A randomized controlled trial evaluating the effects of social networking on chronic disease management in rheumatoid arthritis.

OBJECTIVE: Social networking has been shown to improve health outcomes in certain patient populations. While patients with rheumatoid arthritis (RA) increasingly use social networking to communicate with peers, the effects of these interactions are largely unknown. METHODS: In a randomized controlled trial, we compared RA patients who participated in a social networking group moderated by peer leaders and who had access to a static website offering RA materials with a control group, who only had access to the website. The primary outcomes were patients' RA knowledge, self-efficacy and empowerment. Secondary outcomes included participation in desired health behaviors, and satisfaction with peer support, among others. Follow-up assessments were conducted at 3 and 6 months. Participants who never signed in were excluded from the primary analysis. RESULTS: 105 participants were randomized to each group. Mean age was 52 (±12.4) and 92.4% were females. Knowledge scores improved in both groups, but only in the control group the differences observed at 3 and 6 months were significant (p≤0.02). Self-efficacy scores also improved in both groups, but only the differences observed at 6 months in the Facebook group were significant (p=0.02). When comparing groups, at 3 months the knowledge improvements observed in the control group were greater compared with those observed in the Facebook group (mean difference 0.4 versus 0.1; respectively, p=0.03). No other differences were observed in secondary outcomes between the 2 groups, except in peer support satisfaction. The Facebook® group reported greater peer support satisfaction in 3 out 5 subscales compared with the control group (p≤0.04). CONCLUSION: Peer support satisfaction was higher in participants using an online social network, but this was not translated into greater disease knowledge or empowerment.

The relationship between self-compassion and the risk for substance use disorder.

OBJECTIVE: This study explored the relationship between substance use disorder risk and self-compassion and posits a model for how the two are related through the mitigation of suffering. METHOD: Study participants were recruited using social media to complete an online survey that included a basic socio-demographic survey and two validated instruments, the Self-Compassion Survey and the National Institute on Drug Abuse (NIDA) Alcohol Smoking and Substance Involvement Screening Test (ASSIST), which screens for substance use disorder (SUD) risk. Established cut scores for ASSIST were used to divide participants into low, moderate and high-risk groups. RESULTS: Participants (n=477) were 31 years old on average, almost evenly split by gender, mostly non-Hispanic white, slightly more likely to be single and to hold an Associate's degree or higher. Overall, 89% of participants reported using drugs and/or alcohol in their lifetime. SUD risk was distributed between low risk (52%), moderate risk (37%) and a smaller percentage of high risk (11%). Self-compassion was inversely related to SUD risk. The low risk group had a higher mean self-compassion score (M=2.86, SD=0.75) than the people who were high risk (M=2.25, SD=0.61) (t(298)=5.58 p<0.0001). Bivariate Pearson correlations showed strong associations between high risk and all self-compassion subscales, as well as low risk and five of the subscales. CONCLUSIONS: This study suggests SUD risk has an inverse relationship to self-compassion. Raising self-compassion may be a useful addition to substance use disorder prevention and treatment interventions.

Keeping learning central: a model for implementing emerging technologies.

FELT PROBLEM: Technology integration continues to be a challenge for health science faculty. While students expect emerging technologies to be used in the classroom, faculty members desire a strategic process to incorporate technology for the students' benefit. OUR SOLUTION: We have developed a model that provides faculty a strategy for integrating emerging technologies into the classroom. The model is grounded in student learning and may be applied to any technology. We present the model alongside examples from faculty who have used it to incorporate technology into their health sciences classrooms.

Predictors of student success in graduate biomedical informatics training: introductory course and program success.

OBJECTIVE: To predict student performance in an introductory graduate-level biomedical informatics course from application data. DESIGN: A predictive model built through retrospective review of student records using hierarchical binary logistic regression with half of the sample held back for cross-validation. The model was also validated against student data from a similar course at a second institution. MEASUREMENTS: Earning an A grade (Mastery) or a C grade (Failure) in an introductory informatics course. RESULTS: The authors analyzed 129 student records at the University of Texas School of Health Information Sciences at Houston (SHIS) and 106 at Oregon Health and Science University Department of Medical Informatics and Clinical Epidemiology (DMICE). In the SHIS cross-validation sample, the Graduate Record Exam verbal score (GRE-V) correctly predicted Mastery in 69.4%. Undergraduate grade point average (UGPA) and underrepresented minority status (URMS) predicted 81.6% of Failures. At DMICE, GRE-V, UGPA, and prior graduate degree significantly correlated with Mastery. Only GRE-V was a significant independent predictor of Mastery at both institutions. There were too few URMS students and Failures at DMICE to analyze. Course Mastery strongly predicted program performance defined as final cumulative GPA at SHIS (n=19, r=0.634, r2=0.40, p=0.0036) and DMICE (n=106, r=0.603, r2=0.36, p<0.001). CONCLUSIONS: The authors identified predictors of performance in an introductory informatics course including GRE-V, UGPA and URMS. Course performance was a very strong predictor of overall program performance. Findings may be useful for selecting students for admission and identifying students at risk for Failure as early as possible.

Recruiting future neuroscientists: what asking the recruits can teach us.

Many different strategies are used to recruit students into scientific research careers, including neuroscience research. These strategies are rarely based on knowledge about students; instead, activities are selected based on their ease of implementation. The goal of the LEARN Project is to encourage high school students into mental health science research using the theme of learning and memory. One intervention the authors developed is five Web-based biographies introducing students to contemporary neuroscientist role models studying learning and memory. To guide the design of this intervention, the authors created a survey to determine where students obtain career information and who and what influences their career selection. In a convenience sample of 124 students, the authors found that almost all students use the Internet for information about careers, in addition to consulting family members and teachers. Students' career selections are influenced most by family members, teachers, and people already in the field. The most important factors students look for in their future career are money, fun, and a good match between current interests and future careers. The data affirm the value of outreach efforts that go beyond students to include a broader audience of parents and teachers who play a critical role in career selection.