Explore the Effectiveness of Outpatient Surgery in Pre-school Children’s Fear

Abstract

Objective: Children are susceptible to sentiment because their physiology, psychology, comprehension and language expression has not yet matured. Therefore, when they have to undergo an operation there will be resistance because of fear of the unknown. Methods: This study was conducted from September 2015 to March 2016. Pre-school children who underwent outpatient surgeries were the subjects of the study. The research methods were actual observation and questionnaire survey. The “Pre-school Children’s Preoperative Fear Response Observation Record” was used as the survey tool. Mann-Whitney U Test was used to compare the children’s responsive behaviors before and after intervention. Results: A total of 11 subjects were included in the study. Only 13.3% of pre-school children were willing to cooperate, while 67.8% of pre-school children feel fear before the operation. In order to reduce their fear, this study used clinical care teaching, development of care guidelines for pre-school preoperative fear, the layout of surgery waiting area for outpatient and operating room, providing touch medical equipment and selective medical treatment, use of modeling balloons, use of games in tablets and stickers as a care auxiliary as intervention. Using these, they not only were willing to cooperate but also have reduced their fear. The degree of coordination increased from 13.3% to 76.7%, the extent of fear cut down from 67.8% to 10.3%, and 98% of the family members were satisfied. Conclusion: Throughout the operation, professional nursing care that also focuses on the children’s emotion by establishing good relationship were established. Through teamwork, the group provided the pre-school children with a wide range of care, thereby reducing the preschool children's preoperative fear and family members became more satisfied. Keywords: Outpatient Surgery, Pre-school Children, Children’s Fear

Deep Learning Enabled Atom-by-Atom Analysis of 2D materials on the Million-Atom Scale

“…In particular, understanding the types and densities of point defects in 2D materials is important for producing 2D materials that can achieve theoretical limits of charge mobility and quantum yield for high quality optoelectronics [1]. Recently, machine learning techniques have been widely applied to electron microscopy data for defect identification, image segmentation, and image denoising [2][3][4][5][6]. Machine learning also opens up new possibilities to analyze large datasets of atomic resolution information.…”