Background: Students from culturally and linguistically diverse backgrounds experience language barriers that influence their progression through nursing programs. Linguistic modification is a strategy that eliminates unnecessary wording and cultural bias to improve learning outcomes for students from diverse backgrounds. This integrative review adds to the knowledge of best practices in linguistic modification that can be applied to designing case studies for nursing students with diverse backgrounds.
Method: A comprehensive review of the literature was conducted in scholarly scientific databases from 2002 to the present.
Results: Twenty-three articles discussed linguistic modification or summarized previous literature. Only four research studies were found pertaining to linguistic modification for culturally and linguistically diverse nursing students. Faculty and student practices that ameliorated language barriers were identified.
Conclusion: The use of linguistic modification throughout a nursing curriculum creates an inclusive learning environment. Further research is needed on linguistic modification in nursing education. .
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http://dx.doi.org/10.3928/01484834-20230712-05 | DOI Listing |
J Child Lang
January 2025
ELTE-HUN-REN NAP Comparative Ethology research group, Research Centre for Natural Sciences, Institute of Cognitive Neuroscience and Psychology, Budapest, Hungary.
By comparing infant-directed speech to spouse- and dog-directed talk, we aimed to investigate how pitch and utterance length are modulated by speakers considering the speech context and the partner's expected needs and capabilities. We found that mean pitch was modulated in line with the partner's attentional needs, while pitch range and utterance length were modulated according to the partner's expected linguistic competence. In a situation with a nursery rhyme, speakers used the highest pitch and widest pitch range with all partners suggesting that infant-directed context greatly influences these acoustic features.
View Article and Find Full Text PDFJ Patient Rep Outcomes
January 2025
Ruhr-Universität Bochum, Bochum, Germany.
Background: Patients with Rheumatic and Musculoskeletal Diseases, including axial spondyloarthritis (axSpA), may suffer from stressors like pain and functional impairments leading to limitations in their self-perceived health status. The COping with Rheumatic Stressors (CORS) questionnaire was developed to analyze how patients cope with these stressors. The CORS is currently not available in German.
View Article and Find Full Text PDFJ Interprof Care
January 2025
Medical Education Center, University of Medicine and Pharmacy at Ho Chi Minh City, Ho Chi Minh City, Vietnam.
The Interprofessional Attitudes Scale (IPAS) is a promising tool for assessing interprofessional education (IPE) attitudes, especially in community-oriented initiatives. To meet the need for validated IPE tools in Vietnam, we translated the IPAS into Vietnamese (Viet-IPAS) and evaluated its psychometric properties. The preparatory phases included forward and backward translation, expert consultations, and student feedback, resulting in strong content validity, face validity, linguistic comprehensibility, and test-retest reliability.
View Article and Find Full Text PDFAm J Speech Lang Pathol
January 2025
Department of Speech, Language, and Hearing Sciences, The University of Arizona, Tucson.
Purpose: Conversational recasting treatment is generally effective. However, different versions of this treatment and different targets may yield different outcomes for children. Here, we directly compare multiple variations of conversational recasting to determine how modifications to delivery and target impact treatment outcomes.
View Article and Find Full Text PDFBehav Res Methods
December 2024
Department of Linguistics and Modern Languages, The Chinese University of Hong Kong, Hong Kong, China.
The study of large language models (LLMs) and LLM-powered chatbots has gained significant attention in recent years, with researchers treating LLMs as participants in psychological experiments. To facilitate this research, we developed an R package called "MacBehaviour " ( https://github.com/xufengduan/MacBehaviour ), which interacts with over 100 LLMs, including OpenAI's GPT family, the Claude family, Gemini, Llama family, and other open-weight models.
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