Purpose/objectives: To use transformative learning to investigate what experiences serve as catalysts for mammography screening, the cognitive and affective responses that result from the catalyst, and how screening behavior is impacted.
Research Approach: A descriptive qualitative study.
Setting: Southeastern Wyoming.
Participants: 25 low-income, rural women aged 40 years and older.
Methodologic Approach: Four focus group interviews.
Findings: Cancer experiences triggered universal responses of fear by screeners and nonscreeners. The manner in which that fear response was interpreted was a critical factor in the facilitation of, or impedance to, screening. Dichotomous interpretations of fear responses provided the context for screening behavior. Immobilizing and isolating experiences were associated with nonscreening behavior, whereas motivation and self-efficacy were associated with screening behavior.
Conclusions: Transformative learning theory is a useful framework from which to explain differences in mammography screening behavior. Creating opportunities that facilitate dialogue and critical reflection hold the potential to change immobilizing and isolating frames of reference in nonscreening women.
Interpretation: To help women transcend their fear and become self-efficacious, nurses can assess how cancer and the screening experience is viewed and, if indicated, move beyond standard education and offer opportunities for dialogue and critical reflection.
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http://dx.doi.org/10.1188/14.ONF.176-184 | DOI Listing |
Waste Manag
January 2025
Chair of Waste Processing Technology and Waste Management, Montanuniversitaet Leoben, Leoben, Austria. Electronic address:
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Department of Plastic, Reconstructive and Aesthetic Surgery, Faculty of Medicine, Altınbas University, Istanbul, Turkey.
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January 2025
Industrial Systems Institute (ISI), Athena Research and Innovation Center, 26504 Patras, Greece.
The integration of deep learning (DL) into image processing has driven transformative advancements, enabling capabilities far beyond the reach of traditional methodologies. This survey offers an in-depth exploration of the DL approaches that have redefined image processing, tracing their evolution from early innovations to the latest state-of-the-art developments. It also analyzes the progression of architectural designs and learning paradigms that have significantly enhanced the ability to process and interpret complex visual data.
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Guthrie Cortland Medical Center, Cortland, NY 13045, USA.
Artificial intelligence (AI) in echocardiography represents a transformative advancement in cardiology, addressing longstanding challenges in cardiac diagnostics. Echocardiography has traditionally been limited by operator-dependent variability and subjective interpretation, which impact diagnostic reliability. This study evaluates the role of AI, particularly machine learning (ML), in enhancing the accuracy and consistency of echocardiographic image analysis and its potential to complement clinical expertise.
View Article and Find Full Text PDFJ Clin Med
January 2025
Department of Neurosurgery, "Carol Davila" University of Medicine and Pharmacy, 020021 Bucharest, Romania.
The convergence of Artificial Intelligence (AI) and neuroscience is redefining our understanding of the brain, unlocking new possibilities in research, diagnosis, and therapy. This review explores how AI's cutting-edge algorithms-ranging from deep learning to neuromorphic computing-are revolutionizing neuroscience by enabling the analysis of complex neural datasets, from neuroimaging and electrophysiology to genomic profiling. These advancements are transforming the early detection of neurological disorders, enhancing brain-computer interfaces, and driving personalized medicine, paving the way for more precise and adaptive treatments.
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