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MOL #67454 1 1 Quantification of functional selectivity at the human α 1A -adrenoceptor MOL #67454 2 2 Running Title: Functional selectivity at the α 1A -AR
No full textABSTRACT Although G protein-coupled receptors (GPCRs) are often categorized in terms of their primary coupling to a given type of Gα protein subunit, it is now well established that many show promiscuous coupling and activate multiple signaling pathways. Furthermore Interestingly, epinephrine, a second endogenous adrenoceptor agonist, did not display bias relative to norepinephrine. Our finding that phenylephrine displayed significant signaling bias, despite being highly similar in structure to epinephrine, indicates that subtle differences in agonist-receptor interaction can affect conformational changes in cytoplasmic domains and thereby modulate the repertoire of effector proteins that are activated
Simulation League: The Next Generation
No full textAbstract. We present a modular approach to model multi-agent simulations in 3D environments. Using this approach, we implemented a generic simulator which is totally decoupled from the actual simulation it performs. We believe that for Soccer Simulation League a transition to 3D states exiting new research problems and equally makes it more attractive to watch for spectators. We are proposing to use our framework as basis for a next generation Soccer Server
Transfer of Training: A Meta-Analytic Review On behalf of: Southern Management Association can be found at: Journal of Management Additional services and information for Transfer of Training: A Meta-Analytic Review
No full textAlthough transfer of learning was among the very first issues addressed by early psychologists, the extant literature remains characterized by inconsistent measurement of transfer and significant variability in findings. This article presents a meta-analysis of 89 empirical studies that explore the impact of predictive factors (e.g., trainee characteristics, work environment, training interventions) on the transfer of training to different tasks and contexts. We also examine moderator effects of the relationships between these predictors and transfer. Results confirmed positive relationships between transfer and predictors such as cognitive ability, conscientiousness, motivation, and a supportive work environment. Several moderators had significant effects on transfer relationships, including the nature of the training objectives. Specifically, most predictor variables examined (e.g., motivation, work environment) Introduction Today's most progressive organizations have moved from treating some select human resource management practices (e.g., incentive compensation, employee participation, flexible work arrangements, training) as obligatory cost factors to regarding them as strategic weapons in the battle for competitive advantage. Consistently included in any discussion of such highperformance human resource practices is employee training According to a recent American Society for Training and Development study, U.S. organizations spend more than $125 billion annually on employee training and development In organizational contexts, original learning in a training experience is rarely enough to render that training effective. Rather it is the positive transfer of training-the extent to which the learning that results from a training experience transfers to the job and leads to meaningful changes in work performance-that is the paramount concern of organizational training efforts The purpose of the current study is to provide a comprehensive meta-analysis of predictors of transfer of training. Given the clarion call for more evidence-based practice within the management discipline The Training Transfer Concept The transfer of learning has been an enduring problem in psychology and education Transfer was originally defined as the extent to which learning of a response in one task or situation influences the response in another task or situation (e.g., see While much of the initial interest in transfer focused on understanding educational issues, such as how learning in one domain might affect learning in another Transfer Research As noted by Brown and Sitzmann (in press), the most frequently cited model of training transfer is one presented by The goal of A number of authors have recently attempted to qualitatively summarize what we know about transfer from this expanding research base Contributions of Our Quantitative Review Existing qualitative reviews have provided some evidence of the factors that can affect transfer. Nevertheless, a demand for the "best evidence" to drive future research and practice requires meta-analytic estimates of transfer relationships. Meta-analytic estimates allow us to resolve inconsistent findings in the literature by including confidence intervals, corrected estimates, and measures of variability in correlations across studies, permitting more accurate inferences of the strength and consistency of relationships. We discuss key metaanalyses related to transfer below, highlighting the unique contributions of our meta-analysis. Of the three training inputs identified by As noted by Brown and Sitzmann (in press), research studies need to provide information on training objectives so that research can examine the extent to which training is effective when certain types of objectives are the focus of training. We also examine the relationship between transfer and training interventions (pre-and posttraining), learning outcomes (i.e., trainee knowledge or learning, self-efficacy), and reactions. Our examination of the learning-transfer and reaction-transfer relationships is an update of a meta-analysis by Alliger, Tannenbaum, Bennett, Traver, and Shotland (1997), which focused on the relationships between training criteria. Finally, we consider how transfer has been operationalized and how research design decisions affect reported relationships between training input factors and training transfer. Although the distinction has rarely been made explicit, transfer has typically been measured as either the use of a trained skill or the effectiveness in performing the trained skill. These two ways of measuring transfer are clearly different from one another, and it is unknown to what extent the way transfer has been measured could affect the relationships. In addition, training researchers have too rarely considered how common method variance and the type of transfer measure (ratings, objective measures) will affect observed relationships between predictor variables of interest and training transfer. Taylor, Russ-Eft, and Taylor's (2009) meta-analysis found that ratings of the impact of behavioral modeling training were related in part to the source (self, supervisor) of the transfer measure. Therefore, it is critical that we understand the extent to which transfer relationships differ depending on how transfer is measured. We empirically examine these issues and others (e.g., lab vs. field studies, time between end of training and the measurement of transfer) by looking at moderators of meta-analytic estimates. Research Questions A wide variety of empirical studies have emerged since Research Question 1a: What is the size of the relationship between transfer and trainee characteristics (e.g., cognitive ability, experience, personality, motivation), work environment factors (i.e., support, climate, constraints/opportunity), training interventions, learning outcomes (i.e., knowledge, self-efficacy), and trainee reactions. Directly related to obtaining these estimates, we found a subset of studies that are likely to inflate transfer relationships and, if included in the meta-analytic estimates, would provide inaccurate results. These studies are those in which same-source and samemeasurement-context (SS/SMC) effects are present. For example, in some studies exploring effects of work environment on transfer outcomes, the measurements of both the input factor (i.e., support) and the outcome factor of transfer were gathered from self-report measures at the same time (e.g., Research Question 1b: What are the predictor-transfer relationships after removing SS/SMC bias? Moderators Directly Related to Transfer Measures Another set of unaddressed questions concerns how the operationalization of transfer or measurement context affects the relationship between predictor variables and transfer. We contend that three specific distinctions of transfer measurement are particularly important. First, transfer measures can be taken immediately after training or after some time lag. We would expect that predictor and transfer relationships will be stronger when transfer measures are taken immediately after training, without a time lag. Third, transfer has been measured as both the use of a trained knowledge or skill and the effectiveness of the trainee in applying the knowledge or skill. An example of a use measure of transfer is found in Research Question 2. To what extent does the way that transfer is measured, including (a) whether a time lag exists between training and the transfer measure, (b) self versus non-self (i.e., peer or objective) measures, and (c) use versus effectiveness, influence predictor-transfer relationships? Open Versus Closed Skills As discussed above, among the most conspicuous gaps in the transfer literature is a neglect of how the open or closed nature of the skills being trained (i.e., training objectives) affects subsequent transfer. Training objectives tied to learning specific skills that are to be produced identically in the transfer environment as in the learning context are labeled closed skills, whereas training objectives tied to learning principles are labeled open skills In the case of open skills, the trainee has more choice regarding whether, how, and when to transfer. For example, those more motivated to learn an open skill will more likely seek opportunities in the work place to apply the training and perhaps also seek out coworker support for applying trained skills Additional Moderators Lab versus field context. The transfer literature is characterized by a volume of both laboratory and field studies. Laboratory studies typically include a student sample, whereas field studies have nonstudent samples. Laboratory studies often allow for more control over key variables and thus provide the potential for greater in-depth examination of transfer processes and outcomes Research Question 5: What is the impact on predictor-transfer relationships related to whether the data were from a published or unpublished source? Time between the end of training and the transfer measure. In addition to issues related to the source and type of measurement discussed above, the timing of the measurement is important to consider. More specifically, it seems likely that the length of time between the end of training and a measure of transfer can affect the relationship between predictor variables and transfer. From the standpoint of the temporal context Relationships Between Transfer Measures Our final research question examines the relationship of multiple measures of transfer for a subset of longitudinal studies in which transfer is assessed by multiple sources or at multiple times. First, we examined the relationship between trainees' versus others' (e.g., supervisor or peers) assessment of transfer when these measures are obtained at the same time. Although we are not aware of a meta-analysis on transfer that has reported such relationships, we might expect correlations to be similar to those found between self-other ratings of job performance. For example, Heidemeier and Moser (2009) found an overall meta-analytical estimate of the correlation between self-and supervisory ratings of .22 (r = .34 when corrected for measurement error). Second, we examined the correlation of repeated measures of transfer by the same source at different times. This relates to Method Literature Search We conducted an extensive search for primary empirical studies reporting a correlation between training transfer and at least one of the following variables: age, gender, education, experience, cognitive ability, the Big Five personality traits, locus of control, goal orientation, job involvement, voluntary participation, pretraining self-efficacy, motivation to learn or transfer, work environment, learning outcomes of knowledge or self-efficacy, reactions, and pre-or posttransfer intervention. We defined a measure as transfer if the skill being trained was assessed (a) through different or more complex tasks than the tasks in the training session or (b) in an environment different from the training environment. We excluded studies that looked only at learning outcomes (e.g., declarative or procedural knowledge). We limited the search results to articles that were published in English and based on healthy adult samples (i.e., we excluded studies of children or of adults with a medical condition). Studies included in our meta-analysis were identified by a variety of methods. First, we conducted a search of the PsycINFO and ERIC databases using the keywords training transfer, transfer of training, training effectiveness, and learning transfer for the years 1988 through 2008. In addition to the above four keywords, we conducted an expanded search using the additional keywords training outcome and training performance from several key academic journals 1 to obtain as many published articles as possible that might contain training transfer measures. We also identified studies published prior to 1988 by examining all studies reviewed in For a study to be included in the meta-analysis, it had to either report or allow the computation of a correlation coefficient between any of the predictor variables and a measure of training transfer. 2 On the basis of our literature search, we identified 93 independent samples (N = 24,493), including 60 published articles, 5 unpublished conference papers, 26 dissertations, and 2 unpublished articles. Coding for Meta-Analysis After developing guidelines for coding, two of the authors coded an initial set of 13 articles. Three of the authors then discussed problems encountered and revised the guidelines. The first two then coded and discussed five additional articles. Any discrepancies were resolved by using a consensus discussion among all of us. One of us subsequently coded the remainder of the articles included in the meta-analysis. Another of us also coded 36 of these articles to allow for an examination of interrater agreement. Each of the studies identified was coded as follows: Independence Assumption When a study reported multiple indicators of a focal construct, we followed the recommendation by Geyskens et al. Outliers A primary study coefficient may be an outlier in a meta-analytic study because of unique features of the study's design or sample or because of errors in analyses or reporting Meta-Analytic Procedures We used Hunter and Schmidt's (2004) meta-analytic procedure to conduct the overall analysis on the relationship between each predictor variable and transfer. We calculated a sample-weighted average correlation (r -) and derived the mean of population correlation (r) by correcting for unreliability in both the predictor and dependent variables. Consistent with the suggestion by Geyskens et al. Moderator Analyses We used two heterogeneity tests to aid the detection of moderating effects: the 75% rule proposed by To examine the effect of categorical moderators, we used Hunter and Schmidt's (2004) subgroup analysis. When the subsequent subgroups displayed different mean corrected correlations and had lower average corrected variance than the whole group did, we concluded the categorical moderator was in effect. When moderators are not orthogonal to each other, hierarchical subgroup analysis can separate effects of different moderators To examine the effect of continuous moderators, we employed weighted least squares (WLS) regression with inverse sampling error weighting suggested by Steel and KammeyerMueller (2002). We conducted WLS regression only when there were more than 10 studies for the analysis. Moderator variables with severe positive skewness were first log transformed or power transformed to approximate normal distribution before WLS regression analysis. Results Study Characteristics The final 89 studies that contributed at least one effect size to the meta-analyses included 58 journal articles, 5 conference papers, 24 dissertations, and 2 unpublished manuscripts. The majority of the samples (85%) were from the United States and Canada. The trainees included undergraduate students (24 studies, 27%), MBA or graduate students (12 studies, 13%), managers and supervisors (21 studies, 24%), and other nonmanagerial personnel (32 studies, 36%). The sample included 61 field studies and 28 lab experiments. All of the lab studies had student samples, whereas 90% of field studies had nonstudent samples. The time between training and the transfer measures ranged from immediately after training to 163 weeks after training and was shorter for lab studies (i.e., M = 1.6 weeks; median = 1 day) than for field studies (i.e., M = 15 weeks; median = 7.5 weeks). The median length of training was 6 hours. The field studies contained more open skills training (44 open, 15 closed, and 2 not codable) whereas the lab studies had more closed skills training (12 open and 16 closed). The most common closed skills trained were computer software and those involving simulations, such as flight simulator tasks. Of the 56 studies involving open skills, 71% included interpersonal or leadership skills (e.g., teamwork, negotiation) and 29% included other open skills (e.g., problem solving, substance abuse prevention). The main effects of the predictor variables on transfer are presented in Main Effects Considering SS/SMC Our results indicate that when the predictor variables and transfer were both measured by the trainee at the same time, this SS/SMC bias consistently inflated the relationships between the constructs examined. For example, in the relationship between environment and transfer, the correlation for studies with SS/SMC bias was .54, whereas the correlation for studies without SS/SMC bias was .23. When the 13 studies that had SS/SMC bias were included in the calculation of the effect size, it increased from .23 to .36. Another example is motivation, for which the correlation with transfer was .23 for those studies without SS/ SMC bias versus .41 when SS/SMC bias was present. SS/SMC bias also inflated the results of the relationship with transfer for the following constructs: locus of control, goal orientation, job involvement, posttraining self-efficacy, and utility reactions. To illuminate the uninflated or true relationship between constructs, we report on and discuss below only those results that exclude SS/SMC bias. Among the trainee characteristics examined, cognitive ability (.37), conscientiousness (.28), and voluntary participation (.34) had moderate relationships with training transfer. It should be noted that all but two of the studies that examined the relationship between transfer and cognitive ability were in the lab context, most with no time between the end of training and the transfer measure. Neuroticism (.19), pretraining self-efficacy (.22), and motivation (.23), had small to moderate relationships with transfer. Small correlations were found between training transfer and Big Five personality dimensions agreeableness (−.03), extraversion (.04), and openness to experience (.08). In addition, small correlations were found between training transfer and trainees' age (.04), education (.07), male gender (.12), experience (.09), external locus of control (−.06), and job involvement (.04). Small correlations were also found for learning goal orientation (.14), prove-performance goal orientation (.03), and avoid-performance goal orientation (−.12). Most of the studies involving goal orientation examined transfer in the lab context with little or no time between training and the transfer measure. In addition to the effect size of .22 discussed above for a general environment construct, we were able to perform an analysis on the environment context variable based on how environment was measured. We classified these measures into three different categories: support (e.g., peer support, supervisor support), transfer climate, and reversed-scored organizational constraints (e.g., lack of autonomy, situational constraints). Results indicated that transfer climate had the highest relationship with transfer (.27), followed by support (.21) and constraints (.05; reverse scored), although constraints was based on only two studies. Subsequent analysis indicated that supervisor support (.31) may have a stronger relationship with transfer than does peer support (.14), although these relationships also are based on small sample sizes. For learning outcomes, posttraining self-efficacy (.20) and posttraining knowledge (.24) both had small to moderate effects on transfer. Utility reactions had a corrected correlation of .17, while both affective reactions and reactions that included both affective and utility dimensions had small relationships (.08) with training transfer. The effect of training interventions (i.e., optimistic preview, goal-setting, and relapse prevention) on transfer were small to moderate (.20, .08, and −.06, respectively), and the between-study variation of effect sizes for optimistic preview and relapse prevention was fully attributed to artifacts. However, these results should be interpreted with caution as they are based on a fairly small number of studies (i.e., 3 to 6). In addition, the 80% credibility interval for the correlation between transfer and goal-setting interventions ranged from −.17 to .33, indicating the presence of moderators on how effective these interventions were on improving transfer. An important moderator for goal-setting interventions discussed below is the lab-versus-field context. Moderator Analyses Given our finding that SS/SMC bias consistently inflates the observed relationships between predictors and transfer, the presence of SS/SMC bias could confound any further moderator analysis if SS/SMC bias is not orthogonal to the moderator variable. Therefore, we used only the studies without SS/SMC bias when further examining effects of other moderators. 3 Time lag versus no time lag between end of training and transfer measure. Pretraining selfefficacy is influenced by when transfer is measured, that is, r = .32 when taken immediately after training, versus r = .21 when there is a time lag between training and the transfer measure. For the four studies in which transfer was measured immediately after training, the correlation of posttraining self-efficacy was .38, which was significantly higher than the correlation of .11 for the 11 studies in which there was at least some time between training and the transfer measure. As compared with studies that didn't have a time lag, when there was a time lag between training and the transfer measure, our results also indicate a decrease in r for posttraining knowledge (i.e., from .48 to .18) and for experience (i.e., from
Grading Doc. 2/5/99
No full textEducators have long been faced with the challenge of evaluating student performance. Even in a time when education excluded many of those with diverse learning needs, grading and reporting were difficult tasks. Now the issues surrounding grading and reporting have become even more complex. Although the focus of this paper is students with Individualized Education Programs (IEPs) under the Individuals with Disabilities Education Act (IDEA), our country's overall goal is to provide a free appropriate appropriate appropriate appropriate appropriate public education (FAPE) to all all all all all students. One way to determine the appropriateness of education is to evaluate student performance, but there are many ways to conduct those evaluations. How do we set up guidelines for evaluation that are fair, equitable, and useful to students, parents, and teachers? When grading students, a number of concepts serve as guiding principles. Christianson (1997) put it as follows: Teachers should keep in mind that grades communicate a spectrum of information: ♦ The relative quality (not quantity) of an individual's work. ♦ The student's readiness for future instruction. ♦ The status of a student's work. ♦ The student's level of competence/skill mastery (IEP conditions). ♦ Progress and effort. This range of information is important in communicating the achievements of all all all all all students, with or without disabilities. The reliable reporting of such information is also a critical component in measuring the impact of educational programming
); and SmithKline Beecham Pharmaceuticals, Drug Metabolism and Pharmacokinetics
Get PDFABSTRACT The pharmacokinetics and pharmacodynamics (PK/PD) of a humanized anti-Factor IX IgG1 monoclonal antibody (SB 249417, FIX mAb) were studied in Cynomolgus monkeys. Single i.v. bolus doses of 1, 3, or 10 mg/kg of FIX mAb were administered. The total FIX mAb concentration, activated partial thromboplastin time (aPTT), and Factor IX activity were monitored for up to 4 weeks after dosing. In the monkey, FIX mAb had a plasma clearance of 0.6 ml/h/kg and a steady-state volume of distribution of approximately 70 ml/kg. The elimination phase half-life (3.8 days) was considerably less than other humanized IgG1 mAbs in the monkey, for which there is no binding to endogenous antigen. The suppression of Factor IX activity and the prolongation of aPTT were rapid and dose dependent. The time for aPTT values to return to basal levels (25-170 h) increased with increasing dose. A mechanismbased PK/PD model consistent with the stoichiometry of binding (2:1) was developed to describe the Factor IX activity and aPTT response time course. The model incorporated Factor IX synthesis and degradation rates that were interrupted by the sequestration of Factor IX by the antibody. aPTT values were related to free Factor IX activity. This model was able to describe the PD profiles from the three dose levels simultaneously. The estimated Factor IX half-life was 11 h and the third-order association rate constant was 3.96 ϫ 10 . The PK/PD modeling was useful in summarizing the major determinants (endogenous and antibody-ligand binding) controlling FIX mAb-related effects. Anticoagulant therapy is important for treatment of various vascular disorders including coronary artery thrombosis, deep venous thrombosis, pulmonary embolism, and peripheral arterial occlusion. The types of agents commonly in use include heparin, which acts by binding to antithrombin III (ATIII) and inactivating a number of coagulation enzymes including thrombin (IIa) and Factor Xa The coagulation system can be activated by two separate pathways: the tissue (extrinsic) and contact factor (intrinsic) pathways. Such activation results in the production of thrombin and subsequently the formation of fibri
Survey on Intrusion Detection System in Heterogeneous WSN Using Multipath Routing
Get PDFAbstract: In this paper, we propose an survey on heterogeneous wireless sensor network (HWSN
Spin reorientation in Al/Metglas 2605S2/Al trilayers induced by magnetoelastic effect
Get PDFMössbauer spectroscopy, in a broad temperature interval of 12-425 K, has been applied to investigate the spin reorientation dynamics caused by the temperature induced magnetoelastic effect on Al͑x m͒/Metglas 2605S2 ͑20 m͒ / Al͑x m͒ trilayers ͑x = 0; 2.5; 5 and 20͒. It was found that the angle between the average sample magnetization and gamma ray direction ͑perpendicular to the sample plane͒ depends on the Al layer thickness. For temperatures smaller than 260 K, saturation of spin reorientation, which can be controlled by adjusting the Al thickness, was reached for Al thicknesses larger than and equal to 5 m. For a 20 m Al thickness, changes in the 57 Fe atom spin and charge densities have also been observed. A simple spin model has been proposed to describe qualitatively the spin reorientation effect as well as the influence of the Al thickness on the spin reorientation sensitivity
Video Estimates of Red Snapper and Associated Fish Assemblages on Sand, Shell, and Natural Reef Habitats in the North-Central Gulf of Mexico
Get PDFAbstract.-Video estimation of the relative abundance of fishes is a noninvasive method commonly used to assess fish densities. This technique can be used to characterize habitat use patterns either of fish assemblages or of a particular species of interest. The objectives of this study were to quantify relative abundance of red snapper, Lutjanus campechanus, and to characterize with video methodology the associated fish assemblages over different habitat types. Fishes were enumerated over sand, shell, and natural hard bottom reef habitats in the north-central Gulf of Mexico (GOM) off Alabama on quarterly cruises over a two-year period with a baited stationary underwater video camera array. Red snapper showed both significantly higher abundance and larger size over the reef habitat; however, no seasonal effects were observed, indicating temporal abundance patterns were consistent among seasons. Fish assemblages differed among habitats, with significant differences between reef and shell assemblages. Efforts to identify the species that most contributed to these differences indicated that the red snapper accounted for 59% of the overall similarity within the reef fish assemblage and 20% of the total dissimilarity between the shell and reef fish assemblages. This study highlights the utility of applying video techniques to identify the importance of sand, shell, and reef habitat types both to different life stages of red snapper, and to the different fish assemblages occupying distinct habitats in the north-central GOM
Activation of Nerve Growth Factor-Induced B␣ by Methylene- Substituted Diindolylmethanes in Bladder Cancer Cells Induces Apoptosis and Inhibits Tumor Growth □ S
Get PDFABSTRACT Nerve growth factor-induced B (NGFI-B) genes are orphan nuclear receptors, and NGFI-B␣ (Nur77, TR3) is overexpressed in bladder tumors and bladder cancer cells compared with nontumorous bladder tissue. 1,1-Bis(3Ј-indolyl)-1-(p-methoxyphenyl)-methane (DIM-C-pPhOCH 3 ) and 1,1-bis(3Ј-indolyl)-1-(p-phenyl)methane have previously been identified as activators of Nur77, and both compounds inhibited growth and induced apoptosis of UC-5 and KU7 bladder cancer cells. The proapoptotic effects of methylene-substituted diindolylmethanes (C-DIMs) were unaffected by cotreatment with leptomycin B and were dependent on nuclear Nur77, and RNA interference with a small inhibitory RNA for Nur77 (iNur77) demonstrated that C-DIM-induced activation of apoptosis was Nur77-dependent. Microarray analysis of DIM-C-pPhOCH 3 -induced genes in UC-5 bladder cancer cells showed that this compound induced multiple Nur77-dependent proapoptotic or growth inhibitory genes including tumor necrosis factor-related apoptosisinducing ligand (TRAIL), cystathionase, p21, p8, and sestrin-2. DIM-C-pPhOCH 3 (25 mg/kg/d) also induced apoptosis and inhibited tumor growth in athymic nude mice bearing KU7 cells as xenografts, demonstrating that Nur77-active C-DIMs exhibit potential for bladder cancer chemotherapy by targeting Nur77, which is overexpressed in this tumor type. The nuclear receptor family of transcription factors includes the steroid and thyroid hormones, vitamin D, retinoid and ecdysone receptors, ligand-activated orphan receptors, and orphan receptors with no known ligand
Electronic screening and brief intervention for risky drinking in Swedish university students -A randomized controlled trial
Get PDFa b s t r a c t a r t i c l e i n f o Keywords: College student Alcohol Electronic screening Brief intervention Computer RCT Background: The limited number of electronic screening and brief intervention (e-SBI) projects taking place in young adult student populations has left knowledge gaps about the specific methods needed to motivate reduced drinking. The aim of the present study was to compare differences in alcohol consumption over time after a series of e-SBIs was conducted with two groups of young adult students who were considered risky drinkers. The intervention group (IG) (n = 80) received extensive normative feedback; the control group (CG) (n = 78) received very brief feedback consisting of only three statements. Method: An e-SBI project was conducted in naturalistic settings among young adult students at a Swedish university. This study used a randomized controlled trial design, with respondents having an equal chance of being assigned to either the IG or the CG. The study assessed changes comparing the IG with the CG on four alcohol-related measurements: proportion with risky alcohol consumption, average weekly alcohol consumption, frequency of heavy episodic drinking (HED) and peak blood alcohol concentration (BAC). Follow-up was performed at 3 and 6 months after baseline. Results: The study documented a significant decrease in the average weekly consumption for the IG over time but not for the CG, although the differences between the groups were non-significant. The study also found that there were significant decreases in HED over time within both groups; the differences were about equal in both groups at the 6-month follow-up. The proportion of risky drinkers decreased by about a third in both the CG and IG at the 3-and 6-month follow-ups. Conclusions: As the differences between the groups at 6 months for all alcohol-related outcome variables were not significant, the shorter, generic brief intervention appears to be as effective as the longer one including normative feedback. However, further studies in similar naturalistic settings are warranted with delayed assessment groups as controls in order to increase our understanding of reactivity assessment in email-based interventions among students