Healthcare Equity: Questions of Access and Security

Abstract The rapid growth of mobile technology to improve healthcare conditions, support patient engagement, and enhance patient education is expected to continue¬ its upward trend. Physicians feel that simplified access to health information is one of the greatest benefits of technology. This research connects the growth of patients’ healthcare data access via mobile applications and the growth of access to wireless communication. This article proposes the following questions to investigate potential healthcare equity barriers: “What is the available Wi-Fi coverage?” and “What types of security protocols are used in the wireless access points?” The results indicate that there is a difference in community access to available Wi-Fi coverage. This difference could influence healthcare equity barriers. In addition, communities had identical security protocol usage. This indicates an opportunity to improve knowledge of security protocols and maintenance of access points, as well as influences on health care equity barriers

HIGH SCHOOL STUDENTS’ PERCEPTIONS CASE STUDY: WHAT IS INFORMATION SYSTEMS?

Although the demand for professionals in the field of Information Systems continues to grow, attracting students into Information Systems programs is a challenge. Today’s high school students belong to Gen Z, one of the largest, most educated, and diverse generations. Because this group is just beginning to graduate high school and enter the workforce, understanding what influences their career choices and their perception of Information Systems is of great importance. This research investigates 237 high school students’ perceptions of Information Systems. The authors used a qualitative coding process to explain what influences them when considering classes or a career in Information Systems. Five categories emerged from the analysis: Occupation, Ambivalence, Situation, Self-Efficacy and Unspecified/Blank. These findings may provide insight into the career-making processes of current high school students and contribute to meeting the growing market demands for Information Systems professionals by identifying and promoting pathways into the field for a new generation of Information Systems professionals

Overcoming Health Inequities in Native American Tribal Populations through mHealth

Disparities in health outcomes among people in rural tribal lands appear to be unique to their social and economic conditions. This paper investigates: what health disparities in rural tribal communities can be overcome through mHealth? Data is collected on the social inequities and access point networks from six small towns on an Indian Reservation in the Midwest. The results suggest disparities in living conditions, access to clinics and hospitals, and mobile health access affect the well-being of a population. An analysis is carried out with additional data on the effect of mobile and telephone access on health inequities at the national level to understand the significance of these disparities. The regression suggests that a high level of mobile services is correlated with better health conditions among American Indians. Fixed terrestrial services are positively related to the fair or poor health of American Indians. Contributions are offered on understanding how to overcome health disparities in rural tribal communities using mHealth

A novel improvement to google scholar algorithms through broad topic search.

Google Scholar uses ranking algorithms to find the most relevant academic research possible. However, its algorithms use an exact keyword match and citation count to sort its results. This paper presents a novel improvement to Google Scholar algorithms by aggregating multiple synonymous searches into one set of results, offsetting the necessity to guess all potential search phrases for a research topic. This design science research method uses a broad topic analysis that examines search queries, finds synonymous phrases, and combines all keyword searches into one set of results based on current Google Scholar citation count algorithms. To support and evaluate this research-in-progress, several users will compare multiple niche search queries against old and new algorithms. The expectation of this design is to introduce modern algorithm techniques to academic search engines, resulting in greater quality, discoverability, and core topic diversity of published research

A Proposed Improvement to Google Scholar Algorithms Through Broad Topic Search Emergent Research Forum Paper

Google Scholar uses ranking algorithms to find the most relevant academic research possible. However, its algorithms use an exact keyword match that excludes synonymous search terms that may be overlooked or neglected by researchers. This paper aims to improve on the current Google Scholar Search System by allowing a broad topic search algorithm to diversify and allow synonymous search terms to be included and ranked with other results. The authors propose a Design Science method to improve the Google Scholar Search System by developing a broad topic prototype that will add synonymous keywords into Google Scholar ranking algorithms. The results from twenty users will be evaluated by means of Mean Reciprocal Rank and Discounted Cumulative Gain. This improvement will introduce a modern approach to academic search engines systems, and to allow researchers who overlook potential search queries, an improved core topic diversity, quality, and discoverability of published research

A Proposed Improvement to Google Scholar Algorithms Through Broad Topic Search

Google Scholar uses ranking algorithms to find the most relevant academic research possible. However, its algorithms use an exact keyword match that excludes synonymous search terms that may be overlooked or neglected by researchers. This paper aims to improve on the current Google Scholar Search System by allowing a broad topic search algorithm to diversify and allow synonymous search terms to be included and ranked with other results. The authors propose a Design Science method to improve the Google Scholar Search System by developing a broad topic prototype that will add synonymous keywords into Google Scholar ranking algorithms. The results from twenty users will be evaluated by means of Mean Reciprocal Rank and Discounted Cumulative Gain. This improvement will introduce a modern approach to academic search engines systems, and to allow researchers who overlook potential search queries, an improved core topic diversity, quality, and discoverability of published research

High School Students’ Perceptions of Information Systems: A Case Study in Process

Although the demand for professionals in the field of Information Systems continues to grow, attracting students into Information Systems programs is a challenge. Today’s high school students belong to Gen Z, one of the largest, most educated, and diverse generations. Because this group is just beginning to graduate high school and enter the workforce, understanding what influences their career choices and their perception of Information Systems is of great importance. This research in progress proposes investigating 237 high school students’ perceptions of Information Systems. The authors will use a qualitative coding process to explain what influences them when considering classes or a career in Information Systems. The findings may provide insight into the career-making processes of current high school students and contribute to meeting the growing market demands for Information Systems professionals by identifying and promoting pathways into the field for a new generation of Information Systems professionals

A novel improvement to Google Scholar algorithms through broad topic search

Google Scholar uses ranking algorithms to find the most relevant academic research possible. However, its algorithms use an exact keyword match and citation count to sort its results. This paper presents a novel improvement to Google Scholar algorithms by aggregating multiple synonymous searches into one set of results, offsetting the necessity to guess all potential search phrases for a research topic. This design science research method uses a broad topic analysis that examines search queries, finds synonymous phrases, and combines all keyword searches into one set of results based on current Google Scholar citation count algorithms. To support and evaluate this research-in-progress, several users will compare multiple niche search queries against old and new algorithms. The expectation of this design is to introduce modern algorithm techniques to academic search engines, resulting in greater quality, discoverability, and core topic diversity of published research