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The authors propose that professionalism, rather than being left to the chance that students will model themselves on ideal physicians or somehow be permeable to other elements of professionalism, is fostered by students' engagement with significant, integrated experiences with certain kinds of content. Like clinical reasoning, which cannot occur in a vacuum but must be built on particular knowledge, methods, and the development of skills, professionalism cannot flourish without its necessary basis of knowledge, methods, and skills. The authors present the need for an intellectual widening of the medical curriculum, so that students acquire not only the necessary tools of scientific and clinical knowledge, methods, and skills but also other relevant tools for professional development that can be provided only by particular knowledge, methods, and skills outside bioscience domains. Medical students have little opportunity to engage any body of knowledge not gained through bioscientific/empirical methods. Yet other bodies of knowledge-philosophy, sociology, literature, spirituality, and aesthetics are often the ones where compassion, communication, and social responsibility are addressed, illuminated, practiced, and learned. To educate broadly educated physicians who develop professionalism throughout their education and their careers requires a full-spectrum curriculum and the processes to support it. The authors sketch the ways in which admission, the curriculum (particularly promoting a sociologic consciousness, interdisciplinary thinking, and understanding of the economic/ political dimensions of health care), and assessment and licensure would function.

Consecutive admissions (N = 843) to the Brecksville Veterans Addiction Recovery Center with a primary diagnosis of pathological gambler, alcoholic, or cocaine misuser were compared for differences on impulsivity, sensation seeking, and craving. In contrast to alcoholics and cocaine misusers, gamblers scored significantly higher on impulsivity and inability to resist craving; however, gamblers were not significantly higher than either alcoholics or cocaine misusers on sensation seeking. These findings suggest a need to address high impulsivity and inability to resist cravings in treatment and relapse prevention for gamblers.

Organized medicine's modern-day professionalism movement has reached the quarter-century mark. In this article, the authors travel to an earlier time to examine the concept of profession within the work of Abraham Flexner. Although Flexner used the concept sparingly, it is clear that much of his writing on reforming medical education is grounded in his views on physicians as professionals and medicine as a profession. In the first half, the authors explore Flexner's views of profession, which were (1) empirically (as opposed to philosophically) grounded, (2) case based and comparatively framed, (3) sociological in orientation, and (4) systems based, with professionalism conceptualized as dynamic, evolving, and multidimensional. In the second half, the authors build on Flexner's systems perspective to introduce a complexity science understanding of professionalism. They define professionalism as a complex system, introduce a seven-part typology of professionalism, and explore how the organization of physician work and various flash points within medicine today reveal not one but several competing forms of professionalism at work. The authors then develop a tripartite model of professionalism with analysis at the micro, meso, and macro levels. They conclude with observations on how best to frame professionalism as a force for change in 21st-century medical education. Flexner's reforms were grounded in his vision of two particular types of professional-the physician clinician and the full-time academic physician-scientist. The authors propose reform grounded in professionalism as a complex system composed of competing types.

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Allostatic load (AL) is a complex clinical construct, providing a unique window into the cumulative impact of stress. However, due to its inherent complexity, AL presents two major measurement challenges to conventional statistical modeling (the field’s dominant methodology): it is comprised of a complex causal network of bioallostatic systems, represented by an even larger set of dynamic biomarkers; and, it is situated within a web of antecedent socioecological systems, linking AL to differences in health outcomes and disparities. To address these challenges, we employed case-based computational modeling (CBM), which allowed us to make four advances: (1) we developed a multisystem, 7-factor (20 biomarker) model of AL’s network of allostatic systems; (2) used it to create a catalog of nine different clinical AL profiles (causal pathways); (3) linked each clinical profile to a typology of 23 health outcomes; and (4) explored our results (post hoc) as a function of gender, a key socioecological factor. In terms of highlights, (a) the Healthy clinical profile had few health risks; (b) the pro-inflammatory profile linked to high blood pressure and diabetes; (c) Low Stress Hormones linked to heart disease, TIA/Stroke, diabetes, and circulation problems; and (d) high stress hormones linked to heart disease and high blood pressure. Post hoc analyses also found that males were overrepresented on the High Blood Pressure (61.2%), Metabolic Syndrome (63.2%), High Stress Hormones (66.4%), and High Blood Sugar (57.1%); while females were overrepresented on the Healthy (81.9%), Low Stress Hormones (66.3%), and Low Stress Antagonists (stress buffers) (95.4%) profiles.

In the health informatics era, modeling longitudinal data remains problematic. The issue is method: health data are highly nonlinear and dynamic, multilevel and multidimensional, comprised of multiple major/minor trends, and causally complex -making curve fitting, modeling and prediction difficult. The current study is fourth in a series exploring a case-based density (CBD) approach for modeling complex trajectories; which has the following advantages: it can (1) convert databases into sets of cases (k dimensional vectors) based on a set of bio-social variables, called traces; (2) compute the trajectory (velocity vector) for each case, based on (3) key traces from the k dimensional profile; (4) construct a theoretical map to explain these traces; (5) use vector quantization (i.e., k-means, topographical neural nets) to longitudinally cluster case trajectories into major/minor trends; (6) employ genetic algorithms and ordinary differential equations to create a microscopic (vector field) model (the inverse problem) of these trajectories; (7) look for complex steady-state behaviors (e.g., spiraling sources, etc) in the microscopic model; (8) draw from thermodynamics, synergetics and transport theory to translate the vector field (microscopic model) into the linear movement of macroscopic densities; (9) use the macroscopic model to simulate known and novel case-based scenarios (the forward problem); and (10) construct multiple accounts of the data by linking the theoretical map and k dimensional profile with the macroscopic, microscopic and cluster models. Given the utility of this approach, our purpose here is to organize our method (as applied to recent research) so it can be employed by others.

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