Tracey L Gendron1, E Ayn Welleford2, Jennifer Inker2, John T White2. 1. Department of Gerontology, Virginia Commonwealth University, Richmond. tlgendro@vcu.edu. 2. Department of Gerontology, Virginia Commonwealth University, Richmond.
Abstract
PURPOSE: Language carries and conveys meaning which feeds assumptions and judgments that can lead to the development of stereotypes and discrimination. As a result, this study closely examined the specific language that is used to communicate attitudes and perceptions of aging and older adults. DESIGN AND METHODS: We conducted a qualitative study of a twitter assignment for 236 students participating in a senior mentoring program. Three hundred fifty-four tweets were qualitatively analyzed to explore language-based age discrimination using a thematic analytic approach. RESULTS: Twelve percent of the tweets (n = 43) were found to contain discriminatory language. Thematic analysis of the biased tweets identified 8 broad themes describing language-based age discrimination: assumptions and judgments, older people as different, uncharacteristic characteristics, old as negative, young as positive, infantilization, internalized ageism, and internalized microaggression. IMPLICATIONS: The language of ageism is rooted in both explicit actions and implicit attitudes which make it highly complex and difficult to identify. Continued examination of linguistic encoding is needed in order to recognize and rectify language-based age discrimination.
PURPOSE: Language carries and conveys meaning which feeds assumptions and judgments that can lead to the development of stereotypes and discrimination. As a result, this study closely examined the specific language that is used to communicate attitudes and perceptions of aging and older adults. DESIGN AND METHODS: We conducted a qualitative study of a twitter assignment for 236 students participating in a senior mentoring program. Three hundred fifty-four tweets were qualitatively analyzed to explore language-based age discrimination using a thematic analytic approach. RESULTS: Twelve percent of the tweets (n = 43) were found to contain discriminatory language. Thematic analysis of the biased tweets identified 8 broad themes describing language-based age discrimination: assumptions and judgments, older people as different, uncharacteristic characteristics, old as negative, young as positive, infantilization, internalized ageism, and internalized microaggression. IMPLICATIONS: The language of ageism is rooted in both explicit actions and implicit attitudes which make it highly complex and difficult to identify. Continued examination of linguistic encoding is needed in order to recognize and rectify language-based age discrimination.
Authors: Nazihah Rejab; Noor Azimah Muhammad; Hizlinda Tohid; Noorlaili Mohd Tohit; Pok Wen Kin; Ismail Drahman Journal: Ann Geriatr Med Res Date: 2022-07-11