What impact do healthcare digital libraries have? An evaluation of national resource of infection control at the point of care using the Impact-ED framework

Acceptance of information communication technology-based health information services: Exploring the culture in primary-level health care of South Ethiopia, using Utaut Model, Ethnographic Study

Introduction

In sub-Saharan African countries including Ethiopia, the acceptance of Information Communication Technology (ICT) in health is at the proof-of-concept level with a few unsustainable piecemeal of pilot projects. Thus, a desirable willingness of acceptance among healthcare providers is a paramount.

Material and Methods

Eight months elapsed ethnographic study design was conducted using participant observation and key informant interviews. The data were entered on Qualitative Data Analysis mine software version 1.4. The quotes and field notes were thematized. The Unified Technology Acceptance and Use Theory (UTAUT) is validated and used to generate new meanings.

Results

This study highlighted the different instances of technology acceptance. Although the primary-level healthcare (PLHC) providers displayed tendencies to accept ICTs-based health information services consistent with the UTAUT dimensions such as the degree of simplicity associated with performance expectancy, use/effort expectancy, facilitating conditions, social issue, individual variation, and organization culture there are instances that disputed acceptance. For instance, the gains in data quality and reporting secondary to the use of District Health Information System Two (DHIS-II) are not influenced by acceptance. Rather PLHC providers are burnt-out of additional clerical duties of filling data on the DHIS-2. Furthermore, ICT acceptance is influenced by individual variations and the unique culture of primary level facilities such as leadership commitment.

Conclusions

On this basis, we conclude that the willingness to accept ICT-based health information services at the primary level is not limited to those factors discussed in the UTAUT model.

Distribution of date elements and its relationship to the types of digital libraries: Analysing aggregated metadata of a Polish large-scale distributed system

Large-scale distributed digital library systems with aggregated metadata provide platforms for resource discovery and retrieval. For researchers, aggregated metadata offers a potential for big data analysis and exploration of digital knowledge growth. The paper reports the findings of the study that investigated the distribution of the date elements in the metadata aggregated in the Polish Federation of Digital Libraries and related it to the types of libraries. The purpose of this study was to address the gap in research about heterogeneous digital libraries and explore the dynamics of their growth. The authors included timeline characteristics of the development of Polish digital libraries and proposed a new dynamics parameter – resource release interval. They used histograms, which have been grouped according to the organizational and thematic criteria, developed for this study. All charts are characterized by two similar maximum points. Their shapes and ratio have been analysed by both statistical and visual methods. The shape of resource release interval charts revealed characteristic differences for libraries types. The proposed approach, based on time characteristics, is an important step in the development of systematic classification of digital libraries and digitizing institutions. It can be also considered as a new tool in monitoring the dynamics of digital knowledge growth.

Modeling User Preferences in Recommender Systems

Recommender systems are firmly established as a standard technology for assisting users with their choices; however, little attention has been paid to the application of the user model in recommender systems, particularly the variability and noise that are an intrinsic part of human behavior and activity. To enable recommender systems to suggest items that are useful to a particular user, it can be essential to understand the user and his or her interactions with the system. These interactions typically manifest themselves as explicit and implicit user feedback that provides the key indicators for modeling users’ preferences for items and essential information for personalizing recommendations. In this article, we propose a classification framework for the use of explicit and implicit user feedback in recommender systems based on a set of distinct properties that include Cognitive Effort, User Model, Scale of Measurement, and Domain Relevance. We develop a set of comparison criteria for explicit and implicit user feedback to emphasize the key properties. Using our framework, we provide a classification of recommender systems that have addressed questions about user feedback, and we review state-of-the-art techniques to improve such user feedback and thereby improve the performance of the recommender system. Finally, we formulate challenges for future research on improvement of user feedback.