Purpose: To verify clinical validity evidence for the ineffective social support network nursing diagnosis.
Method: A quantitative, descriptive, cross-sectional study was performed with 98 violence-victimized women treated in two reference centers for violence in the city of Recife, Pernambuco, Brazil. The women were interviewed from August 2021 to June 2022.
Findings: The clinical indicators that best predicted the nursing diagnosis were as follows: Frustration with unmet support expectations, negative social interaction, perceived neglect of support demands, feeling of abandonment, low reciprocity, and encouragement of negative behaviors. Etiological factors that showed greater association were excessive demand for support, limited social network, social isolation, the fragility of institutional service networked organizations, and inadequate appreciation of available social support.
Conclusions: The clinical validity evidence for the ineffective social support network nursing diagnosis has been verified. Thus, the validated clinical indicators and etiological factors can accurately diagnose and predict the emergence of this phenomenon in violence-victimized women.
Implications For Nursing Practice: The results contribute to advancing scientific knowledge in nursing teaching, research, and practice and support the nursing process in violence-victimized women.
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http://dx.doi.org/10.1111/2047-3095.12476 | DOI Listing |
Psychooncology
January 2025
Department of Psychology, University of California, Los Angeles, California, USA.
Background: Black women generally report high levels of spirituality. Less is known about Black women's spiritual coping with a cancer diagnosis. Persisting health disparities between Black breast cancer survivors and other racial groups necessitate examining whether spirituality can be a contextual and personal resource for Black women with breast cancer.
View Article and Find Full Text PDFVaccines (Basel)
January 2025
Department of Family and Geriatric Nursing, Faculty of Health Sciences, Medical University of Lublin, Staszica 6 Str., PL-20-081 Lublin, Poland.
Background: Vaccination is one of the most effective ways of protecting individuals against serious infectious diseases and their fatal consequences.
Objectives: The aim of this scoping review was to synthesize data on parental attitudes toward vaccination and identify factors influencing the motivators and barriers to children's vaccination based on Polish studies.
Methods: The scoping review process and reporting were based on the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScRs) checklist.
Front Vet Sci
January 2025
Flourish Veterinary Consulting, Firestone, CO, United States.
Individuals in the veterinary profession are experiencing significant mental health and wellbeing challenges. A holistic view of wellbeing, which encompasses both physical and mental health, underscores their interconnected nature. This integrated approach reduces the artificial separation of wellbeing facets, and highlights how mental states influence not only individuals, but also their interactions with animals, the environment, and others in the workplace.
View Article and Find Full Text PDFLung
January 2025
Division of Pulmonary and Critical Care Medicine, Albany Medical College, 16 New Scotland Avenue, MC-91, Albany, NY, 12208, USA.
Purpose: The priorities and concerns of sarcoidosis patients in the United States (US) have not been well-described.
Methods: A survey constructed by sarcoidosis patients and doctors was administered to US sarcoidosis patients. The survey queried patients concerning their demographics, disease state, disease impact on health and well-being, health care priorities and impressions of sarcoidosis care.
Sci Rep
January 2025
College of Mathematics and Systems Science, Shandong University of Science and Technology, Qingdao, 266510, China.
The ability to assess and manage corporate credit risk enables financial institutions and investors to mitigate risk, enhance the precision of their decision-making, and adapt their strategies in a prompt and effective manner. The growing quantity of data and the increasing complexity of indicators have rendered traditional machine learning methods ineffective in enhancing the accuracy of credit risk assessment. Consequently, academics have begun to explore the potential of models based on deep learning.
View Article and Find Full Text PDFEnter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!