The MIT Press, 2019. DOI: 10.7551/mitpress/11555.001.0001
[22] Greenhalgh, T. et al. (2018). Real-World Implementation of Video Outpatient Consultations at Macro, Meso, and Micro Levels: Mixed-Method Study. J. Med. Internet Res., 20(4): e150. DOI: 10.2196/jmir.9897
[23] Greenhalgh, T., J. Wherton, S. Shaw, and C. Morrison (2020). Video consultations for covid-19. BMJ, 368(m998). DOI: 10.1136/bmj.m998
[24] Hassoon A. et al. (2018). Increasing Physical Activity Amongst Overweight and Obese Cancer Survivors Using an Alexa-Based Intelligent Agent for Patient Coaching: Protocol for the Physical Activity by Technology Help (PATH) Trial. JMIR Res. Protoc., 7(2): e27. DOI: 10.2196/resprot.9096
[25] Hilbert, M. (2011). Digital gender divide or technologically empowered women in developing countries? A typical case of lies, damned lies, and statistics. Womens Stud. Int. Forum, 34(6): 479–489. DOI:10.1016/j.wsif.2011.07.001
[26] Holm, S. (2002). Principles of Biomedical Ethics, 5th edn.: Beauchamp T L, Childress J F. Oxford University Press, 2001, £19.95, pp 454. ISBN 0-19-514332-9. J. Med. Ethics, 28(5): 332–332. DOI:10.1136/jme.28.5.332-a
[27] Hoque, R. and G. Sorwar (2017). Understanding factors influencing the adoption of mHealth by the elderly: An extension of the UTAUT model. Int. J. Med. Inf., 101: 75–84. DOI: 10.1016/j.ijmedinf.2017.02.002
[28] Ioannidis, J. P. A. (2018). The Challenge of Reforming Nutritional Epidemiologic Research. JAMA, 320(10): 969–970. DOI: 10.1001/jama.2018.11025
[29] Irani, L., J. Vertesi, P. Dourish, K. Philip, and R. E. Grinter (2010). Postcolonial computing: a lens on design and development, Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, in CHI ’10. New York, NY, USA: Association for Computing Machinery, pp. 1311–1320. DOI:10.1145/1753326.1753522
[30] Jobin, A., M. Ienca, and E. Vayena (2019). The global landscape of AI ethics guidelines. Nat. Mach. Intell., vol. 1, no. 9, Art. no. 9. DOI: 10.1038/s42256-019-0088-2
[31] Koenecke A. et al. (2020). Racial disparities in automated speech recognition. Proc. Natl. Acad. Sci., 117(14): 7684–7689. DOI: 10.1073/pnas.1915768117
[32] Krumpal, I. (2013). Determinants of social desirability bias in sensitive surveys: a literature review. Qual. Quant., 47(4): 2025–2047. DOI: 10.1007/s11135-011-9640-9
[33] Kruse, C. S., N. Krowski, B. Rodriguez, L. Tran, J. Vela, and M. Brooks (2017). Telehealth and patient satisfaction: a systematic review and narrative analysis. BMJ Open, 7(8): e016242. DOI: 10.1136/bmjopen-2017-016242
[34] Kruse, C. S., S. Bouffard, M. Dougherty, and J. S. Parro (2016). Telemedicine Use in Rural Native American Communities in the Era of the ACA: a Systematic Literature Review. J. Med. Syst., 40(6): 145. DOI:10.1007/s10916-016-0503-8
[35] Langarizadeh, M. et al. (2017). Telemental Health Care, an Effective Alternative to Conventional Mental Care: a Systematic Review. Acta Inform. Medica, 25(4): 240–246. DOI: 10.5455/aim.2017.25.240-246
[36] Liamputtong, P. (2007). Researching the Vulnerable. SAGE Publications, Ltd, 2007. DOI:10.4135/9781849209861.
[37] Lucas, G. M., J. Gratch, A. King, and L.-P. Morency (2014). It’s only a computer: Virtual humans increase willingness to disclose. Comput. Hum. Behav., 37: 94–100. DOI: 10.1016/j.chb.2014.04.043
[38] Lungu, M. (2023). The Coding Manual for Qualitative Researchers. Am. J. Qual. Res. Available at: https://www.academia.edu/95636548/The_Coding_Manual_for_Qualitative_Researchers
[39] M. A. and A. Perrin (2017). Technology use among seniors, Pew Research Center: Internet, Science and Tech. Available at: https://www.pewresearch.org/internet/2017/05/17/technology-use-among-seniors/
[40] Man, D. W. K., J. C. C. Chung, and G. Y. Y. Lee (2012). Evaluation of a virtual reality-based memory training programme for Hong Kong Chinese older adults with questionable dementia: a pilot study. Int. J. Geriatr. Psychiatry, 27(5): 513–520. DOI: 10.1002/gps.2746
[41] Mason, M. (2010). Sample Size and Saturation in PhD Studies Using Qualitative Interviews. Forum Qual. Sozialforschung Forum Qual. Soc. Res., vol. 11, no. 3, Art. no. 3, Aug., DOI: 10.17169/fqs-11.3.1428
[42] Mazhar, T. et al. (2022). The Role of ML, AI and 5G Technology in Smart Energy and Smart Building Management,” Electronics, 11(23), Art. no. 23, Jan. 2022. DOI: 10.3390/electronics11233960
[43] Metcalf, J. and K. Crawford (2016). Where are human subjects in Big Data research? The emerging ethics divide. Big Data Soc., 3(1). DOI: 10.1177/2053951716650211
[44] Miner, A. S., A. Milstein, and J. T. Hancock (2017). Talking to Machines About Personal Mental Health Problems. JAMA, 318(13): 1217–1218. DOI: 10.1001/jama.2017.14151
[45] Mishna, F., M. Bogo, and J.-L. Sawyer (2015). Cyber counseling: Illuminating benefits and challenges. Clin. Soc. Work J., 43(2): 169–178. DOI: 10.1007/s10615-013-0470-1
[46] Nemer, D. and G. Freeman (2015). Selfies| Empowering the Marginalized: Rethinking Selfies in the Slums of Brazil. Int. J. Commun., vol. 9, no. 0, Art. no. 0, May 2015.
[47] Norris, P. (2001). Digital Divide: Civic Engagement, Information Poverty, and the Internet Worldwide,” Cambridge Core. Available: https://www.cambridge.org/core/books/digital-divide/20EFED5574695AC79D1BBC6E295B1EC0
[48] Obermeyer, Z. and E. J. Emanuel (2016). Predicting the Future - Big Data, Machine Learning, and Clinical Medicine. N. Engl. J. Med., 375(13): 1216–1219. DOI: 10.1056/NEJMp1606181
[49] Pantelopoulos, A. and N. G. Bourbakis (2010). A Survey on Wearable Sensor-Based Systems for Health Monitoring and Prognosis. IEEE Trans. Syst. Man Cybern. Part C Appl. Rev., 40(1): 1–12. DOI:10.1109/TSMCC.2009.2032660
[50] Parrott, L. and I. Madoc-Jones (2008). Reclaiming Information and Communication Technologies for Empowering Social Work Practice. J. Soc. Work, 8(2): 181–197. DOI: 10.1177/1468017307084739
[51] Piper, A. M., R. Campbell, and J. D. Hollan, (2010). Exploring the accessibility and appeal of surface computing for older adult health care support, Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, in CHI ’10. New York, NY, USA: Association for Computing Machinery, pp. 907–916. DOI: 10.1145/1753326.1753461
[52] Powell, R. E., J. M. Henstenburg, G. Cooper, J. E. Hollander, and K. L. Rising (2017). Patient Perceptions of Telehealth Primary Care Video Visits. Ann. Fam. Med., 15(3): 225–229. DOI: 10.1370/afm.2095
[53] Pu, L., W. Moyle, C. Jones, and M. Todorovic (2019). The Effectiveness of Social Robots for Older Adults: A Systematic Review and Meta-Analysis of Randomized Controlled Studies. The Gerontologist, 59(1): e37–e51. DOI: 10.1093/geront/gny046
[54] Ramsetty, A. and C. Adams (2020). Impact of the digital divide in the age of COVID-19. J. Am. Med. Inform. Assoc. JAMIA, 27(7): 1147–1148. DOI: 10.1093/jamia/ocaa078
[55] Reisdorf B. C. and D. Groselj (2017). Internet (non-)use types and motivational access: Implications for digital inequalities research. New Media Soc., 19(8): 1157–1176. DOI: 10.1177/1461444815621539
[56] Robinson L. et al. (2015). Digital inequalities and why they matter. Inf. Commun. Soc., 18(5): 569–582, May 2015. DOI: 10.1080/1369118X.2015.1012532
[57] Rose, M. L., A. M. Raymer, L. E. Lanyon, and M. C. Attard (2013). A systematic review of gesture treatments for post-stroke aphasia,” in Database of Abstracts of Reviews of Effects (DARE): Quality-assessed Reviews [Internet], Centre for Reviews and Dissemination (UK). Available at: https://www.ncbi.nlm.nih.gov/books/NBK169255/
[58] Shi, S., Zhang, L., and Wang G. (2023). Bridging the Digital Divide: Internet Use of Older People from the Perspective of Peer Effects. Sustainability, 15(15), Art. no. 15, Jan. 2023. DOI: 10.3390/su151512024
[59] Sedgwick, P. (2015). Bias in observational study designs: cross sectional studies. BMJ, 350: h1286. DOI:10.1136/bmj.h1286
[60] Sparrow, R. (2016). Robots in aged care: a dystopian future? AI Soc., 31(4): 445–454. DOI: 10.1007/s00146-015-0625-4
[61] Srinuan, C. and E. Bohlin (2011). Understanding the digital divide: A literature survey and ways forward,” Calgary: International Telecommunications Society (ITS). Available at: https://www.econstor.eu/handle/10419/52191
[62] Story, M. F., J. L. Mueller, and R. L. Mace (1998). The Universal Design File: Designing for People of All Ages and Abilities. Revised Edition, Center for Universal Design, NC State University, Box 8613, Raleigh, NC 27695-8613. Available at: https://eric.ed.gov/?id=ED460554
[63] Stumbo, N. J., J. K. Martin, and B. N. Hedrick (2009). Assistive technology: Impact on education, employment, and independence of individuals with physical disabilities. J. Vocat. Rehabil., 30(2): 99–110. DOI: 10.3233/JVR-2009-0456
[64] Wade, V. A., J. Karnon, A. G. Elshaug, and J. E. Hiller (2010). A systematic review of economic analyses of telehealth services using real time video communication. BMC Health Serv. Res., 10(1): 233. DOI:10.1186/1472-6963-10-233
[65] Wootton, R. (2012). Twenty years of telemedicine in chronic disease management--an evidence synthesis. J. Telemed. Telecare, 18(4): 211–220. DOI: 10.1258/jtt.2012.120219
[66] Wyche, S. P. and L. L. Murphy (2012). ‘Dead China-make’ phones off the grid: investigating and designing for mobile phone use in rural Africa, in Proceedings of the Designing Interactive Systems Conference, Newcastle Upon Tyne United Kingdom: ACM, pp. 186–195. DOI: 10.1145/2317956.2317985
[67] Yeung, A. W. K. et al. (2021). Virtual and Augmented Reality Applications in Medicine: Analysis of the Scientific Literature. J. Med. Internet Res., 23(2): e25499. DOI: 10.2196/25499
[68] Designing and Conducting Mixed Methods Research, SAGE Publications Ltd. Available at: https://uk.sagepub.com/en-gb/asi/designing-and-conducting-mixed-methods-research/book241842
[69] Field, A.P. (2018). Discovering Statistics Using IBM SPSS Statistics. 5th Edition, Sage, Newbury Park. - References - Scientific Research Publishing. Available at: https://www.scirp.org/(S(vtj3fa45qm1ean45wffcz5%205))/reference/referencespapers.aspx?referenceid=3504991
[70] Institute, A. N. “AI Now 2017 Report,” AI Now Institute. Available at: https://ainowinstitute.org/publication/ai-now-2017-report-2
[71] Keep families closer with GrandPad. Available at: https://www.grandpad.net/
[72] Lynne Rienner Publishers. Disability and the Internet Confronting a Digital Divide. Available at: https://www.rienner.com/title/Disability_and_the_Internet_Confronting_a_Digital_Divide
[73] Smartphones help blacks, Hispanics bridge some – but not all – digital gaps with whites, Benton Foundation. Available at: https://www.benton.org/headlines/smartphones-help-blacks-hispanics-bridge-some-%E2%80%93-not-all-%E2%80%93-digital-gaps-whites
[74] Technology and Social Inclusion, MIT Press. Available at: https://mitpress.mit.edu/9780262731737/technology-and-social-inclusion/
[75] Weltgesundheitsorganisation (2012). Ed., Addressing the social determinants of health: the urban dimension and the role of local government. Copenhagen: World Health Organization, Regional Office for Europe.
Published
2024-06-28
How to Cite
PHAM, Minh Khang et al.
Empowering Vulnerable Populations through Technology: Innovations and Challenges in Social Work.
Journal of Research in Educational Sciences, [S.l.], v. 15, n. 1, p. 5-16, june 2024.
ISSN 2068-8407.
Available at: <https://journals.aserspublishing.eu/jres/article/view/8492>. Date accessed: 27 july 2024.
doi: https://doi.org/10.14505/jres.v15.1(17).01.
Section
Articles
The Copyright Transfer Form to ASERS Publishing (The Publisher)
This form refers to the manuscript, which an author(s) was accepted for publication and was signed by all the authors.
The undersigned Author(s) of the above-mentioned Paper here transfer any and all copyright-rights in and to The Paper to The Publisher. The Author(s) warrants that The Paper is based on their original work and that the undersigned has the power and authority to make and execute this assignment. It is the author's responsibility to obtain written permission to quote material that has been previously published in any form. The Publisher recognizes the retained rights noted below and grants to the above authors and employers for whom the work performed royalty-free permission to reuse their materials below. Authors may reuse all or portions of the above Paper in other works, excepting the publication of the paper in the same form. Authors may reproduce or authorize others to reproduce the above Paper for the Author's personal use or for internal company use, provided that the source and The Publisher copyright notice are mentioned, that the copies are not used in any way that implies The Publisher endorsement of a product or service of an employer, and that the copies are not offered for sale as such. Authors are permitted to grant third party requests for reprinting, republishing or other types of reuse. The Authors may make limited distribution of all or portions of the above Paper prior to publication if they inform The Publisher of the nature and extent of such limited distribution prior there to. Authors retain all proprietary rights in any process, procedure, or article of manufacture described in The Paper. This agreement becomes null and void if and only if the above paper is not accepted and published by The Publisher, or is with drawn by the author(s) before acceptance by the Publisher.