Bio

Savanna Stromberg, BS, is an Associate Project Coordinator at Stanford CDI, who will be working across projects on C-DIAS, CMHIS, and RASC. She recently received her BS in Psychology from the University of Washington, where she conducted research on trauma and mental health outcomes in the Honors Psychology program. With a strong interest in substance use interventions and mental health, Savanna is eager to expand her expertise in implementation research. At Stanford CDI, Savanna supports multi-project management, helps build and organize data, and assists with the development, communication, and design of research findings to internal and external audiences.

Additional Info

All Publications

  • Building bridges, breaking silos: a social network analysis of collaboration in an implementation science center for addiction health services. Translational behavioral medicine Chokron Garneau, H., Vermeer, W., Stromberg, S., Olatunde, P., McGovern, M. 2026; 16 (1)

    Abstract

    Large scale collaborative or research centers are designed to accelerate scientific progress, promote sustained collaboration, and bridge gaps across areas of expertise. Traditionally, metrics of evaluation for such centers have focused on quantifiable outcomes, such as number and impact of publications. These take time to materialize yet formation, growth, and evolution of collaborative ties, which are fundamental to achieve these quantifiable outcomes, are seldom examined.To investigate collaborative ties, we applied social network analysis to the NIH-funded Center for Dissemination & Implementation at Stanford (C-DIAS). We illustrate the development and structure of collaborative, cross-disciplinary, and mentorship relationships within the center during its first three years.Data was collected from C-DIAS members in 2020, 2023, and 2025. Social network analyses examined four components: growth of interaction and collaboration networks, bridging disciplinary boundaries, establishing national reach, and building capacity through mentorship.Across three waves, C-DIAS expanded cross-disciplinary networks, with boundary-spanning ties growing from 21 to 180 while maintaining high connectivity. Interaction and collaboration networks grew substantially, converting interpersonal contact into productive partnerships. Geographic reach increased from 10 to 28 institutions, and near-universal mentorship integrated new members, supporting sustained development of early-career investigators across cohorts.In 3 years, C-DIAS cultivated a highly connected, disciplinary network linking addiction health services and implementation science researchers. Intentional infrastructure fostered collaboration, mentorship, and national reach, demonstrating that structured networks can break silos and accelerate the translation of research into practice.

    View details for DOI 10.1093/tbm/ibag074

    View details for PubMedID 42842330

    View details for PubMedCentralID PMC13644531

  • Posttraumatic stress disorder and climate change: A meta-analysis of prospective, acute predictors of PTSD following extreme weather events CLINICAL PSYCHOLOGY REVIEW Gauthier, G. M., Pandey, S., Sanchez, V., Stromberg, S. E., Zoellner, L. A. 2025; 122: 102662

    Abstract

    Extreme weather events are increasing in intensity and frequency due to climate change. Better understanding key prospective predictors of chronic posttraumatic stress disorder (PTSD) present in the acute wake of a disaster is essential to allocating limited resources to those at greatest risk. Additionally, identifying prospective predictors of PTSD may help elucidate etiological factors that could be harnessed for psychological interventions to prevent chronic PTSD.PubMed, PsycInfo and PTSDPubs were systematically searched in February 2025 for longitudinal studies prospectively examining PTSD in adults following extreme weather events (e.g., hurricanes) with at least one pre-disaster or acutely post-disaster (< 18 months) timepoint. Effect sizes of demographic (e.g., gender), pre-event (e.g., pre-disaster psychopathology), and environmental factors (e.g., social support) were extracted and synthesized as correlations using random effects modeling.Forty-seven unique samples (N = 25,085) were included, with a majority collected in North America (63.83 %). The most frequent disaster type was hurricanes/typhoons (53.19 %). Severity of disaster exposure (r = 0.26), income (r = -0.26), and prior psychopathology (r = 0.26) emerged as the strongest predictors of PTSD.This is the first meta-analysis to examine prospective predictors of PTSD following extreme weather events collectively. The small- to moderate-sized effects observed suggest that predictors likely operate cumulatively to confer risk. Relative to other trauma types, access to resources, such as higher income, may be an important buffer against mental health problems. Future work should investigate modifiable predictors of PTSD that could potentially be harnessed in early interventions.

    View details for DOI 10.1016/j.cpr.2025.102662

    View details for Web of Science ID 001598692900001

    View details for PubMedID 41086540

https://orcid.org/0009-0002-3514-5974