The Effect of the Physical Examination Course on Self-Confidence, Anxiety, and Knowledge Levels in the Postoperative Clinical Decision-Making Process Among Nursing Students

Nurşen Kulakaç, Sema Koçan — Mon, 25 Sep 2023 00:00:00 +0300

This study aimed to investigate the impact of a physical examination course on self-confidence, anxiety, and knowledge levels regarding postoperative clinical decision-making among nursing students. This quasi-experimental, pretest-posttest control group study focused on second-year students in the nursing department of a university situated in eastern Turkey. Without sample selection, all students who agreed to participate in the study and completed the questionnaires on both the first and last days of the physical examination course were included in the analysis. The data were collected using the “Sociodemographic Data Collection Form”, the “Abdominal Examination/Surgical Drain Follow-up Knowledge Test”, and the “The Nursing Anxiety and Self-Confidence with Clinical Decision-Making Scale”. Statistical analyses were conducted employing chi-square, Wilcoxon, and Mann-Whitney U tests. According to the results, post-test anxiety scores were significantly lower among participants in the experimental group than those in the control group (p<0.001) and their self-confidence post-test scores were significantly higher. A positive and moderately significant correlation was observed between abdominal examination (p=0.004), surgical drain follow-up (p=0.011), and the self-confidence sub-dimension of the clinical decision-making scale. Additionally, a moderately significant negative relationship was identified between the anxiety and self-confidence levels of nursing students (p=0.014). It is concluded that the physical examination course contributed to increased knowledge levels in abdominal examination and surgical drain follow-up, along with enhanced self-confidence in clinical decision-making and reduced anxiety levels.

Generative Text-to-Image Models in Architectural Design

Emel Cantürk Alyıldız — Mon, 25 Sep 2023 00:00:00 +0300

Text-guided generation of images with deep learning technology has made significant advances and has seen an increasing interest since 2021. With these mostly web-based models, users can synthesise photorealistic and high-quality digital images from natural language descriptions with no or little understanding of the underlying technology. Although these AI technologies are in the early phases, there is already an explosion in AI-generated architectural activity. While generative AI technologies propose a new design method for designers and architects, it will undoubtedly redefine the skills, knowledge and competencies that designers should equipped with. This research focuses on understanding the “artificial intelligence – architect” interaction as a design method, specifically the “language as a design driver”, and interrogates the role of the designer in AI-driven design. In the context of the research, the textual inputs (“prompts”) and the outputs of the architectural design studies of 36 subjects generated in Midjourney – a text-to-image latent diffusion model – were analysed in terms of the possible relationships between the language of the prompts, (1) prompt length, (2) descriptive language, (3) specific architecture-related indicators, and the quality of the outputs in two terms of architectural quality and architectural creativity.

ICONTECH INTERNATIONAL JOURNAL

The Effect of the Physical Examination Course on Self-Confidence, Anxiety, and Knowledge Levels in the Postoperative Clinical Decision-Making Process Among Nursing Students

Generative Text-to-Image Models in Architectural Design