Objective: Self-report measures are typically used to assess the effectiveness of road safety advertisements. However, psychophysiological measures of persuasive processing (i.e., skin conductance response [SCR]) and objective driving measures of persuasive outcomes (i.e., in-vehicle Global Positioning System [GPS] devices) may provide further insights into the effectiveness of these advertisements. This study aimed to explore the persuasive processing and outcomes of 2 anti-speeding advertisements by incorporating both self-report and objective measures of speeding behavior. In addition, this study aimed to compare the findings derived from these different measurement approaches.
Methods: Young drivers (N = 20, M age = 21.01 years) viewed either a positive or negative emotion–based anti-speeding television advertisement. While viewing the advertisement, SCR activity was measured to assess ad-evoked arousal responses. The RoadScout GPS device was then installed in participants' vehicles for 1 week to measure on-road speed-related driving behavior. Self-report measures assessed persuasive processing (emotional and arousal responses) and actual driving behavior.
Results: There was general correspondence between the self-report measures of arousal and the SCR and between the self-report measure of actual driving behavior and the objective driving data (as assessed via the GPS devices).
Conclusions: This study provides insights into how psychophysiological and GPS devices could be used as objective measures in conjunction with self-report measures to further understand the persuasive processes and outcomes of emotion-based anti-speeding advertisements. 相似文献
Decades of intensive industrial and agricultural practices as well as rapid urbanization have left communities like Pueblo, Colorado facing potential health threats from pollution of its soils, air, water and food supply. To address such concerns about environmental contamination, we conducted an urban geochemical study of the city of Pueblo to offer insights into the potential chemical hazards in soil and inform priorities for future health studies and population interventions aimed at reducing exposures to inorganic substances. The current study characterizes the environmental landscape of Pueblo in terms of heavy metals, and relates this to population distributions. Soil was sampled within the city along transects and analyzed for arsenic (As), cadmium (Cd), mercury (Hg) and lead (Pb). We also profiled Pueblo’s communities in terms of their socioeconomic status and demographics. ArcGIS 9.0 was used to perform exploratory spatial data analysis and generate community profiles and prediction maps. The topsoil in Pueblo contains more As, Cd, Hg and Pb than national soil averages, although average Hg content in Pueblo was within reported baseline ranges. The highest levels of As concentrations ranged between 56.6 and 66.5 ppm. Lead concentrations exceeded 300 ppm in several of Pueblo’s residential communities. Elevated levels of lead are concentrated in low-income Hispanic and African-American communities. Areas of excessively high Cd concentration exist around Pueblo, including low income and minority communities, raising additional health and environmental justice concerns. Although the distribution patterns vary by element and may reflect both industrial and non-industrial sources, the study confirms that there is environmental contamination around Pueblo and underscores the need for a comprehensive public health approach to address environmental threats in urban communities. 相似文献
Models that predict distribution are now widely used to understand the patterns and processes of plant and animal occurrence as well as to guide conservation and management of rare or threatened species. Application of these methods has led to corresponding studies evaluating the sensitivity of model performance to requisite data and other factors that may lead to imprecise or false inferences. We expand upon these works by providing a relative measure of the sensitivity of model parameters and prediction to common sources of error, bias, and variability. We used a one-at-a-time sample design and GPS location data for woodland caribou (Rangifer tarandus caribou) to assess one common species-distribution model: a resource selection function. Our measures of sensitivity included change in coefficient values, prediction success, and the area of mapped habitats following the systematic introduction of geographic error and bias in occurrence data, thematic misclassification of resource maps, and variation in model design. Results suggested that error, bias and model variation have a large impact on the direct interpretation of coefficients. Prediction success and definition of important habitats were less responsive to the perturbations we introduced to the baseline model. Model coefficients, prediction success, and area of ranked habitats were most sensitive to positional error in species locations followed by sampling bias, misclassification of resources, and variation in model design. We recommend that researchers report, and practitioners consider, levels of error and bias introduced to predictive species-distribution models. Formal sensitivity and uncertainty analyses are the most effective means for evaluating and focusing improvements on input data and considering the range of values possible from imperfect models. 相似文献