• Journal of Korean Academy of Nursing
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• v.2 no.1
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• pp.3-33
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• 1971

Nursing of today has as one of its objectives the solving of problems related to human needs arising from the demands of a rapidly changing society. This nursing objective, I believe, can he attained by the appropriate application of scientific principles in the giving of comprehensive nursing care. Comprehensive nursing care may be defined as nursing care which meets all of the patient's needs. the needs of patients are said to fall into five broad categories: physical needs, psychological needs, environmental needs, socio-economic needs, and teaching needs. Most people who become ill have adjustment problems related to their new situation. Because patient teaching is one of the most important functions of professional nursing, the success of this teaching may be used as a gauge for evaluating comprehensive nursing care. This represents a challenge foe the future. A questionnaire consisting of 67 items was distributed to 200 professional nurses working ill direct patient care at Yonsei University Medical Center in Seoul, Korea. 160 (80,0%) nurses of the total sample returned completed questionnaires 81 (50.6%) nurses were graduates of 3 fear diploma courser 79 (49.4%) nurses were graduates of 4 year collegiate nursing schools in Korea 141 (88,1%) nurses had under 5 years of clinical experience in a medical center, while 19 (11.9%) nurses had more than 5years of clinical experience. Three hypotheses were tested: 1. “Nurses had high levels of concept and knowledge toward patient teaching”-This was demonstrated by the use of a statistical method, the mean average. 2. “Nurses graduating from collegiate programs and diploma school programs of nursing show differences in concepts and knowledge toward patient teaching”-This was demonstrated by a statistical method, the mean average, although the results showed little difference between the two groups. 3. “Nurses having different amounts of clinical experience showed differences in concepts and knowledge toward patient teaching”-This was demonstrated by the use of a statistical method, the mean average. 2. “Nurses graduating from collegiate programs and diploma school programs of nursing show differences in concepts and knowledge toward patient teaching”-This was demonstrated by a statistical method, the mean average, although the results showed little difference between the two groups. 3. “Nurses having different amounts of clinical experience showed differences in concepts and knowledge toward patient teaching”-This was demonstrated by the use of the T-test. Conclusions of this study are as follow: Before attempting the explanation, of the results, the questionnaire will he explained. The questionnaire contained 67 questions divided into 9 sections. These sections were: concept, content, time, prior preparation, method, purpose, condition, evaluation, and recommendations for patient teaching. 1. The nurse's concept of patient teaching: Most of the nurses had high levels of concepts and knowledge toward patient teaching. Though nursing service was task-centered at the turn of the century, the emphasis today is put on patient-centered nursing. But we find some of the nurses (39.4%) still are task-centered. After, patient teaching, only a few of the nurses (14.4%) checked this as “normal teaching.”It seems therefore that patient teaching is often done unconsciously. Accordingly it would he desirable to have correct concepts and knowledge of teaching taught in schools of nursing. 2. Contents of patient teaching: Most nurses (97.5%) had good information about content of patient teaching. They teach their patients during admission about their diseases, tests, treatments, and before discharge give nurses instruction about simple nursing care, personal hygiene, special diets, rest and sleep, elimination etc. 3. Time of patient teaching: Teaching can be accomplished even if there is no time set aside specifically for it. -a large part of the nurse's teaching can be done while she is giving nursing care. If she believes she has to wait for time free from other activities, she may miss many teaching opportunities. But generally proper time for patient teaching is in the midmorning or midafternoon since one and a half or two hours required. Nurses meet their patients in all stages of health: often tile patient is in a condition in which learning is impossible-pain, mental confusion, debilitation, loss of sensory perception, fear and anxiety-any of these conditions may preclude the possibility of successful teaching. 4. Prior preparation for patient teaching: The teaching aids, nurses use are charts (53.1%), periodicals (23.8%), and books (7.0%) Some of the respondents (28.1%) reported that they had had good preparation for the teaching which they were doing, others (27.5%) reported adequate preparation, and others (43.8%) reported that their preparation for teaching was inadequate. If nurses have advance preparation for normal teaching and are aware of their objectives in teaching patients, they can do effective teaching. 5. Method of patient teaching: The methods of individual patient teaching, the nurses in this study used, were conversation (55.6%) and individual discussion (19.2%) . And the methods of group patient teaching they used were demonstration (42.3%) and lecture (26.2%) They should also he prepared to use pamphlet and simple audio-visual aids for their teaching. 6. Purposes of patient teaching: The purposes of patient teaching is to help the patient recover completely, but the majority of the respondents (40.6%) don't know this. So it is necessary for them to understand correctly the purpose of patient teaching and nursing care. 7. Condition of patient teaching: The majority of respondents (75.0%) reported there were some troubles in teaching uncooperative patients. It would seem that the nurse's leaching would be improved if, in her preparation, she was given a better understanding of the patient and communication skills. The majority of respondents in the total group, felt teaching is their responsibility and they should teach their patient's family as well as the patient. The place for teaching is most often at the patient's bedside (95.6%) but the conference room (3.1%) is also used. It is important that privacy be provided in learning situations with involve personal matters. 8. Evaluation of patient teaching: The majority of respondents (76.3%,) felt leaching is a highly systematic and organized function requiring special preparation in a college or university, they have the idea that teaching is a continuous and ever-present activity of all people throughout their lives. The suggestion mentioned the most frequently for improving preparation was a course in patient teaching included in the basic nursing program. 9. Recommendations: 1) It is recommended, that in clinical nursing, patient teaching be emphasized. 2) It is recommended, that insertive education the concepts and purposes of patient teaching he renewed for all nurses. In addition to this new knowledge, methods and materials which can be applied to patient teaching should be given also. 3) It is recommended, in group patient teaching, we try to embark on team teaching.

• Journal of Intelligence and Information Systems
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• v.21 no.4
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• pp.93-110
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• 2015

The recommender system is a system which recommends products to the customers who are likely to be interested in. Based on automated information filtering technology, various recommender systems have been developed. Collaborative filtering (CF), one of the most successful recommendation algorithms, has been applied in a number of different domains such as recommending Web pages, books, movies, music and products. But, it has been known that CF has a critical shortcoming. CF finds neighbors whose preferences are like those of the target customer and recommends products those customers have most liked. Thus, CF works properly only when there's a sufficient number of ratings on common product from customers. When there's a shortage of customer ratings, CF makes the formation of a neighborhood inaccurate, thereby resulting in poor recommendations. To improve the performance of CF based recommender systems, most of the related studies have been focused on the development of novel algorithms under the assumption of using a single profile, which is created from user's rating information for items, purchase transactions, or Web access logs. With the advent of big data, companies got to collect more data and to use a variety of information with big size. So, many companies recognize it very importantly to utilize big data because it makes companies to improve their competitiveness and to create new value. In particular, on the rise is the issue of utilizing personal big data in the recommender system. It is why personal big data facilitate more accurate identification of the preferences or behaviors of users. The proposed recommendation methodology is as follows: First, multimodal user profiles are created from personal big data in order to grasp the preferences and behavior of users from various viewpoints. We derive five user profiles based on the personal information such as rating, site preference, demographic, Internet usage, and topic in text. Next, the similarity between users is calculated based on the profiles and then neighbors of users are found from the results. One of three ensemble approaches is applied to calculate the similarity. Each ensemble approach uses the similarity of combined profile, the average similarity of each profile, and the weighted average similarity of each profile, respectively. Finally, the products that people among the neighborhood prefer most to are recommended to the target users. For the experiments, we used the demographic data and a very large volume of Web log transaction for 5,000 panel users of a company that is specialized to analyzing ranks of Web sites. R and SAS E-miner was used to implement the proposed recommender system and to conduct the topic analysis using the keyword search, respectively. To evaluate the recommendation performance, we used 60% of data for training and 40% of data for test. The 5-fold cross validation was also conducted to enhance the reliability of our experiments. A widely used combination metric called F1 metric that gives equal weight to both recall and precision was employed for our evaluation. As the results of evaluation, the proposed methodology achieved the significant improvement over the single profile based CF algorithm. In particular, the ensemble approach using weighted average similarity shows the highest performance. That is, the rate of improvement in F1 is 16.9 percent for the ensemble approach using weighted average similarity and 8.1 percent for the ensemble approach using average similarity of each profile. From these results, we conclude that the multimodal profile ensemble approach is a viable solution to the problems encountered when there's a shortage of customer ratings. This study has significance in suggesting what kind of information could we use to create profile in the environment of big data and how could we combine and utilize them effectively. However, our methodology should be further studied to consider for its real-world application. We need to compare the differences in recommendation accuracy by applying the proposed method to different recommendation algorithms and then to identify which combination of them would show the best performance.

• Journal of Intelligence and Information Systems
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• v.25 no.1
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• pp.139-161
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• 2019

The utilization of the e-commerce market has become a common life style in today. It has become important part to know where and how to make reasonable purchases of good quality products for customers. This change in purchase psychology tends to make it difficult for customers to make purchasing decisions in vast amounts of information. In this case, the recommendation system has the effect of reducing the cost of information retrieval and improving the satisfaction by analyzing the purchasing behavior of the customer. Amazon and Netflix are considered to be the well-known examples of sales marketing using the recommendation system. In the case of Amazon, 60% of the recommendation is made by purchasing goods, and 35% of the sales increase was achieved. Netflix, on the other hand, found that 75% of movie recommendations were made using services. This personalization technique is considered to be one of the key strategies for one-to-one marketing that can be useful in online markets where salespeople do not exist. Recommendation techniques that are mainly used in recommendation systems today include collaborative filtering and content-based filtering. Furthermore, hybrid techniques and association rules that use these techniques in combination are also being used in various fields. Of these, collaborative filtering recommendation techniques are the most popular today. Collaborative filtering is a method of recommending products preferred by neighbors who have similar preferences or purchasing behavior, based on the assumption that users who have exhibited similar tendencies in purchasing or evaluating products in the past will have a similar tendency to other products. However, most of the existed systems are recommended only within the same category of products such as books and movies. This is because the recommendation system estimates the purchase satisfaction about new item which have never been bought yet using customer's purchase rating points of a similar commodity based on the transaction data. In addition, there is a problem about the reliability of purchase ratings used in the recommendation system. Reliability of customer purchase ratings is causing serious problems. In particular, 'Compensatory Review' refers to the intentional manipulation of a customer purchase rating by a company intervention. In fact, Amazon has been hard-pressed for these "compassionate reviews" since 2016 and has worked hard to reduce false information and increase credibility. The survey showed that the average rating for products with 'Compensated Review' was higher than those without 'Compensation Review'. And it turns out that 'Compensatory Review' is about 12 times less likely to give the lowest rating, and about 4 times less likely to leave a critical opinion. As such, customer purchase ratings are full of various noises. This problem is directly related to the performance of recommendation systems aimed at maximizing profits by attracting highly satisfied customers in most e-commerce transactions. In this study, we propose the possibility of using new indicators that can objectively substitute existing customer 's purchase ratings by using RFM multi-dimensional analysis technique to solve a series of problems. RFM multi-dimensional analysis technique is the most widely used analytical method in customer relationship management marketing(CRM), and is a data analysis method for selecting customers who are likely to purchase goods. As a result of verifying the actual purchase history data using the relevant index, the accuracy was as high as about 55%. This is a result of recommending a total of 4,386 different types of products that have never been bought before, thus the verification result means relatively high accuracy and utilization value. And this study suggests the possibility of general recommendation system that can be applied to various offline product data. If additional data is acquired in the future, the accuracy of the proposed recommendation system can be improved.