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This study's objective was assessment of cardiovascular disease (CVD) risk factor knowledge in young adults, its association with 10-year changes in risk factor levels, and variables related to risk factor knowledge. A total of 4,193 healthy persons (55% female, 48% Black; mean age = 30 years) from four urban US communities were queried about risk factor knowledge in 1990-1991 and were reexamined in 2000-2001. Of six risk factors considered (hypertension, hyperlipidemia, smoking, overweight, sedentary lifestyle, and unhealthy diet), participants mentioned a mean of two; more than 65% were not aware of any risk factors, and less than 35% recognized being overweight as a risk factor. After adjustment, variables associated with mentioning more than two CVD risk factors versus one or fewer were Black race (OR = 0.52, 95% confidence interval (CI): 0.44, 0.61), having a high school education or less (OR = 0.88, 95% CI: 0.80, 0.95), having one or two (vs. zero) risk factors (OR = 1.27, 95% CI: 1.05, 1.53), and having three or more (vs. zero) risk factors (OR = 1.79, 95% CI: 1.35, 2.38). More knowledge was marginally associated with less increase in body mass index 10 years later (p = 0.06) but was unrelated to other risk factor changes. Knowledge of CVD risk factors was very low in these young adults but increased with the presence of risk factors. Knowledge alone did not predict 10-year changes in risk factors. [ABSTRACT FROM AUTHOR]

Many accounts of categorization equate goodness-of-example with central tendency for common taxonomic categories; the best examples of a category are average members--those that are most similar to most other category members. In the present study, we asked 24 tree experts and 20 novices to rate goodness-of-example for a sample of 48 trees and found (1) that the internal structure of the category tree differed between novices and experts and (2) that central tendency did not determine goodness-of-example ratings for either group. For novices, familiarity determined goodness-of-example ratings. For experts, the 'ideal' dimensions of height and weediness, rather than average similarity to other trees, were the primary predictors of goodness-of-example ratings for experts. The best examples of tree were not species of average height, but of extreme height. The worst examples were the weediest trees. We also found systematic differences in predictors of goodness-of-example as a function of type of expertise. We argue that the internal structure of taxonomic categories can be shaped by goal-related experience and is not necessarily a reflection of the attributional structure of the environment. Implications for models of category structure and category learning are discussed. [ABSTRACT FROM AUTHOR]