دورية أكاديمية
أعرض في EDS

Audio delivery of health information: An NLP study of information difficulty and bias in listeners.

التفاصيل البيبلوغرافية
العنوان: Audio delivery of health information: An NLP study of information difficulty and bias in listeners.
المؤلفون: Ahmed A; The University of Arizona, Tucson 85721, U.S.A., Leroy G; The University of Arizona, Tucson 85721, U.S.A., Lu HY; The University of Arizona, Tucson 85721, U.S.A., Kauchak D; Pomona College, 333N College Way, Claremont 91711, U.S.A., Stone J; The University of Arizona, Tucson 85721, U.S.A., Harber P; The University of Arizona, Tucson 85721, U.S.A., Rains SA; The University of Arizona, Tucson 85721, U.S.A., Mishra P; The University of Arizona, Tucson 85721, U.S.A., Chitroda B; The University of Arizona, Tucson 85721, U.S.A.
المصدر: Procedia computer science [Procedia Comput Sci] 2023; Vol. 219, pp. 1509-1517. Date of Electronic Publication: 2023 Mar 22.
نوع المنشور: Journal Article
اللغة: English
بيانات الدورية: Publisher: Elsevier Country of Publication: Netherlands NLM ID: 101537771 Publication Model: Print-Electronic Cited Medium: Print ISSN: 1877-0509 (Print) NLM ISO Abbreviation: Procedia Comput Sci Subsets: PubMed not MEDLINE
أسماء مطبوعة: Original Publication: [Amsterdam] : Elsevier
مستخلص: Health literacy is the ability to understand, process, and obtain health information and make suitable decisions about health care [3]. Traditionally, text has been the main medium for delivering health information. However, virtual assistants are gaining popularity in this digital era; and people increasingly rely on audio and smart speakers for health information. We aim to identify audio/text features that contribute to the difficulty of the information delivered over audio. We are creating a health-related audio corpus. We selected text snippets and calculated seven text features. Then, we converted the text snippets to audio snippets. In a pilot study with Amazon Mechanical Turk (AMT) workers, we measured the perceived and actual difficulty of the audio using the response of multiple choice and free recall questions. We collected demographic information as well as bias about doctors' gender, task preference, and health information preference. Thirteen workers completed thirty audio snippets and related questions. We found a strong correlation between text features lexical chain, and the dependent variables, and multiple choice response, percentage of matching word, percentage of similar word, cosine similarity, and time taken (in seconds). In addition, doctors were generally perceived to be more competent than warm. How warm workers perceive male doctors correlated significantly with perceived difficulty.
References: Soc Sci Med. 2004 Mar;58(6):1049-58. (PMID: 14723901)
Health Aff (Millwood). 2012 Feb;31(2):434-43. (PMID: 22262723)
Health Commun. 2020 May;35(6):726-730. (PMID: 30835569)
Assessment. 2020 Dec;27(8):1870-1885. (PMID: 30095000)
Curr Opin Behav Sci. 2015 Jun;3:45-50. (PMID: 27453920)
J Behav Med. 2001 Dec;24(6):573-86. (PMID: 11778351)
IEEE J Biomed Health Inform. 2019 Sep;23(5):2164-2173. (PMID: 30530380)
Int J Public Health. 2009;54(5):313-24. (PMID: 19644651)
AMIA Annu Symp Proc. 2008 Nov 06;:394-8. (PMID: 18998902)
J Pers Soc Psychol. 2002 Jun;82(6):878-902. (PMID: 12051578)
معلومات مُعتمدة: R01 LM011975 United States LM NLM NIH HHS
فهرسة مساهمة: Keywords: Health information; Health literacy; NLP; Natural Language Processing; Text features; audio information delivery
تواريخ الأحداث: Date Created: 20230519 Latest Revision: 20230522
رمز التحديث: 20231215
مُعرف محوري في PubMed: PMC10191245
DOI: 10.1016/j.procs.2023.01.442
PMID: 37205132
قاعدة البيانات: MEDLINE
الوصف
تدمد:1877-0509
DOI:10.1016/j.procs.2023.01.442