Irmgard Willcockson

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.

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.

Development of a computer-aided training program for brief cognitive-behavioral therapy in primary care

Psychotherapies such as CBT are complex, requiring intense training over a prolonged period, usually under expert supervision. Comprehensive psychotherapy training often occurs within mental health graduate and postgraduate and residency programs, and focused training in specific methods such as CBT are often optional. As a result, few practitioners, even those from mental health programs, are adequately trained to conduct focused, evidence-based psychotherapy approaches, and even fewer are able to adapt such training to fit within non-mental health specialty care settings (e.g., primary care). Focused efforts are needed to expand access to evidence-based psychotherapy trainings for treatments such as CBT (8, 9). As a supplement and/or alternative to postgraduate training, other CBT training approaches have focused on in-person workshops that require significant resources (i.e., time, money, trainers) and are relatively inflexible for participants (i.e., limited availability, travel requirements, etc.). Recent efforts by Craske et al. (10) and Ros et al. (11) suggest that computer-aided innovations in CBT training and service delivery (e.g., provision of care by non-CBT experts) can potentially improve the treatment of anxiety in the primary care setting. The current project sought to develop a computer-aided CBT training program using a flexible, online approach (labeled computer-aided CBT; CA-CBT) to increase training access for a broad audience of mental health providers with varying levels of psychotherapy experience. CA-CBT was envisioned to provide focused training in CBT to mental health providers, regardless of discipline and or previous training in CBT, to advance and/or establish practice-based CBT skills. The following educational resource document describes the process of constructing CA-CBT to aid others seeking to use similar computerbased teaching methods.

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.

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 Associates 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.