Recognising and attending to patients' most relevant issues and main concerns are the basis for patient-oriented work. This qualitative study investigates the ways in which doctors communicate with their patients. The method of study is conversation and discourse analysis. The source of data are audio recordings of 20 introductory medical consultations in an oncological outpatient department in Austria. In a macro-analytical approach the duration of verbal contribution as well as the topics mentioned are analyzed. Results show that 34% of the consultation time is used for activities other than the actual doctor-patient-communication. Furthermore, the share of patients' verbal contribution was found to be half that of the doctor. Much room is given to information about chemotherapy, less is dedicated to topics like the stages of the illness and the hope for recovery. The micro-analytical approach shows that patients keep trying to allude to topics which are especially relevant to them. This happens very subtly and implicitly by means of interactional markings of relevance. These are communicative and interactive methods such as a change in volume or in speech patterns, the use of strong metaphors or hesitation phenomena. Doctors, however, often give insufficient attention to such initiatives from patients and follow their own, often institutionally-related, pre-requisites. Drawing on two examples, this article shows how insufficient attention to patient-relevant issues results in a lower quality of doctor-patient-communication, and lower satisfaction of patients and doctors. Two positive examples show that adequate attention to patient-relevant issues is possible and increases quality of doctor-patient-communication, as well as participants' satisfaction. It is argued that insufficient attention to patient-relevant issues also reduces time efficiency.
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Sci Rep
January 2025
Faculty of Science and Technology, Suan Sunandha Rajabhat University, Bangkok, 10300, Thailand.
Attention mechanisms such as the Convolutional Block Attention Module (CBAM) can help emphasize and refine the most relevant feature maps such as color, texture, spots, and wrinkle variations for the avocado ripeness classification. However, the CBAM lacks global context awareness, which may prevent it from capturing long-range dependencies or global patterns such as relationships between distant regions in the image. Further, more complex neural networks can improve model performance but at the cost of increasing the number of layers and train parameters, which may not be suitable for resource constrained devices.
View Article and Find Full Text PDFThe rapid growth of modern Internet applications demands ever-increasing transmission capacity and reduced latency in optical interconnect systems utilizing intensity modulation and direct detection (IM/DD). However, the intrinsic limitations of silica-based standard single-mode fiber (SMF) will ultimately be insufficient to meet these escalating demands. The nested antiresonant nodeless fiber (NANF), a newly designed hollow-core fiber, has garnered significant attention as a potential solution to these challenges.
View Article and Find Full Text PDFSpectral analysis is a widely used method for monitoring photosynthetic capacity. However, vegetation indices-based linear regression exhibits insufficient utilization of spectral information, while full spectra-based traditional machine learning has limited representational capacity (partial least squares regression) or uninterpretable (convolution). In this study, we proposed a deep learning model with enhanced interpretability based on attention and vegetation indices calculation for global spectral feature mining to accurately estimate photosynthetic capacity.
View Article and Find Full Text PDFSci Rep
January 2025
Key Laboratory of Ethnic Language Intelligent Analysis and Security Governance of MOE, Minzu University of China, Beijing, 100081, China.
Aspect Category Sentiment Analysis (ACSA) is a fine-grained sentiment analysis task aimed at predicting the sentiment polarity associated with aspect categories within a sentence.Most existing ACSA methods are based on a given aspect category to locate sentiment words related to it. When irrelevant sentiment words have semantic meaning for the given aspect category, it may cause the problem that sentiment words cannot be matched with aspect categories.
View Article and Find Full Text PDFChemistry
January 2025
Sichuan University, School of Chemical Engineering, School of Chemical Engineering, Sichuan University, Chengdu 610065, China, 610065, Chendu, CHINA.
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