{"id":5690,"date":"2018-06-20T20:47:39","date_gmt":"2018-06-20T20:47:39","guid":{"rendered":"http:\/\/cjni.net\/journal\/?p=5690"},"modified":"2018-10-21T06:59:25","modified_gmt":"2018-10-21T06:59:25","slug":"google-tools-for-nursing-research","status":"publish","type":"post","link":"https:\/\/cjni.net\/journal\/?p=5690","title":{"rendered":"Google Tools for Nursing Research"},"content":{"rendered":"

Software Column<\/h1>\n

by Allen McLean, RN, MN, MSc, PhD(c)<\/em><\/p>\n

Allen is currently a PhD student in Health Sciences at the University of Saskatchewan (Saskatoon) in the Computational Epidemiology and Public Health Informatics Lab. His research interests include the development of computer modeling and simulation software for addressing health systems challenges, chronic diseases and health inequities at the population level, as well as mobile technologies applied in long-term care facilities. Allen previously attended the University of Victoria earning an MN and MSc (Health Information Science) in a unique dual degree program for Nursing Informatics professionals. Allen has over 20 years\u2019 experience in healthcare as an ultrasound technologist, clinical educator, team leader and community health RN.<\/p><\/blockquote>\n

COLUMN<\/h2>\n

\"Google<\/a>Nurses are fortunate to have many free software resources available to help with a variety of research problems. Frustratingly, even some of the best are not well known and not well publicized. In this issue, I would like to introduce three interesting and useful tools developed by Google. These tools are all extensions of their search algorithm, easy to use, and offered at no charge. Google is not the only company to offer software tools like these \u2013 but I will save a description of other similar resources (for instance, Gapminder, Microsoft, Twitter) for future issues. I encourage you to explore!<\/p>\n

Google Trends<\/h2>\n

The first is Google Trends<\/a> [1] \u2013 a public web service that shows how often a particular search-term is entered relative to total search-volume. The horizontal axis of the main graph represents time (starting from 2004), and the vertical axis represents how often a term is searched for, relative to the total number of searches. Below the main graph, popularity is broken down by countries, regions, cities and language. It is possible to refine the main graph by region and time period. A noteworthy example of health research is provided by Jeremy Ginsberg et al.(2009) \u2013 researchers who found that Google Trends data can be used to track influenza-like illness in a population. Because the relative frequency of certain search queries is highly correlated with the percentage of physician visits in which a patient presents with influenza-like symptoms, an estimate of weekly influenza activity can be reported for a specific geographic region.<\/p>\n

Google Correlate<\/h2>\n

Google Correlate<\/a> [2] is like Google Trends in reverse. With Google Trends, you type in a query and get back a data series of activity over time. With Google Correlate, you enter a data series (the target) and get back a list of queries whose data series follows a similar pattern. Google Correlate finds search patterns which correspond with real-world trends. With Google Correlate, you can upload data charted over either time or space, and Google will look for matching patterns in search volumes. If you don\u2019t have data of your own to upload, you can simply specify search terms, and Google will calculate the trending pattern and show matching patterns. Amusingly, on multiple occasions Google has had to remind users and researchers that correlation does not necessarily equal causation! (A quick search of examples where folks have made this mistake is guaranteed to put a smile on your face.)<\/p>\n

Google\u2019s Public Data Explorer<\/h2>\n

Finally, Google\u2019s<\/a> Public Data Explorer<\/a> [3] \u2013 makes large, public-interest datasets easy to explore, visualize and communicate. As the charts and maps animate over time, the changes in the world become easier to understand. You don’t have to be a data expert to navigate between different views, make your own comparisons, and share your findings. Nurses can use the tool to create visualizations of public data, links to them, and embed them in their own webpages or research papers. All of the datasets in the Public Data Explorer are provided for researchers at no charge by various international organizations, national statistical offices, non-governmental organizations, and research institutions.<\/p>\n

Links<\/h2>\n

[1] Google (2018). Google Trends. Retrieved from
\nhttps:\/\/trends.google.ca\/trends\/?geo=CA<\/a><\/p>\n

[2] Google (2018). Google Correlate. Retrieved from https:\/\/www.google.com\/trends\/correlate\/<\/a><\/p>\n

[3] Google (2018). Public Data Explorer. Retrieved from https:\/\/www.google.com\/publicdata\/directory<\/a><\/p>\n

Reference<\/h2>\n

Ginsberg, J. Mohebbi, M., Patel, R., Brammer, M., & Smolinski, L. Brilliant. (2009). Detecting influenza epidemics using search engine query data. Nature, 457<\/i> (2009), pp. 1012-1014<\/p>\n

OUR SPONSORS<\/h2>\n
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Visit the Nursing Informatics Learning Center for resources, courses, news, and other informatics related content.<\/p><\/div>\n

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Save<\/span>Save<\/span><\/p>\n<\/div>","protected":false},"excerpt":{"rendered":"

by Allen McLean
\nSoftware Column<\/p>\n

Volume 13 No 2
\nSpring 2018<\/p>\n","protected":false},"author":1,"featured_media":5699,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":[],"categories":[1096,1155],"tags":[917,538,1162,1160,1161,20,1159,1158,40],"_links":{"self":[{"href":"https:\/\/cjni.net\/journal\/index.php?rest_route=\/wp\/v2\/posts\/5690"}],"collection":[{"href":"https:\/\/cjni.net\/journal\/index.php?rest_route=\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/cjni.net\/journal\/index.php?rest_route=\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/cjni.net\/journal\/index.php?rest_route=\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/cjni.net\/journal\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=5690"}],"version-history":[{"count":11,"href":"https:\/\/cjni.net\/journal\/index.php?rest_route=\/wp\/v2\/posts\/5690\/revisions"}],"predecessor-version":[{"id":5730,"href":"https:\/\/cjni.net\/journal\/index.php?rest_route=\/wp\/v2\/posts\/5690\/revisions\/5730"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/cjni.net\/journal\/index.php?rest_route=\/wp\/v2\/media\/5699"}],"wp:attachment":[{"href":"https:\/\/cjni.net\/journal\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=5690"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/cjni.net\/journal\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=5690"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/cjni.net\/journal\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=5690"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}