Term Usage Patterns Research Articles

Usage Patterns of Telepsychology and Face-to-Face Psychotherapy: Clients' Profiles and Perceptions.

BackgroundCurrently, most people who might need mental health care services do not receive them due to a number of reasons. Many of these reasons can be overcome by telepsychology, in other words, the use of ICT technologies for therapy (e.g., phone, videoconferencing, and apps); given that it facilitates access to specialized interventions. In fact, telepsychology is currently offered as an active service in many psychotherapy centers. However, its usage, how it is perceived, and who uses it are still largely unknown.ObjectiveThe aim of this study was (1) to determine if any pattern exists in the usage of telepsychology and face-to-face psychology, (2) to clarify people’s perception of telepsychology in terms of the advantages, barriers and efficacy of online psychotherapy, and (3) to examine usage patterns in terms of individual characteristics and identify patients’ profiles.MethodsAn online survey was conducted on a convenience sample of 514 subjects recluted by using an online advertisement. The inclusion criteria were: (1) to be older than 18 years old and (2) to answer completely the questionnaire. Cluster analysis, ANOVAs, and discriminant analysis were performed to test our research objectives.ResultsThree usage clusters were found: (1) face-to-face psychotherapy (57%; n = 292); (2) non-therapy (36.8%; n = 189); and (3) combined face-to-face psychotherapy and telepsychology (6.4%; n = 33). In addition, the perception of telepsychology varied among usage clusters, but a common perception emerged about the main telepsychology advantages, barriers and efficacy. Finally, the results showed that personal characteristics differentiated people in each of these clusters.ConclusionThe most common form of access to psychotherapy is the face-to-face form but the second way of delivery was a combination between face to face and online psychotherapy (research objective 1). People who combine face to face with online psychotherapy perceives this last as more efficient and with less barriers to access (research objective 2). Finally, some characteristic as eHealth experience and sociodemographic variables can help to identify people that will attend telepsychology initiatives (research objective 3). These clusters provide insight into opportunities for face-to-face and online patient engagement strategies.

Keyword Extraction for Medium‐Sized Documents Using Corpus‐Based Contextual Semantic Smoothing

Keyword extraction refers to the process of selecting most significant, relevant, and descriptive terms as keywords, which are present inside a single document. Keyword extraction has major applications in the information retrieval domain, such as analysis, summarization, indexing, and search, of documents. In this paper, we present a novel supervised technique for extraction of keywords from medium‐sized documents, namely Corpus‐based Contextual Semantic Smoothing (CCSS). CCSS extends the concept of Contextual Semantic Smoothing (CSS), which considers term usage patterns in similar texts to improve term relevance information. We introduce four more features beyond CSS as our novel contributions in this work. We systematically compare the performance of CCSS with other techniques, when implemented over INSPEC dataset, where CCSS outperforms all state‐of‐the‐art keyphrase extraction techniques presented in the literature.

Collaboration and term usage dynamics in agricultural buffer strip research: A research weaving protocol

Abstract Research weaving is an approach that combines systematic mapping methods with bibliometric and scientometric analyses, shedding light on how research in a systematic map is connected or disconnected. Given its novelty, few examples exist that demonstrate methods for research weaving or highlight its value in the evidence synthesis context. Here, we seek to fill this gap by applying a research weaving analysis to a previously published systematic map on the roles of buffer strips in agricultural fields. This work identified a lack of studies addressing multifunctional roles of buffer strips and a remarkable array of terminology used by researchers to describe buffer strips. Using network visualization, such as co‐authorship and bibliographic coupling networks, as well as content and text analyses, we aim to build on these findings addressing questions related to collaboration, disciplinary contributions and citation and term usage patterns. As a result of this work, we aim to provide workflows, tools, and recommendations for the application of research weaving across a wide range of evidence maps in any domain. We will discuss the unique challenges of conducting bibliometric analysis in an evidence synthesis context and will propose solutions to address these challenges.

Instagram Bağımlılığının Belirleyicileri Olarak Sosyal Medya Kullanımı, Benlik Sunumu, Narsisizm ve Özsaygı: Kültürlerarası Bir Karşılaştırma

Instagram is a social media tool that increases in the number and popularity of users. Providing information about events and people, entertainment and relaxation, maintaining relationships, personal presentation, interacting with different identities, making new friendships and making life easier in many respects from photography to video, from live broadcast to instant story sharing, the excessive, unnecessary use of Instagram causes addiction problems. This research is designed to compare Turkish and Malaysian college students’ Instagram addiction level; and, relationships between Instagram addiction level and their level of narcissism, self-esteem, self-presentation, usage patterns in terms of exhibiting the situation in two different cultures. Data were obtained from 403 participants by face-to-face questionnaire. As a result of the study, it was found that the level of Instagram addiction did not differ significantly according to the gender of the participants. Depending on the country in which they live, Instagram addiction levels vary significantly. Instagram addiction levels of Malaysian participants is higher than students in Turkey. The result that self-presentation and social media usage time is a positively significant predictor on Instagram addiction whereas self-esteem level is a negatively significant predictor stands out. The level of narcissism was not significant.

An ensembled data frequency prediction based framework for fast processing using hybrid cache optimization

Technological advancements have led to an exponential growth in input–output intensive data that demands high-performance computing. To access the data, the frequency of firing the same query is quite high. Hence, predicting and prefetching these frequently-used queries can enhance the performance in terms of execution time and cache hit ratio. Therefore, a prediction-based framework has been proposed which initially, generates memory traces to identify the data usage patterns in terms of query frequency. The future query requests have been predicted and classified using an ensembled approach that yields 87.5% accuracy. It successfully reduces the error rate up to 11%. Furthermore, the predicted classified results have been tagged as hot and cold data on the basis of threshold frequency. The identified hot data has been prefetched into the cache that provides 96.5% cache hits with 9.7% decreased execution time. Hybrid cache replacement algorithm has been utilized to keep the cache updated with the hot data. The experimental results have been compared with the existing frameworks and benchmarks, which shows 6.8% improvement in accuracy with 9% increment in cache hits.

Comparing Medical Term Usage Patterns of Professionals and Search Engine and Community Question Answering Service Users in Japan: Log Analysis.

BackgroundDespite increasing opportunities for acquiring health information online, discussion of the specific words used in searches has been limited.ObjectiveThe aim of this study was to clarify the medical information gap between medical professionals and the general public in Japan through health information–seeking activities on the internet.MethodsSearch and posting data were analyzed from one of the most popular domestic search engines in Japan (Yahoo! JAPAN Search) and the most popular Japanese community question answering service (Yahoo! Chiebukuro). We compared the frequency of 100 clinical words appearing in the clinical case reports of medical professionals (clinical frequency) with their frequency in Yahoo! JAPAN Search (search frequency) logs and questions posted to Yahoo! Chiebukuro (question frequency). The Spearman correlation coefficient was used to quantify association patterns among the three information sources. Additionally, user information (gender and age) in the search frequency associated with each registered user was extracted.ResultsSignificant correlations were observed between clinical and search frequencies (r=0.29, P=.003), clinical and question frequencies (r=0.34, P=.001), and search and question frequencies (r=0.57, P<.001). Low-frequency words in clinical frequency (eg, “hypothyroidism,” “ulcerative colitis”) highly ranked in search frequency. Similarly, “pain,” “slight fever,” and “numbness” were highly ranked only in question frequency. The weighted average of ages was 34.5 (SD 2.7) years, and the weighted average of gender (man –1, woman +1) was 0.1 (SD 0.1) in search frequency. Some words were specifically extracted from the search frequency of certain age groups, including “abdominal pain” (10-20 years), “plasma cells” and “inflammatory findings” (20-30 years), “DM” (diabetes mellitus; 30-40 years), “abnormal shadow” and “inflammatory findings” (40-50 years), “hypertension” and “abnormal shadow” (50-60 years), and “lung cancer” and “gastric cancer” (60-70 years).ConclusionsSearch and question frequencies showed similar tendencies, whereas search and clinical frequencies showed discrepancy. Low-clinical frequency words related to diseases such as “hypothyroidism” and “ulcerative colitis” had high search frequencies, whereas those related to symptoms such as “pain,” “slight fever,” and “numbness” had high question frequencies. Moreover, high search frequency words included designated intractable diseases such as “ulcerative colitis,” which has an incidence of less than 0.1% in the Japanese population. Therefore, it is generally worthwhile to pay attention not only to major diseases but also to minor diseases that users frequently seek information on, and more words will need to be analyzed in the future. Some characteristic words for certain age groups were observed (eg, 20-40 years: “cancer”; 40-60 years: diagnoses and diseases identified in health examinations; 60-70 years: diseases with late adulthood onset and “death”). Overall, this analysis demonstrates that medical professionals as information providers should be aware of clinical frequency, and medical information gaps between professionals and the general public should be bridged.

Real-time sensing in residential area using IoT technology for finding usage patterns to suggest action plan to conserve energy

Abstract To conserve energy in a residential sector, showing action plans how to conserve energy is important. However, we need to consider both energy efficiency and indoor comfort. The usage patterns in the residential sector change according to their life style and some people would try to maintain the indoor comfort at high level even paying high energy cost. It is necessary to understand home energy usage patterns in terms of the relationships between indoor comfort and electricity consumption in order to provide appropriate energy saving action plans to the residential sector. For this purpose, in this study we have collected real time data of electricity consumption and indoor temperature using IoT devices and sensors installed to 13 households for about one month in Summer at Sumida ward in Tokyo, Japan. Then, we have analyzed the data using three types of method, which are a clustering algorism to delineate the electricity usage pattern which characterize the household life style, conducted correlation analysis to look at the relationship between electricity consumption, and the indoor temperature for households living in condominium apartments and detached houses with different building structure and ages.

An Exploratory Study Investigating How and Why Managers Use Tablets to Support Managerial Decision-Making

Managers are often mobile and a large proportion of their work is dealing with decisions. Although many managers currently use tablet computers in their work, there is little research on the use of tablets for managerial decision-support. This exploratory study aims to investigate the ways in which managers use tablets to support their decision-making and the reasons why they do so. Using Task-Technology Fit theory, semi-structured interviews were conducted with 20 managers, 17 of whom used tablets for their work-related decision-making. The study reveals managers’ tablet usage patterns in terms of location, tablet applications, decision activities and types. This study has also found that a range of tablet characteristics and decision-task characteristics affect managers’ use of tablets to support decision-making at work. This exploratory study contributes to both academia and industry by providing evidence on the tablet decision-support area, and affording organisations, tablet vendors and tablet application developers informative findings for further improvement in the provision of tablet-based decision support.

Personal computer wallpaper user segmentation based on Sasang typology

BackgroundAs human-computer interaction (HCI) is becoming a significant part of all human life, the user's emotional satisfaction is an important factor to consider. These changes have been pointed out by several researchers who claim that a user's personality may become the most important factor in the design. The objective of this study is to examine Sasang typology as a user segmentation method in the area of HCI design. To test HCI usage patterns in terms of the user's personality and temperament, this study focuses on personal computer (PC) or lap-top wallpaper settings. MethodsOne hundred and four Facebook friends completed a QSCC II survey assessing Sasang typology type and sent a captured image of their personal PC or lap-top wallpaper. To classify the computer usage pattern, folder organization and wallpaper setting were investigated. ResultsThe research showed that So-Yang type organized folders and icons in an orderly manner, whereas So-Eum type did not organize folders and icons at all. With regard to wallpaper settings, So-Yang type used the default wallpaper provided by the PC but So-Eum type used landscape images. Because So-Yang type was reported to be emotionally stable and extrovert, they tended to be highly concerned with online privacy compared with So-Eum type. So-Eum type use a lot of images of landscapes as the background image, which demonstrates So-Eum's low emotional stability, anxiety, and the desire to obtain analogy throughout the computer screen. Also, So-Yang's wallpapers display family or peripheral figures and this is due to the sociability that extrovert So-Yang types possess. ConclusionBy proposing the Sasang typology as a factor in influencing an HCI usage pattern in this study, it can be used to predict the user's HCI experience, or suggest a native design methodology that can actively cope with the user's psychological environment.

A study on health care consumers’ diabetes term usage across identified categories

Purpose– The purpose of this paper is to investigate health care consumers’ diabetes term usage patterns based on Yahoo!Answers social question and answers (Q&amp;A) forum, identified characteristics and relationships among terms within three pairs of related categories identified from the Q&amp;A log, and revealed users’ diabetes term usage patterns.Design/methodology/approach– The Q&amp;A analysis method allowed first-hand investigation of massive data from health care consumers. Visual term clustering analysis across categories was conducted using a multi-dimensional scaling (MDS) visualization method which provides an intuitive and interactive way to explore and discover term association patterns in a visual environment. Closely related categories were identified and corresponding visual term clustering analyses between categories (Sign &amp; Symptom and Organ &amp; Body Part; Diagnosis and Test; and Diagnosis and Medication) as well at the term level were analyzed.Findings– The findings show that there are close relationships between terms in two related categories. Related terms were grouped and patterns were revealed. All the stress values of the MDS analyses fall below 0.10 and RSQ for each of the combined categories is over 0.90 which indicate the investigated terms were well clustered in the visual analyses.Originality/value– The study provides a unique research methodology for similar consumer health research studies. The results of this study offer insight into consumer health term use behavior, and enrich existing thesauri and subject heading lists, enhance diabetes-related web sites or portals, and improve effectiveness of internal search engines.

Subjectivity and prototype structure in causal connectives: A cross-linguistic perspective

The meaning and use of causal discourse connectives has often been characterized in terms of subjectivity, speech act type or domains of use: English since is considered subjective, just like French car and puisque (as opposed to parce que), German denn and da (versus weil) and Dutch want and aangezien (versus omdat). Yet, there is no agreement on the precise definition of these conceptual distinctions between classes of connectives. Corpus studies suggest that language users’ preference for one causal connective over another highly similar one in relation to a given causal category is a matter of degree, rather than a black-and-white distinction. In this paper, we reinterpret usage patterns in terms of the cognitive structures associated with human categorization. We propose to analyze usage contexts of a given connective as prototypical and less prototypical instantiations of its subjectivity category. First, we show that remarkable convergence exists across languages and across theoretical frameworks with respect to prototypical usage types of connectives. Next, we report results of a meta-analysis concerning prototypicality characteristics in corpus data on French, German, and Dutch causal connectives in written discourse. The corpus data suggest that our prototype structure hypothesis indeed has cross-linguistic validity.

Characterizing ADSL customer behaviours by network traffic data-mining

The very rapid adoption of new applications by some segments of the adsl customers may have a strong impact on the quality of service delivered to all customers. This makes the segmentation of adsl customers according to their network usage a critical step both for a better understanding of the market and for the prediction and dimensioning of the network. Relying on a “bandwidth only” perspective to characterize network customer behaviour does not allow the discovery of usage patterns in terms of applications. In this paper, we shall describe how data mining techniques applied to network measurement data can help to extract qualitative and quantitative knowledge.

Related, but not Relevant: Content-Based Collaborative Filtering in TREC-8

Historically, solutions to the TREC filtering tasks have focused exclusively on the content of documents and search topic descriptions as training data. These approaches are well-known for their ability to focus on those salient concepts in the document stream which are most useful for separating relevant documents from irrelevant ones. However, one kind of information that has not been used is the relationships among the topics themselves. In our TREC-8 routing experiments, we employed a collaborative (or social) filtering algorithm, based on latent semantic indexing which highlights common term usage patterns among groups of filtering profiles. Our hypothesis was that this would allow related topics to share common relevant documents. We found, however, that the algorithm also recommends many documents of related, yet irrelevant interest. As a result of this process, many similar search topics are “linked” together by common sets of documents recommended to them. We visualize these topic relationships using graphs where topics are nodes and edges exist where two topics share a recommended document.