Family medicine has traditionally prioritised patient care over research. However, recent recommendations to strengthen family medicine include calls to focus more on research including improving research methods used in the field. Binary logistic regression is one method frequently used in family medicine research to classify, explain or predict the values of some characteristic, behaviour or outcome. The binary logistic regression model relies on assumptions including independent observations, no perfect multicollinearity and linearity. The model produces ORs, which suggest increased, decreased or no change in odds of being in one category of the outcome with an increase in the value of the predictor. Model significance quantifies whether the model is better than the baseline value (ie, the percentage of people with the outcome) at explaining or predicting whether the observed cases in the data set have the outcome. One model fit measure is the count- R 2 , which is the percentage of observations where the model correctly predicted the outcome variable value. Related to the count- R 2 are model sensitivity—the percentage of those with the outcome who were correctly predicted to have the outcome—and specificity—the percentage of those without the outcome who were correctly predicted to not have the outcome. Complete model reporting for binary logistic regression includes descriptive statistics, a statement on whether assumptions were checked and met, ORs and CIs for each predictor, overall model significance and overall model fit.

A recent review of frameworks used in dissemination and implementation (D&I) science described 61 judged to be related either to dissemination, implementation, or both. The current use of these frameworks and their contributions to D&I science more broadly has yet to be reviewed. For these reasons, our objective was to determine the role of these frameworks in the development of D&I science. We used the Web of Science™ Core Collection and Google Scholar™ to conduct a citation network analysis for the key frameworks described in a recent systematic review of D&I frameworks (Am J Prev Med 43(3):337–350, 2012). From January to August 2016, we collected framework data including title, reference, publication year, and citations per year and conducted descriptive and main path network analyses to identify those most important in holding the current citation network for D&I frameworks together. The source article contained 119 cited references, with 50 published articles and 11 documents identified as a primary framework reference. The average citations per year for the 61 frameworks reviewed ranged from 0.7 to 103.3 among articles published from 1985 to 2012. Citation rates from all frameworks are reported with citation network analyses for the framework review article and ten highly cited framework seed articles. The main path for the D&I framework citation network is presented. We examined citation rates and the main paths through the citation network to delineate the current landscape of D&I framework research, and opportunities for advancing framework development and use. Dissemination and implementation researchers and practitioners may consider frequency of framework citation and our network findings when planning implementation efforts to build upon this foundation and promote systematic advances in D&I science.

Residents of rural jurisdictions face significant health challenges, including some of the highest rates of risky health behaviors and worst health outcomes of any group in the country. Rural communities are served by smaller local health departments (LHDs) that are more understaffed and underfunded than their suburban and urban peers. As a result of history and current need, rural LHDs are more likely than their urban peers to be providers of direct health services, leading to relatively lower levels of population-focused activities. This review examines the double disparity faced by rural LHDs and their constituents: pervasively poorer health behaviors and outcomes and a historical lack of investment by local, state, and federal public health entities.

Journal of Medical Internet Research - International Scientific Journal for Medical Research, Information and Communication on the Internet

Objectives. We examined whether characteristics of local health departments (LHD) and their geographic region were associated with using Facebook and Twitter. We also examined the number of tweets per month for Twitter accounts as an indicator of social media use by LHDs. Methods. In 2012, we searched for Facebook and Twitter accounts for 2565 LHDs nationwide, and collected adoption date and number of connections for each account. Number of tweets sent indicated LHD use of social media. LHDs were classified as innovators, early adopters, or nonadopters. Characteristics of LHDs were compared across adoption categories, and we examined geographic characteristics, connections, and use. Results. Twenty-four percent of LHDs had Facebook, 8% had Twitter, and 7% had both. LHDs serving larger populations were more likely to be innovators, tweeted more often, and had more social media connections. Frequency of tweeting was not associated with adoption category. There were differences in adoption across geographic regions, with western states more likely to be innovators. Innovation was also higher in states where the state health department adopted social media. Conclusions. Social media has the potential to aid LHDs in disseminating information across the public health system. More evidence is needed to develop best practices for this emerging tool.

Objectives. Little is known about the use of social media as a tool for health communication. We used a mixed-methods design to examine communication about childhood obesity on Twitter. Methods. NodeXL was used to collect tweets sent in June 2013 containing the hashtag #childhoodobesity. Tweets were coded for content; tweeters were classified by sector and health focus. Data were also collected on the network of follower connections among the tweeters. We used descriptive statistics and exponential random graph modeling to examine tweet content, characteristics of tweeters, and the composition and structure of the network of connections facilitating communication among tweeters. Results. We collected 1110 tweets originating from 576 unique Twitter users. More individuals (65.6%) than organizations (32.9%) tweeted. More tweets focused on individual behavior than environment or policy. Few government and educational tweeters were in the network, but they were more likely than private individuals to be followed by others. Conclusions. There is an opportunity to better disseminate evidence-based information to a broad audience through Twitter by increasing the presence of credible sources in the #childhoodobesity conversation and focusing the content of tweets on scientific evidence.