• 한국노년학
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• v.36 no.4
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• pp.1037-1058
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• 2016

This study aims to investigate the female caregivers'experiences of aggression by nursing home residents, and to identify the policy strategies for violence prevention referred by the female caregivers. A total of 121 female caregivers with more than 6 months of working experiences had participated in this study. Of these, 56.2% had experienced verbal aggression, 51.2% physical aggression, and 27.3% sexual aggression, which reveals that client violence toward caregivers in nursing homes was at an alarming level. Although, physical and verbal violences were mostly caused unintentionally, about a half of the sexual aggression were caused deliberately. Aggression occurred the most when caregivers were providing the following services: changing the diapers or clothes, giving a bath, and serving meals. It was found that 'hitting' was the most common form of physical aggression and it was 'swearing' and 'touching or physical contacting' in the case of verbal and sexual aggression, respectively. Though there was a difference depending on the type of aggression, the most frequent reactions against client violence were to start a conversation or calm down the nursing home residents, and to leave the scene or ignore the incident. This means that the caregivers are coping very passively through resolving the aggressions by themselves, or overlooking the situation. The most frequently recommended strategy to prevent resident aggression was to provide educational programs on violence prevention to nursing home residents and caregivers(42.7%). Compared to the previous studies, this study indicates some differentiated strategies to prevent violence in nursing homes, which include hiring male caregivers, assuring directors to pay closer attention toward caregivers, using refined language between caregivers and residents, and keeping caregivers to wear appropriate clothes. Based on the study results, some policy recommendations on the prevention of client violence in nursing homes were suggested.

• Korean Journal of Psychosomatic Medicine
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• v.29 no.2
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• pp.111-120
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• 2021

Objectives : Shift work has been known to cause various health problems by making it difficult for humans to adapt to their natural circadian rhythms. In particular, shift workers tend to complain of sleep difficulties associated with work schedules, and sometimes use alcohol as a self-medication to induce sleep. To date, no clear mechanism has been identified regarding the link between shift work and sleep, between shift work and drinking. This study aims to confirm the relationship between shift work and sleep, and to analyze whether the change in sleep caused by shift work causes drinking. Methods : This study included 11360 people (5704 men and 5656 women) among the Korean National Health and Nutrition Survey data in 2014, 2016 and 2018. The work type between 6am-6pm was defined as day work, and other work types were defined as shift work. Using logistic regression analysis, the relationship between shift work and sleep quality, shift work and high risk drinking, drinking amount at 1 time, drinking frequency were analyzed. In addition, we analyzed whether sleep mediates the relationship between shift work and drinking using mediated analysis. Results : Shift work showed a significant negative relationship with sleep quality in men and women (male OR=1.37, 95% CI 1.11-1.70, female OR=1.26, 95% CI=1.05-1.50). There was no significant relationship between shift work and alcohol in the case of men, but in the case of women, there was a significant positive relationship between shift work and the number of alcohol consumption (OR=1.34, 95% CI=1.04-1.72). When mediation analysis was conducted, it was found that women's sleep quality partially mediated the relationship between shift work and the frequency of drinking. Conclusions : The results of this study suggest that shift work causes sleep difficulties, and in the case of women, drinking can be induced through the partial mediating effect of sleep. Considering that women are more likely to choose drinking as a coping method for sleep problems, more active interventions for female shift workers are needed.

• Journal of Intelligence and Information Systems
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• v.26 no.2
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• pp.1-25
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• 2020

In this paper, we suggest an application system architecture which provides accurate, fast and efficient automatic gasometer reading function. The system captures gasometer image using mobile device camera, transmits the image to a cloud server on top of private LTE network, and analyzes the image to extract character information of device ID and gas usage amount by selective optical character recognition based on deep learning technology. In general, there are many types of character in an image and optical character recognition technology extracts all character information in an image. But some applications need to ignore non-of-interest types of character and only have to focus on some specific types of characters. For an example of the application, automatic gasometer reading system only need to extract device ID and gas usage amount character information from gasometer images to send bill to users. Non-of-interest character strings, such as device type, manufacturer, manufacturing date, specification and etc., are not valuable information to the application. Thus, the application have to analyze point of interest region and specific types of characters to extract valuable information only. We adopted CNN (Convolutional Neural Network) based object detection and CRNN (Convolutional Recurrent Neural Network) technology for selective optical character recognition which only analyze point of interest region for selective character information extraction. We build up 3 neural networks for the application system. The first is a convolutional neural network which detects point of interest region of gas usage amount and device ID information character strings, the second is another convolutional neural network which transforms spatial information of point of interest region to spatial sequential feature vectors, and the third is bi-directional long short term memory network which converts spatial sequential information to character strings using time-series analysis mapping from feature vectors to character strings. In this research, point of interest character strings are device ID and gas usage amount. Device ID consists of 12 arabic character strings and gas usage amount consists of 4 ~ 5 arabic character strings. All system components are implemented in Amazon Web Service Cloud with Intel Zeon E5-2686 v4 CPU and NVidia TESLA V100 GPU. The system architecture adopts master-lave processing structure for efficient and fast parallel processing coping with about 700,000 requests per day. Mobile device captures gasometer image and transmits to master process in AWS cloud. Master process runs on Intel Zeon CPU and pushes reading request from mobile device to an input queue with FIFO (First In First Out) structure. Slave process consists of 3 types of deep neural networks which conduct character recognition process and runs on NVidia GPU module. Slave process is always polling the input queue to get recognition request. If there are some requests from master process in the input queue, slave process converts the image in the input queue to device ID character string, gas usage amount character string and position information of the strings, returns the information to output queue, and switch to idle mode to poll the input queue. Master process gets final information form the output queue and delivers the information to the mobile device. We used total 27,120 gasometer images for training, validation and testing of 3 types of deep neural network. 22,985 images were used for training and validation, 4,135 images were used for testing. We randomly splitted 22,985 images with 8:2 ratio for training and validation respectively for each training epoch. 4,135 test image were categorized into 5 types (Normal, noise, reflex, scale and slant). Normal data is clean image data, noise means image with noise signal, relfex means image with light reflection in gasometer region, scale means images with small object size due to long-distance capturing and slant means images which is not horizontally flat. Final character string recognition accuracies for device ID and gas usage amount of normal data are 0.960 and 0.864 respectively.