This research investigates the cognitive perceptual process that homeowners go through when faced with the decision to protect themselves from the risk of wildfires. This decision can be examined by looking at the interaction between the integrated protection motivation theory-transtheoretical model and different levels of homeowners' subjective knowledge related to wildfire risks. We investigated the role of motivation, decision stages of risk readiness, and subjective knowledge on the number of risk-mitigating actions undertaken by homeowners living in high-risk communities. The results indicate that homeowners who are in an early or precontemplative stage (both low and high subjective knowledge) as well as low knowledge contemplatives are motivated by their perceived degree of vulnerability to mitigate the risk. In contrast, high knowledge contemplatives' potential behavioral changes are more likely to be motivated by increasing their perceptions of the severity of the risk. Risk-mitigating behaviors undertaken by high knowledge action homeowners are influenced by their perceptions of risk severity, self-efficacy, and response efficacy. In contrast, the low knowledge action homeowners engage in risk reduction behaviors without the influence of any of the PMT variables; demonstrating their motivation to emulate others in their community. These results have implications for the type of information that should be used to effectively communicate risks in an effort to influence the diverse homeowner segments to engage in risk-reduction behaviors.

Influenza A pandemic (H1N1) 2009 virus continues to rapidly spread worldwide. In 2009, pandemic (H1N1) 2009 infection in a domestic cat from Iowa was diagnosed by a novel PCR assay that distinguishes between Eurasian and North American pandemic (H1N1) 2009 virus matrix genes. Human-to-cat transmission is presumed.

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This study describes the result of implementing the Problem List Generator (PLG), a computer-based tool designed to helpYou aren't feeling well, so you go to your doctor. A physical exam reveals nothing obvious, so blood and urine samples are sent to the lab. When the results come back, your doctor has a problem. The extent to which that problem is resolved satisfactorily (and, perhaps, your health) depends on your doctor's ability to interpret all the data that have been collected. This skill, the process of turning all the information available to the physician into a diagnosis and treatment plan, is known as clinical problem solving; the subproblem of correctly interpreting the clinical laboratory data is diagnostic problem solving.Smith and Ragan (1999) defined problem solving as "the ability to combine previously learned principles, procedures, declarative knowledge, and cognitive strategies in a unique way within a domain of content to solve previously unencountered problems" (p. 132). Jonassen (2000) suggested that problem solving involves two processes: (a) the construction of a mental model of the problem (the problem space), and (b) activity-based manipulation of the problem space. He argued that general models of problem solving have proven inadequate for dealing with the rich diversity of problems faced by learners, as manifested by the fact that, although researchers tend to value problem solving as a learning outcome, many instructional design models and teaching theories provide sparse or nonspecific guidance when it comes to instructional strategies for learning problem solving. There are many kinds of problems, varying in complexity and inex-

Teaching introductory clinical pathology to veterinary students is a challenging endeavor that requires a shift in learning strategies from rote memorization to diagnostic reasoning. Educational research has identified discrete cognitive stages required to achieve the automated, unconscious thinking process used by experts. Building on this knowledge, we developed a case-based approach to clinical pathology instruction that actively engages students in the learning process and links performance with positive reward. Simulated cases provide context and create a structure, or "schema", which enhances the learning process by enabling students to synthesize facts and link them with their causal mechanism to reach a defensible diagnostic conclusion. Web-based tools, including the "Problem List Generator" and tutorials, have been developed to facilitate this process. Through the collaborative Biomedical Informatics Research Group, we are working to further develop and evaluate Web-based instructional tools and new educational methods, to clarify the diagnostic reasoning processes used by experienced clinical pathologists, and, ultimately, to better educate our future students to be effective diagnosticians.