Calhoun, J. G., K. Ramiah, et al. (2008). "Development of a core competency model for the master of public health degree." American Journal of Public Health 98(9): 1598-1607.
Core competencies have been used to redefine curricula across the major health professions in recent decades. In 2006, the Association of Schools of Public Health identified core competencies for the master of public health degree in graduate schools and programs of public health. We provide an overview of the model development process and a listing of 12 core domains and 119 competencies that can serve as a resource for faculty and students for enhancing the quality and accountability of graduate public health education and training. The primary vision for the initiative is the graduation of professionals who are more fully prepared for the many challenges and opportunities in public health in the forthcoming decade.
Centers for Disease Control and Prevention (2012). CDC’s Guide to Writing for Social Media, CDC Electronic Media Branch.
Duggan, M. and J. Brenner (2013). The Demographics of Social Media Users - 2012, Pew Internet & American Life Project.
Eysenbach, G. (2011). "Can tweets predict citations? Metrics of social impact based on Twitter and correlation with traditional metrics of scientific impact." J Med Internet Res 13(4).
Citations in peer-reviewed articles and the impact factor are generally accepted measures of scientific impact. Web 2.0 tools such as Twitter, blogs or social bookmarking tools provide the possibility to construct innovative article-level or journal-level metrics to gauge impact and influence. However, the relationship of the these new metrics to traditional metrics such as citations is not known. (1) To explore the feasibility of measuring social impact of and public attention to scholarly articles by analyzing buzz in social media, (2) to explore the dynamics, content, and timing of tweets relative to the publication of a scholarly article, and (3) to explore whether these metrics are sensitive and specific enough to predict highly cited articles. Between July 2008 and November 2011, all tweets containing links to articles in the Journal of Medical Internet Research (JMIR) were mined. For a subset of 1573 tweets about 55 articles published between issues 3/2009 and 2/2010, different metrics of social media impact were calculated and compared against subsequent citation data from Scopus and Google Scholar 17 to 29 months later. A heuristic to predict the top-cited articles in each issue through tweet metrics was validated. A total of 4208 tweets cited 286 distinct JMIR articles. The distribution of tweets over the first 30 days after article publication followed a power law (Zipf, Bradford, or Pareto distribution), with most tweets sent on the day when an article was published (1458/3318, 43.94% of all tweets in a 60-day period) or on the following day (528/3318, 15.9%), followed by a rapid decay. The Pearson correlations between tweetations and citations were moderate and statistically significant, with correlation coefficients ranging from .42 to .72 for the log-transformed Google Scholar citations, but were less clear for Scopus citations and rank correlations. A linear multivariate model with time and tweets as significant predictors (P < .001) could explain 27% of the variation of citations. Highly tweeted articles were 11 times more likely to be highly cited than less-tweeted articles (9/12 or 75% of highly tweeted article were highly cited, while only 3/43 or 7% of less-tweeted articles were highly cited; rate ratio 0.75/0.07 = 10.75, 95% confidence interval, 3.4-33.6). Top-cited articles can be predicted from top-tweeted articles with 93% specificity and 75% sensitivity. Tweets can predict highly cited articles within the first 3 days of article publication. Social media activity either increases citations or reflects the underlying qualities of the article that also predict citations, but the true use of these metrics is to measure the distinct concept of social impact. Social impact measures based on tweets are proposed to complement traditional citation metrics. The proposed twimpact factor may be a useful and timely metric to measure uptake of research findings and to filter research findings resonating with the public in real time.
Eysenbach, G. and C.-E. Group (2011). "CONSORT-EHEALTH: improving and standardizing evaluation reports of Web-based and mobile health interventions." J Med Internet Res 13(4).
Web-based and mobile health interventions (also called "Internet interventions" or "ehealth/mhealth interventions") are tools or treatments, typically behaviorally based, that are operationalized and transformed for delivery via the Internet or mobile platforms. These include electronic tools for patients, informal caregivers, healthy consumers, and health care providers. The "Consolidated Standards of Reporting Trials" (CONSORT) was developed to improve the suboptimal reporting of randomized controlled trials (RCTs). While broadly the CONSORT statement can be applied to provide guidance on how ehealth and mhealth trials should be reported, RCTs of web-based interventions pose very specific issues and challenges, in particular related to reporting sufficient details of the intervention to allow replication and theory-building. To develop a checklist, dubbed CONSORT-EHEALTH (Consolidated Standards of Reporting Trials of Electronic and Mobile HEalth Applications and onLine TeleHealth), as an extension of the CONSORT statement that provides guidance for authors of ehealth and mhealth interventions. A literature review was conducted, followed by a survey among ehealth experts and a workshop. An instrument and checklist was constructed as an extension of the CONSORT statement. The instrument has been adopted by the Journal of Medical Internet Research (JMIR) and authors of ehealth RCTs are required to submit an electronic checklist explaining how they addressed each subitem. CONSORT-EHEALTH has the potential to improve reporting and provides a basis for evaluating the validity and applicability of ehealth trials. Subitems describing how the intervention should be reported can also be used for non-RCT evaluation reports. As part of the development process, an evaluation component is essential, therefore feedback from authors will be solicited, and a before-after study will evaluate whether reporting has been improved.
Facebook (2013). Best Practices Guide: Marketing on Facebook.
Fordis, M., R. L. Street, et al. (2011). "The prospects for web 2.0 technologies for engagement, communication, and dissemination in the era of patient-centered outcomes research: Selected articles developed from the Eisenberg Conference Series 2010 Meeting." Journal of Health Communication 16(SUPPL. 1): 3-9.
Fox, S. (2012). "The Social Life of Health Information | Pew Research Center's Internet & American Life Project." from http://www.pewinternet.org/Reports/2009/8-The-Social-Life-of-Health-Information.aspx.
Gibbons, M. C., L. Fleisher, et al. (2011). "Exploring the Potential of Web 2.0 to Address Health Disparities." Journal of Health Communication 16(sup1): 77-89.
This article addresses use of the Internet and Web 2.0 technologies by racial and ethnic minorities and explores the potential opportunities and challenges in leveraging Web 2.0 approaches to impact health disparities. These opportunities and challenges include developing approaches and methods to (a) identify strategies for integrating social media into health promotion interventions focused on major health-related issues that affect members of medically underserved groups; (b) amalgamate techniques to leverage and connect social-media technologies to other evidence-informed online resources; (c) integrate health communication best practices, including addressing health literacy issues; (d) capitalize on social networking to enhance access and communication with health care providers; and (e) advance current efforts and ongoing expansion of research participation by individuals from underserved communities.
Google (2013). "Social Media Measurement With Google Analytics." 2012, from http://www.google.com/analytics/features/social.html.
Hesse, B. W., M. O'Connell, et al. (2011). "Realizing the Promise of Web 2.0: Engaging Community Intelligence." Journal of Health Communication 16(sup1): 10-31.
Discussions of Health 2.0, a term first coined in 2005, were guided by three main tenets: (a) health was to involve more participation, because an evolution in the web encouraged more direct consumer engagement in their own health care; (b) data was to become the new ?Intel Inside? for systems supporting the vital decisions in health; and (c) a sense of collective intelligence from the network would supplement traditional sources of knowledge in health decision making. Interests in understanding the implications of a new paradigm for patient engagement in health and health care were kindled by findings from surveys such as the National Cancer Institute's Health Information National Trends Survey, showing that patients were quick to look online for information to help them cope with disease. This article considers how these 3 facets of Health 2.0?participation, data, and collective intelligence?can be harnessed to improve the health of the nation according to Healthy People 2020 goals. The authors begin with an examination of evidence from behavioral science to understand how Web 2.0 participative technologies may influence patient processes and outcomes, for better or worse, in an era of changing communication technologies. The article then focuses specifically on the clinical implications of Health 2.0 and offers recommendations to ensure that changes in the communication environment do not detract from national (e.g., Healthy People 2020) health goals. Changes in the clinical environment, as catalyzed by the Health Information Technology for Economic and Clinical Health Act to take advantage of Health 2.0 principles in evidence-based ways, are also considered.
Kanter, B. (2012). "How To Create A Terrific Facebook Cover Image If You Don’t Have Resources To Hire A Designer.". from http://www.bethkanter.org/fb-cover-images/.
Kanter, B. (2012). Integrated Content Strategy. New Media for the Networked NGO.
Kaplan, A. M. and M. Haenlein (2010). "Users of the world, unite! The challenges and opportunities of Social Media." Business Horizons 53(1): 59-68.
The concept of Social Media is top of the agenda for many business executives today. Decision makers, as well as consultants, try to identify ways in which firms can make profitable use of applications such as Wikipedia, YouTube, Facebook, Second Life, and Twitter. Yet despite this interest, there seems to be very limited understanding of what the term “Social Media” exactly means; this article intends to provide some clarification. We begin by describing the concept of Social Media, and discuss how it differs from related concepts such as Web 2.0 and User Generated Content. Based on this definition, we then provide a classification of Social Media which groups applications currently subsumed under the generalized term into more specific categories by characteristic: collaborative projects, blogs, content communities, social networking sites, virtual game worlds, and virtual social worlds. Finally, we present 10 pieces of advice for companies which decide to utilize Social Media.
Lasica, J. D. (2011). The 7 elements of a social media plan.
Metzger, M. J. and A. J. Flanagin (2011). "Using web 2.0 technologies to enhance evidence-based medical information." Journal of Health Communication 16(SUPPL. 1): 45-58.
This article invokes research on information seeking and evaluation to address how providers of evidence-based medical information can use Web 2.0 technologies to increase access to, enliven users experiences with, and enrich the quality of the information available. In an ideal scenario, evidence-based medical information can take appropriate advantage of community intelligence spawned by Web 2.0 technologies, resulting in the ideal combination of scientifically sound, high-quality information that is imbued with experiential insights from a multitude of individuals. To achieve this goal, the authors argue that people will engage with information that they can access easily, and that they perceive as (a) relevant to their information-seeking goals and (b) credible. The authors suggest the utility of Web 2.0 technologies for engaging stakeholders with evidence-based medical information through these mechanisms, and the degree to which the information provided can and should be trusted. Last, the authors discuss potential problems with Web 2.0 information in relation to decision making in health contexts, and they conclude with specific and practical recommendations for the dissemination of evidence-based health information via Web 2.0 technologies. © 2011 Copyright Taylor and Francis Group, LLC.
Parvanta, C. F. (2011). Essentials of public health communication. Sudbury, Mass., Jones & Bartlett Learning.
Samplin-Salgado, M. and A. Moore (2011). Doing more with less: Efficiently and effectively using new mediaHHS New Media, AIDS.gov.
Schein, R., K. Wilson, et al. (2010). Literature review on effectiveness of the use of social media, Peel Public Health.
Tactile Technology Collective (2013). "Designing your strategy | Message in-a-Box."
The Centers for Disease Control and Prevention (2011). The Health Communicator’s Social Media Toolkit.
Turnbull, A. P., J. A. Summers, et al. (2009). "Fostering Wisdom-Based Action through Web 2.0 Communities of Practice: An Example of the Early Childhood Family Support Community of Practice." Infants and Young Children 22(1): 54-62.
This article discusses a new approach to knowledge translation using Web 2.0 technologies in an online Community of Practice (CoP). The purpose of the CoP is to promote wisdom-based action, a process that encourages people to engage with knowledge, match it to their own values, vision, and contexts, make a well-informed decision, and act on that decision. We use our own Early Childhood Family Support CoP as a case study.