Perceived teacher autonomy support in nurse education remains understudied in the literature. This study examined the relationship between students' perceived teacher autonomy support, perceived competence in learning, and academic performance. A cross-sectional correlation descriptive design was used for 225 participants, undergraduate nursing students studying in Saudi Arabia. Perceived teacher autonomy support, perceived competence in learning, and academic performance were measured using the Learning Climate Questionnaire, Perceived Competence Scale for Learning, and student grade point average, respectively. The results revealed a high level of perceived teacher autonomy support and perceived competence in learning among the nursing students, with students in the internship year (final year) reporting higher perceived teacher autonomy support than students in other years. There was a strong positive correlation between perceived teacher autonomy support and perceived competence in learning. Further, students' perceived teacher autonomy support predicted their academic performance, indicating that those with high perceived teacher autonomy support were more likely to have a higher grade point average. Nurse educators must prioritize student autonomy support for better learning and performance, especially upon enrollment in a nursing program.
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http://dx.doi.org/10.1111/nhs.13090 | DOI Listing |
Res Nurs Health
January 2025
Department of Kinesiology, Sport, and Recreation, College of Health and Human Services, Eastern Illinois University, Charleston, Illinois, USA.
The objectives of this study were to characterize burnout in five different health professions (i.e., pharmacists, nurses, occupational therapists, psychologists, and mental health counselors) as well as to determine if moral distress, ethical stress, and/or ethical climate were predictive of burnout and job satisfaction.
View Article and Find Full Text PDFBMC Psychol
January 2025
Department of Psychology, MSB Medical School Berlin, Rüdesheimer Str. 50, Berlin, 14197, Germany.
Background: A growing body of research suggests that the provision of social support can have benefits not only for the recipients but also for the provider. Although initial evidence for affective, self-evaluative and physiological outcomes has been established, the beneficial effects of support provision do not occur consistently across all support interactions, and some interactions may even have detrimental effects on providers. The aim of our experimental paradigm is to enable researchers to test the conditions under which the provision of social support to dyadic partners affects affective, self-evaluative, physiological, and relationship outcomes for the provider.
View Article and Find Full Text PDFBMC Med Ethics
January 2025
Department of Pharmacy, Makerere University, Kampala, Uganda.
Background: Shared decision-making in healthcare is a collaborative process where patients are supported to make informed decisions according to their preferences. Healthcare decisions affect patients' lives which necessitates patients to participate in decisions concerning their health. This study explored experiences and ethical issues related to shared decision-making in a rural healthcare setting.
View Article and Find Full Text PDFBackground: There is substantial interest among policy makers in using telecare to support independence in older adults. However, research on how telecare can be most beneficial in promoting independence is limited. This realist review aimed to understand the contexts in which telecare can support independence and for whom, to aid older people in remaining at home.
View Article and Find Full Text PDFAnn Intern Med
January 2025
Clinical Epidemiology and Research Center (CERC), Department of Biomedical Sciences, Humanitas University, and IRCCS Humanitas Research Hospital, Milan, Italy, and Fraunhofer Institute for Translational Medicine and Pharmacology ITMP, Allergology and Immunology, Berlin, Germany (H.J.S.).
Description: Artificial intelligence (AI) has been defined by the High-Level Expert Group on AI of the European Commission as "systems that display intelligent behaviour by analysing their environment and taking actions-with some degree of autonomy-to achieve specific goals." Artificial intelligence has the potential to support guideline planning, development and adaptation, reporting, implementation, impact evaluation, certification, and appraisal of recommendations, which we will refer to as "guideline enterprise." Considering this potential, as well as the lack of guidance for the use of AI in guidelines, the Guidelines International Network (GIN) proposes a set of principles for the development and use of AI tools or processes to support the health guideline enterprise.
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