Across developed countries, experimentation with alcohol, tobacco, and other drugs often begins in the early adolescent years. Several evidence-based programs have been developed to prevent adolescent substance use. Many of the most rigorously tested and empirically supported prevention programs were initially developed and tested in the United States. Increasingly, these interventions are being adopted for use in Europe and throughout the world. This paper reports on a large-scale comprehensive initiative designed to select, adapt, implement, and sustain an evidence-based drug abuse prevention program in Italy. As part of a large-scale regionally funded collaboration in the Lombardy region of Italy, we report on processes through which a team of stakeholders selected, translated and culturally adapted, planned, implemented and evaluated the Life Skills Training (LST) school-based drug abuse prevention program, an evidence-based intervention developed in the United States. We discuss several challenges and lessons learned and implications for prevention practitioners and researchers attempting to undertake similar international dissemination projects. We review several published conceptual models designed to promote the replication and widespread dissemination of effective programs, and discuss their strengths and limitations in the context of planning and implementing a complex, large-scale real-world dissemination effort. 相似文献
Several considerations guided the research reported in this paper. First, recovery is pivotal for preventing stressful experiences from inducing long-term consequences. Second, cortisol levels under relaxed conditions constitute a good baseline measure. Third, there are many calls to avoid common method problems. Therefore, the Job Demands–Control (JDS) model, one of the most prominent models in occupational stress, should be tested by a combination of observation, self-report, and physiological data in terms of predicting recovery-related variables. In a sample of 53 Swiss employees, we assessed the JDS variables, demands and control, by systematic observation, fatigue at the end of work as an indicator of short-term recovery by questionnaire, and delayed recovery by baseline levels of cortisol on a Sunday under relaxing conditions. In line with expectations, regression analyses showed an impact of job demands and control on Sunday cortisol levels, and this effect was fully mediated by after work fatigue. Contrary to expectations, there was no significant interaction between job demands and control. Demonstrating that job demands and control predict after-work fatigue as well as a delayed physiological marker of recovery, these findings suggest that high after-work fatigue may entail costs to the individual’s physiological systems. 相似文献
This paper starts with the assumption that when people are asked to describe the level of demands they face at work, it cannot be assumed that those demands are necessarily stressful, even if they are rated as strong or high demands. Thirty demand questions were designed for use with a sample of 2,253 public sector employees in Western Australia. As well as rating frequency of demand the respondents were asked to rate their level of dissatisfaction with the demand. For only 16 of the demands was there a correlation high enough to assume that the demand might be a stressor. Having demonstrated this, the rest of the paper compares different ways of combining the two scores to predict the level of psychological distress as measured by the General Health Questionnaire (GHQ12). The results support the claim in the title, that it is important to know the affective meaning of job demands. 相似文献
ABSTRACT In the stepwise procedure of selection of a fixed or a random explanatory variable in a mixed quantitative linear model with errors following a Gaussian stationary autocorrelated process, we have studied the efficiency of five estimators relative to Generalized Least Squares (GLS): Ordinary Least Squares (OLS), Maximum Likelihood (ML), Restricted Maximum Likelihood (REML), First Differences (FD), and First-Difference Ratios (FDR). We have also studied the validity and power of seven derived testing procedures, to assess the significance of the slope of the candidate explanatory variable x2 to enter the model in which there is already one regressor x1. In addition to five testing procedures of the literature, we considered the FDR t-test with n ? 3 df and the modified t-test with n? ? 3 df for partial correlations, where n? is Dutilleul's effective sample size. Efficiency, validity, and power were analyzed by Monte Carlo simulations, as functions of the nature, fixed vs. random (purely random or autocorrelated), of x1 and x2, the sample size and the autocorrelation of random terms in the regression model. We report extensive results for the autocorrelation structure of first-order autoregressive [AR(1)] type, and discuss results we obtained for other autocorrelation structures, such as spherical semivariogram, first-order moving average [MA(1)] and ARMA(1,1), but we could not present because of space constraints. Overall, we found that:
the efficiency of slope estimators and the validity of testing procedures depend primarily on the nature of x2, but not on that of x1;
FDR is the most inefficient slope estimator, regardless of the nature of x1 and x2;
REML is the most efficient of the slope estimators compared relative to GLS, provided the specified autocorrelation structure is correct and the sample size is large enough to ensure the convergence of its optimization algorithm;
the FDR t-test, the modified t-test and the REML t-test are the most valid of the testing procedures compared, despite the inefficiency of the FDR and OLS slope estimators for the former two;
the FDR t-test, however, suffers from a lack of power that varies with the nature of x1 and x2; and
the modified t-test for partial correlations, which does not require the specification of an autocorrelation structure, can be recommended when x1 is fixed or random and x2 is random, whether purely random or autocorrelated. Our results are illustrated by the environmental data that motivated our work.