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Making Cancer Health Text on the Internet Easier to Read for Deaf People Who Use American Sign Language

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Abstract

People with relatively limited English language proficiency find the Internet’s cancer and health information difficult to access and understand. The presence of unfamiliar words and complex grammar make this particularly difficult for Deaf people. Unfortunately, current technology does not support low-cost, accurate translations of online materials into American Sign Language. However, current technology is relatively more advanced in allowing text simplification, while retaining content. This research team developed a two-step approach for simplifying cancer and other health text. They then tested the approach, using a crossover design with a sample of 36 deaf and 38 hearing college students. Results indicated that hearing college students did well on both the original and simplified text versions. Deaf college students’ comprehension, in contrast, significantly benefitted from the simplified text. This two-step translation process offers a strategy that may improve the accessibility of Internet information for Deaf, as well as other low-literacy individuals.

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References

  1. Stableford S, Mettger W (2007) Plain language: a strategic response to the health literacy challenge. J Public Health Policy 28(1):71–93

    Article  PubMed  Google Scholar 

  2. Ellimoottil C, Polcari A, Kadlec A, Gupta G (2012) Readability of websites containing information about prostate cancer treatment options. J Urol. doi:10.1016/j.juro.2012.07.105

    Google Scholar 

  3. Pruthi A, Nielsen ME, Raynor MC, Woods ME, Wallen EM, Smith AB (2015) Readability of American online patient education materials in urologic oncology: a need for simple communication. Urology 85:351–356

    Article  PubMed  Google Scholar 

  4. Vargas CR, Ricci JA, Chuang DJ, Lee BT (2015) Online patient resources for liposuction. Ann Plast Surg. doi:10.1097/SAP.0000000000000438

    Google Scholar 

  5. Patton LL, George SF, Hollowell RP (2014) Content, quality, and readability of website information on dental care for patients with cancer. Oral Surg Oral Med Oral Pathol Oral Radiol. doi:10.1016/j.oooo.2014.03.019

    PubMed  Google Scholar 

  6. Traxler CB (2000) Measuring up to performance standards in reading and mathematics: achievement of selected deaf and hard-of-hearing students in the national norming of the 9th Edition Stanford Achievement Test. J Deaf Stud Deaf Educ 5:337–348

    Article  CAS  PubMed  Google Scholar 

  7. Qi S, Mitchell RE (2011) Large-scale academic achievement testing of deaf and hard-of-hearing students: past, present, and future. J Deaf Stud Deaf Educ. doi:10.1093/deafed/enr028

    PubMed  Google Scholar 

  8. Hopper MJ (2011) Positioned as bystanders: deaf students’ experiences and perceptions of informal learning phenomena (doctoral dissertation). University of Rochester, Rochester, NY

    Google Scholar 

  9. Convertino C, Borgna G, Marschark M, Durkin A (2014) Word and world knowledge among deaf learners with and without cochlear implants. J Deaf Stud Deaf Educ. doi:10.1093/deafed/enu024

    PubMed  PubMed Central  Google Scholar 

  10. Smith CE, Massey-Stokes M, Lieberth A (2012) Health information needs of d/Deaf adolescent females: a call to action. Am Ann Deaf 157:41–47

    Article  PubMed  Google Scholar 

  11. Kuenburg A, Fellinger P, Fellinger J (2016) Health care access among deaf people. J Deaf Stud Deaf Educ 21(1):1–10

    Article  PubMed  Google Scholar 

  12. Kushalnagar P, Naturale J, Paludneviciene R, Smith SR, Werfel E, Doolittle R, Jacobs S, DeCaro J (2015) Health websites: accessibility and usability for American sign language users. Health Commun. doi:10.1080/10410236.2013.853226

    PubMed  Google Scholar 

  13. Pascual A, Ribera M, Granollers T (2014) Impact of web accessibility barriers on users with hearing impairment. Proceedings of the XV international conference on human computer interaction. ACM, Puerto de la Cruz, Tenerife, Spain, pp. 1–2

    Google Scholar 

  14. Siddharthan A (2014) A survey of research on text simplification. Int J Appl Linguistics 165(2):259–298

    Google Scholar 

  15. Chandrasekar R, Srinivas B (1997) Automatic induction of rules for text simplification. Knowl-Based Syst 10:183–190

    Article  Google Scholar 

  16. Xu W, Callison-Burch C, Napoles C (2015) Problems in current text simplification research: new data can help. Trans Assoc Comput Linguist 3:283–297 Retrieved from https://tacl2013.cs.columbia.edu/ojs/index.php/tacl/article/view/549

  17. Štajner S, Mitkov R, Saggion H (2014) One step closer to automatic evaluation of text simplification systems. In: Proceedings of the 3rd workshop on predicting and improving text readability for target reader populations (PITR)@ EACL pp 1–10

  18. Shardlow M (2014) A survey of automated text simplification. Int J Adv Comput Sci Appl 4(1). doi:10.14569/SpecialIssue.2014.040109

  19. Štajner S, Evans R, Orasan C, Mitkov R (2012) What can readability measures really tell us about text complexity. In: Proceedings of workshop on natural language processing for improving textual accessibility pp 14–22

  20. Rello L (2012) DysWebxia: a model to improve accessibility of the textual web for dyslexic users. In: SIGACCESS accessible computing 102: 41–44. ACM, New York

  21. Petersen SE, Ostendorf M (2007) Text simplification for language learners: a corpus analysis. In: Proceedings of workshop on speech and language technology for education

  22. Howlader N, Noone AM, Krapcho M, Neyman N, Aminou R, Altekruse SF, Kosary CL, et al. (2012) SEER cancer statistics review, 1975–2009 (Vintage 2009 Populations). National Cancer Institute, Bethesda Retrieved September 7, 2012

    Google Scholar 

  23. Kincaid JP, Fishburne RP Jr, Rogers RL, Chissom BS (1975) Derivation of new readability formulas (Automated Readability Index, Fog Count and Flesch Reading Ease Formula) for Navy-enlisted personnel (No. RBR-8-75). Naval technical training command Millington TN research branch

  24. Baker DW, Williams MV, Parker RM, Gazmararian JA, Nurss J (1999) Development of a brief test to measure functional health literacy. Patient Educ Couns 38(1):33–42

    Article  CAS  PubMed  Google Scholar 

  25. Margellos-Anast H, Hedding T, Miller L (2004) Improving access to health and mental health for Chicago’s deaf community: a survey of deaf adults. Sinai Health System and Advocate Health Care, Chicago

    Google Scholar 

  26. Koehn P, Hoang H, Birch A, Callison-Burch C, Federico M, Bertoldi N, Cowan B et al (2007) Moses: open source toolkit for statistical machine translation. In: Proceedings of the 45th annual meeting of the ACL on interactive poster and demonstration sessions (pp. 177–180). Association for computational linguistics

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Authors and Affiliations

  1. Gallaudet University, Washington, DC, USA

    Poorna Kushalnagar & Raja Kushalnagar

  2. Rochester Institute of Technology, Rochester, NY, USA

    Scott Smith, Melinda Hopper & Micah Rinkevich

  3. University of Texas at Austin, Austin, TX, USA

    Claire Ryan

Authors
  1. Poorna Kushalnagar
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  2. Scott Smith
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  3. Melinda Hopper
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  4. Claire Ryan
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  5. Micah Rinkevich
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  6. Raja Kushalnagar
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Correspondence to Poorna Kushalnagar.

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Kushalnagar, P., Smith, S., Hopper, M. et al. Making Cancer Health Text on the Internet Easier to Read for Deaf People Who Use American Sign Language. J Canc Educ 33, 134–140 (2018). https://doi.org/10.1007/s13187-016-1059-5

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  • DOI: https://doi.org/10.1007/s13187-016-1059-5

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