Veterinary students across the United States face the challenge of stress during school every day. When managed improperly, stress can become chronic and manifest in physical and emotional consequences. The purpose of this study was to examine the utility of the multi-theory model (MTM) of health behavior change in predicting the initiation and sustenance of stress management behaviors among veterinary students. A cross-sectional design was used to study the efficacy of the MTM in predicting initiation and sustenance of stress management behaviors among veterinary students at a private College of Veterinary Medicine in the Southeast United States. Researchers collected data using a 54-item valid and reliable survey. Only students who did not already engage in daily stress management behaviors were included in the study. After recruitment and exclusion, a total of 140 students remained and participated in the study. Hierarchical multiple regression revealed that, for initiation of stress management behaviors, 49.5% of the variance was explained by depression, academic classification, and behavioral confidence. Regarding sustenance of stress management behaviors, 50.4% of the variance was explained by perceived stress, depression, academic classification, and emotional transformation. MTM serves as a promising framework for predicting initiation and sustenance of health behavior change. Based on the results of this study, interventions aimed to promote stress management behaviors in veterinary students should focus on the MTM constructs of behavioral confidence and emotional transformation.
Patients with a medical home tend to fare better. One of the first steps toward establishing a medical home is to create panels by designating a clinic responsible for each patient. In 2006, we defined active clinic panels (all patients assigned to a clinic and seen there for one or more outpatient medical visits during the past 2 years) for the San Francisco Department of Public Health's 13 community- and four public hospital-based primary care clinics and began automatically assigning previously unassigned patients to clinics based on utilization. In 2007, we created a Web-based user interface for managing panels from within the electronic medical record. Providers and medical directors can now view and verify their panels and link to patient demographic and utilization data. Through April 2008, 14 508 patients have been auto-assigned to a clinic; on average 320 patients were assigned monthly. A total of 82,637 primary care patients were on a clinic panel, and 73.6 percent of them were active. Patient demographics, panel size, and productivity vary considerably by clinic. By establishing active panels and providing Web-based access to panel data, we can systematically assign patients a clinical home; enable providers to manage their panels; accurately measure utilization, capacity, and productivity; assess patient characteristics; and generate clinical quality indicators based on an accurate denominator. These management tools will allow us to set policies and work toward our goal of establishing a medical home for San Franciscans who rely on publicly funded care.
Routine data generation, review of data with administrators and providers, data-driven policies and panel size standards, and interventions to bolster team-based care are important tools for increasing capacity at our primary care clinics.
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