The wineglass model: tracking the locational histories of health professionals.

ABSTRACT

This paper introduces a space-time continuum model to evaluate the effectiveness of programs that encourage recruitment and retention of health professionals. Based on the shape of a wineglass, the model provides a framework to study the locational histories of a cohort of health professionals, both conceptually and quantitatively. A key component of the model is that it measures geographic dispersion over time from a medical school, residency program, or other shared location. Geographic dispersion can be studied in the model through standard deviational ellipses, standard distance, or average distance circles. The model enables analysis through structuration theory, which is used as a guide for analyzing the interplay between human agency (e.g., individual decisions on practice location) and structure (e.g., medical education). Space-time modeling is linked with structuration theory. Variations in the shape of the wineglass reveal how people may be bound by a general structure, yet through lifetime locational decisions may change that structure over space and time. Using data on University of Nebraska alumni, the authors constructed a pilot demonstration to test and confirm the model's potential usefulness. In the pilot demonstration, standard deviational ellipses represented the range of physician locations during each year, overlaid on maps of the US. The pilot demonstration indicated the model's strength in identifying changing mobility over time, while also pointing to concerns about unevenness in data availability from one year to the next. Further application of the wineglass model could be used toward studying the life histories of health professionals. The impact of community-based training on long- or short-term retention, changes in the mobility of male vs female providers, and the career trajectories of people in different health professions or medical schools are only a few examples of potential future applications of the model.