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Aim: This paper reports on the second phase of a national study in Wales. The research aimed to assess the level of Welsh language awareness amongst healthcare professionals across Wales, and to identify the factors that enhance language choice within service delivery.
Background: The literature suggests that language sensitive healthcare practice is central to ensuring high quality care. However, it is evident that language barriers continue to compromise the quality of care within nursing and other health services. One issue that has received little attention is the level of language awareness that healthcare professionals currently demonstrate. Furthermore the factors that influence language choice for bilingual/multilingual speakers are not well explored in the literature.
Methods: The study involved semi-structured interviews with a range of healthcare professionals in acute and community settings across Wales. Using a systematic sampling matrix, a purposeful sample of 83 professionals was selected to participate. Twenty-seven of the respondents were nurses, health visitors and midwives. The interviews focussed on the factors that facilitate or impede language sensitive healthcare practice. All interviews were audiotaped and, using a framework analysis approach, conceptual codes were developed and defined and categories and sub-categories were constructed to create thematic charts.
Findings: Three main themes were identified: care enhancement, which focussed on the process and outcome of offering language choice to bilingual patients; organizational issues, which reflected issues relating to the infrastructure of service provision; and training implications, which focused on Welsh language learning in health care.
Conclusions: Complex dynamics of language use are in operation within bilingual healthcare settings and organizational as well as individual factors are important in facilitating appropriate language use. Many of the issues highlighted are not peculiar to the Welsh context, but apply to healthcare settings across the world, where other minority languages are in use.
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http://dx.doi.org/10.1111/j.1365-2648.2006.03733.x | DOI Listing |
BMC Med Educ
September 2025
Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, 171 77, Sweden.
Background: Health professions students may encounter a range of stressors during their clinical education that may impact their quality of life. This study aimed to explore how various health professions students perceive their quality of life and the environment in which they develop their clinical skills.
Methods: An online survey was administered among registered undergraduate students in the physiotherapy, speech-language pathology, nursing, or medical programs.
Clin Rheumatol
September 2025
Histocompatibility Department, Hedi Chaker UH, University of Sfax, Sfax, Tunisia.
Objective: Systemic sclerosis (SSc) is a complex autoimmune connective tissue disease. Genetic factors may play a pivotal role in determining susceptibility to these disorders. HLA associations with SSc, especially HLA class II, were investigated in different populations but not in Tunisia.
View Article and Find Full Text PDFBehav Res Methods
September 2025
Laboratoire de Psychologie, Université de Bordeaux, LabPsy UR 4139, 3 Place de la Victoire, 33076, Bordeaux Cedex, France.
This article presents a new set of semantic feature production norms, collected from 580 young adults, for 360 French concepts across various semantic categories. Although empirically derived feature norms have been developed for several languages and have been shown to be useful for investigating semantic memory and providing assessment tools, none are currently available for native French-speaking populations. In this study, the participants performed a property generation task in which they were asked to list features to describe the characteristics of each given concept (e.
View Article and Find Full Text PDFJ Imaging Inform Med
September 2025
Department of Diagnostic, Interventional and Pediatric Radiology (DIPR), Inselspital, Bern University Hospital and University of Bern, Bern, Switzerland.
Large language models (LLMs) have been successfully used for data extraction from free-text radiology reports. Most current studies were conducted with LLMs accessed via an application programming interface (API). We evaluated the feasibility of using open-source LLMs, deployed on limited local hardware resources for data extraction from free-text mammography reports, using a common data element (CDE)-based structure.
View Article and Find Full Text PDFJ Imaging Inform Med
September 2025
Department of Biomedical Engineering, Gachon University, Seongnam-Si 13120, Gyeonggi-Do, Republic of Korea.
To develop and validate a deep-learning-based algorithm for automatic identification of anatomical landmarks and calculating femoral and tibial version angles (FTT angles) on lower-extremity CT scans. In this IRB-approved, retrospective study, lower-extremity CT scans from 270 adult patients (median age, 69 years; female to male ratio, 235:35) were analyzed. CT data were preprocessed using contrast-limited adaptive histogram equalization and RGB superposition to enhance tissue boundary distinction.
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