Informatics for Health & Social Care 

About: Informatics for Health & Social Care is an academic journal published by Taylor & Francis. The journal publishes majorly in the area(s): Health care & Health informatics. It has an ISSN identifier of 1753-8157. Over the lifetime, 390 publications have been published receiving 6845 citations. The journal is also known as: Informatics for health and social care.

Navigating the digital divide: a systematic review of eHealth literacy in underserved populations in the United States

Abstract: eHealth provides an important mechanism to connect medically underserved populations with health information, but little is known about gaps in eHealth literacy research in underserved adult populations within the U.S. Between June and July 2013, three systematic literature reviews of five databases were conducted and a subsequent hand search was completed. Identified literature was screened and studies meeting exclusion and inclusion criteria were synthesized and analyzed for common themes. Of the 221 articles critically appraised, 15 met these criteria. Thirty-five of these studies were excluded due to international origin. Of the articles meeting the inclusion criteria, underserved populations assessed included immigrant women, the elderly, low-income, the un- and underemployed, and African-American and Hispanic populations. eHealth literacy assessments utilized included one or two item screeners, the eHEALS scale, health information competence and cognitive task analysis. Factors examined in relation to eHealth literacy included age, experience, overall health literacy, education, income and culture. The majority did not assess the impact of locality and those that did were predominately urban. These data suggest that there is a gap in the literature regarding eHealth literacy knowledge for underserved populations, and specifically those in rural locations, within the U.S.

Readability of online health information: implications for health literacy

Abstract: Accessibility is one of six quality criteria articulated by the European Commission in its code of conduct for health websites. Readability plays an integral part in determining a website's accessibility. Health information that is hard to read may remain inaccessible to people with low health literacy. This study aimed to calculate the readability of websites on various causes of disease. The names of 22 health conditions were entered into five search engines, and the readability of the first 10 results for each search were evaluated using Gunning FOG, SMOG, Flesch-Kincaid and Flesch Reading Ease tests (n=352). Readability was stratified and assessed by search term, search term complexity, top-level domain and paragraph position. The mean reading grade was 12.30, and the mean FRE was 46.08, scores considered 'difficult'. Websites on certain topics were found to be even harder to read than average. Where conditions had multiple names, searching for the simplest one led to the most readable results. Websites with .gov and .nhs TLDs were the most readable while .edu sites were the least. Within texts, a trend of increasing difficulty was found with concluding paragraphs being the hardest to read. It was also found that some of the most frequent search results (such as Wikipedia pages) were amongst the hardest to read. Health professionals, with the help of public and specialised libraries, need to create and direct patients towards high-quality, plain language health information in multiple languages.

Machine learning in autistic spectrum disorder behavioral research: A review and ways forward

Abstract: Autistic Spectrum Disorder (ASD) is a mental disorder that retards acquisition of linguistic, communication, cognitive, and social skills and abilities. Despite being diagnosed with ASD, some indiv...

Investigating factors influencing the adoption of e-Health in developing countries: A patient's perspective.

Abstract: Purpose: The aim of the study was to investigate factors that influence the adoption and use of e-Health applications in Bangladesh from citizens’ (patients’) perspectives by extending the technology acceptance model (TAM) to include privacy and trust. Methods: A structured questionnaire survey was used to collect data from more than 350 participants in various private and public hospitals in Dhaka, the capital city of Bangladesh. The data were analyzed using the partial least-squares (PLS) method, a statistical analysis technique based on structural equation modeling (SEM). Results: The study determined that perceived ease of use and perceived usefulness and trust (p 0.05) was identified as a less significant factor in the context of e-Health in Bangladesh. The findings also revealed that gender was strongly associated with the adoption and use of e-Health services. Conclusions: The findings of the present ...

User acceptance of mobile health services from users’ perspectives: The role of self-efficacy and response-efficacy in technology acceptance

Abstract: With the swift emergence of electronic medical information, the global popularity of mobile health (mHealth) services continues to increase steadily. This study aims to investigate the efficacy factors that directly or indirectly influence individuals' acceptance of mHealth services. Based on the technology acceptance model, this research incorporates efficacy factors into the acceptance decision process. A research model was proposed involving the direct and indirect effects of self-efficacy and response-efficacy on acceptance intention, along with their moderating effects. The model and hypotheses were validated using data collected from a field survey of 650 potential service users. The results reveal that: (1) self-efficacy and response-efficacy are both positively associated with perceived ease of use; and (2) self-efficacy and response-efficacy moderate the impact of perceived usefulness toward adoption intention. Self-efficacy and response-efficacy both play an important role in individuals' acceptance of mHealth services, which not only affect their perceived ease of use of mHealth services, but also positively moderate the effects of perceived usefulness on adoption intention. Our findings serve to provide recommendations that are specifically customized for mHealth service providers and their marketers.