This paper is in the following e-collection/theme issue:

Consumer & Patient Education and Shared-Decision Making (679) eHealth Literacy / Digital Literacy (390) Crowdsourcing and Mechanical Turks (98)

Published on in Vol 15, No 7 (2013): July

User Evaluation of the Effects of a Text Simplification Algorithm Using Term Familiarity on Perception, Understanding, Learning, and Information Retention

User Evaluation of the Effects of a Text Simplification Algorithm Using Term Familiarity on Perception, Understanding, Learning, and Information Retention

User Evaluation of the Effects of a Text Simplification Algorithm Using Term Familiarity on Perception, Understanding, Learning, and Information Retention

Authors of this article:

Gondy Leroy1, 2 ;   James E. Endicott1 ;   David Kauchak3 ;   Obay Mouradi1 ;   Melissa Just4
Article Authors Cited by (40) Tweetations (2) Metrics

Journals

  1. Kuang J, Argo L, Stoddard G, Bray B, Zeng-Treitler Q. Assessing Pictograph Recognition: A Comparison of Crowdsourcing and Traditional Survey Approaches. Journal of Medical Internet Research 2015;17(12):e281 View
  2. Smith S, Samar V. Dimensions of Deaf/Hard-of-Hearing and Hearing Adolescents’ Health Literacy and Health Knowledge. Journal of Health Communication 2016;21(sup2):141 View
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  4. Yu B, He Z, Xing A, Lustria M. An Informatics Framework to Assess Consumer Health Language Complexity Differences: Proof-of-Concept Study. Journal of Medical Internet Research 2020;22(5):e16795 View
  5. Kauchak D, Leroy G, Hogue A. Measuring text difficulty using parse‐tree frequency. Journal of the Association for Information Science and Technology 2017;68(9):2088 View
  6. Cowie J, Burstein F. A decision support tool for supporting individuals living with long-term conditions make informed choices: LTC-Choices tool for continuous healthcare. Journal of Decision Systems 2018;27(sup1):123 View
  7. Leroy G, Kauchak D. The effect of word familiarity on actual and perceived text difficulty. Journal of the American Medical Informatics Association 2014;21(e1):e169 View
  8. Al Aqeel S, Abanmy N, Aldayel A, Al-Khalifa H, Al-Yahya M, Diab M. Readability of written medicine information materials in Arabic language: expert and consumer evaluation. BMC Health Services Research 2018;18(1) View
  9. Puspitasari I, Moriyama K, Fukui K, Numao M. Effects of Individual Health Topic Familiarity on Activity Patterns During Health Information Searches. JMIR Medical Informatics 2015;3(1):e16 View
  10. Walkington C, Clinton V, Sparks A. The effect of language modification of mathematics story problems on problem-solving in online homework. Instructional Science 2019;47(5):499 View
  11. Alfano M, Lenzitti B, Lo Bosco G, Muriana C, Piazza T, Vizzini G. Design, development and validation of a system for automatic help to medical text understanding. International Journal of Medical Informatics 2020;138:104109 View
  12. Kauchak D, Leroy G. Moving Beyond Readability Metrics for Health-Related Text Simplification. IT Professional 2016;18(3):45 View
  13. Silver M. Patient Perspectives on Online Health Information and Communication With Doctors: A Qualitative Study of Patients 50 Years Old and Over. Journal of Medical Internet Research 2015;17(1):e19 View
  14. Garcia-Dia M, Fitzpatrick J, Madigan E, Peabody J. Using Text Reminder to Improve Childhood Immunization Adherence in the Philippines. CIN: Computers, Informatics, Nursing 2017;35(4):212 View
  15. Kloehn N, Leroy G, Kauchak D, Gu Y, Colina S, Yuan N, Revere D. Improving Consumer Understanding of Medical Text: Development and Validation of a New SubSimplify Algorithm to Automatically Generate Term Explanations in English and Spanish. Journal of Medical Internet Research 2018;20(8):e10779 View
  16. Lu W, Guttentag A, Elbel B, Kiszko K, Abrams C, Kirchner T. Crowdsourcing for Food Purchase Receipt Annotation via Amazon Mechanical Turk: A Feasibility Study. Journal of Medical Internet Research 2019;21(4):e12047 View
  17. Harber P, Leroy G. Assessing Work–Asthma Interaction With Amazon Mechanical Turk. Journal of Occupational & Environmental Medicine 2015;57(4):381 View
  18. Leroy G, Kauchak D, Hogue A. Effects on Text Simplification: Evaluation of Splitting Up Noun Phrases. Journal of Health Communication 2016;21(sup1):18 View
  19. Arfé B, Mason L, Fajardo I. Simplifying informational text structure for struggling readers. Reading and Writing 2018;31(9):2191 View
  20. Adamusiak T, Shimoyama N, Shimoyama M. Next Generation Phenotyping Using the Unified Medical Language System. JMIR Medical Informatics 2014;2(1):e5 View
  21. Shardlow M, Sellar S, Rousell D. Collaborative augmentation and simplification of text (CoAST): pedagogical applications of natural language processing in digital learning environments. Learning Environments Research 2022;25(2):399 View
  22. Xie J, Zhang B, Brown S, Zeng D. Write Like a Pro or an Amateur? Effect of Medical Language Formality. ACM Transactions on Management Information Systems 2021;12(3):1 View
  23. Jing X. UMLS at 30 years: How it is used and published (Preprint). JMIR Medical Informatics 2020 View
  24. Ondov B, Attal K, Demner-Fushman D. A survey of automated methods for biomedical text simplification. Journal of the American Medical Informatics Association 2022;29(11):1976 View
  25. August T, Wang L, Bragg J, Hearst M, Head A, Lo K. Paper Plain : Making Medical Research Papers Approachable to Healthcare Consumers with Natural Language Processing. ACM Transactions on Computer-Human Interaction 2023;30(5):1 View
  26. Nattam A, Vithala T, Wu T, Bindhu S, Bond G, Liu H, Thompson A, Wu D. Assessing the Readability of Online Patient Education Materials in Obstetrics and Gynecology Using Traditional Measures: Comparative Analysis and Limitations. Journal of Medical Internet Research 2023;25:e46346 View
  27. Säuberli A, Hansen-Schirra S, Holzknecht F, Gutermuth S, Deilen S, Schiffl L, Ebling S. Enabling text comprehensibility assessment for people with intellectual disabilities using a mobile application. Frontiers in Communication 2023;8 View
  28. Benson‐Goldberg S, Geist L, Erickson K. Simplified COVID‐19 guidance for adults with intellectual and developmental disabilities. Journal of Applied Research in Intellectual Disabilities 2024;37(3) View
  29. Rahman M, Irbaz M, North K, Williams M, Zampieri M, Lybarger K. Health text simplification: An annotated corpus for digestive cancer education and novel strategies for reinforcement learning. Journal of Biomedical Informatics 2024;158:104727 View

Books/Policy Documents

  1. Ermakova L, Bellot P, Braslavski P, Kamps J, Mothe J, Nurbakova D, Ovchinnikova I, San-Juan E. Advances in Information Retrieval. View
  2. Ermakova L, Bellot P, Braslavski P, Kamps J, Mothe J, Nurbakova D, Ovchinnikova I, SanJuan E. Experimental IR Meets Multilinguality, Multimodality, and Interaction. View
  3. Brahmananda Reddy A, Vaishnavi P, Jahnavi M, Sameeksha G, Sandhya K. Proceedings of Second International Conference on Advances in Computer Engineering and Communication Systems. View
  4. Kauchak D, Song V, Mishra P, Leroy G, Harber P, Rains S, Hamre J, Morgenstein N. Intelligent Systems and Applications. View
  5. Chattaraj D, Vijayaraghavan A. Pedagogy of Space and The Global South. View