This research proposes to develop a layered, extensible system modeling framework representing the diverse, complex, and interdependent institutions and policies that influence the quality and availability of the U.S. academic medicine/health sciences (M/HS) workforce. This approach avoids the tendency to lock in to a single model that may be appropriate only for certain types of analysis by developing multiple models at different levels of resolution, using a variety of modeling tools. Model development will be an iterative and recursive process, starting with top-down modeling to develop the key insights needed and guide data collection, which will in turn suggest the detailed model structure required to make sense of the empirical findings. The choice of system domains and behaviors to model will be guided by consultations with subject-matter experts and coordination with other efforts funded under this solicitation. Quantitative associations will be established through review of the published literature and examination of extant datasets, including a number of large-scale longitudinal surveys not heretofore available to modelers. Selected issues and policy scenarios will be investigated via simulation runs. Model interfaces will be developed that align with the needs and capacities of key stakeholders, such as NIH managers and policymakers. The long-term goal of this research is to encourage the use of integrated simulation modeling tools within the M/HS community to support the establishment of sound policies and practices for workforce improvement and beneficial interventions in education, recruitment and retention. Such models have the potential to enhance decision-making processes by allowing leaders to (a) account for historical patterns in the M/HS education systems, (b) examine what might have occurred under other historical conditions, (c) explore what may happen in a variety of future scenarios, (d) identify methods and/or points of intervention with higher leverage (stronger influence) over system behavior, (e) characterize the time constants applicable to various actions or changes, and (f) identity the most critical areas for future research and data collection.