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Binge drinking and alcohol-related problems among students at traditional 4-year universities have been well documented. However, little is known about the frequency of their such behaviors and its consequences among community college students, who comprise roughly 44% of all undergraduate students in the United States. The present study examined binge drinking and alcohol-related problems in 762 (61% female) ethnically diverse (65% Caucasian, 20% Hispanic, 9% African American) community college students (mean age = 26.23, SD = 7.81). Based on gender-specific criteria, 25% engaged in binge drinking. As compared to nonbingers and current abstainers, bingers had higher rates of drinking-related problems. The implications of these findings for research and for prevention/intervention programs are discussed. (Contains 1 note.)

F. K. Del Boca, J. Darkes, P. E. Greenbaum, and M. S. Goldman (2004) examined temporal variations in drinking during the freshmen college year and the relationship of several risk factors to these variations. Here, using the same data, the authors investigate whether a single growth curve adequately characterizes the variability in individual drinking trajectories. Latent growth mixture modeling identified 5 drinking trajectory classes: light-stable, light-stable plus high holiday, medium-increasing, high-decreasing, and heavy-stable. In multivariate predictor analyses, gender (i.e., more women) and lower alcohol expectancies distinguished the light-stable class from other trajectories; only expectancies differentiated the high-decreasing from the heavy-stable and medium-increasing classes. These findings allow for improved identification of individuals at risk for developing problematic trajectories and for development of interventions tailored to specific drinker classes.

Surveys have documented excessive drinking among college students and tracked annual changes in consumption over time. This study extended previous work by examining drinking changes during the freshman year, using latent growth curve (LGC) analysis to model individual change, and relating risk factors for heavy drinking to growth factors in the model. Retrospective monthly assessments of daily drinking were used to generate weekly estimates. Drinking varied considerably by week, apparently as a function of academic requirements and holidays. A 4-factor LGC model adequately fit the data. In univariate analyses, gender, race/ethnicity, alcohol expectancies, sensation seeking, residence, and data completeness predicted growth factors (ps is less than .05); gender, expectancies, residence, and data completeness remained significant when covariates were tested simultaneously. Substantive, methodological, and policy implications are discussed.

F. K. Del Boca, J. Darkes, P. E. Greenbaum, and M. S. Goldman (2004) examined temporal variations in drinking during the freshmen college year and the relationship of several risk factors to these variations. Here, using the same data, the authors investigate whether a single growth curve adequately characterizes the variability in individual drinking trajectories. Latent growth mixture modeling identified 5 drinking trajectory classes: light-stable, light-stable plus high holiday, medium-increasing, high-decreasing, and heavy-stable. In multivariate predictor analyses, gender (i.e., more women) and lower alcohol expectancies distinguished the light-stable class from other trajectories; only expectancies differentiated the high-decreasing from the heavy-stable and medium-increasing classes. These findings allow for improved identification of individuals at risk for developing problematic trajectories and for development of interventions tailored to specific drinker classes.

Surveys have documented excessive drinking among college students and tracked annual changes in consumption over time. This study extended previous work by examining drinking changes during the freshman year, using latent growth curve (LGC) analysis to model individual change, and relating risk factors for heavy drinking to growth factors in the model. Retrospective monthly assessments of daily drinking were used to generate weekly estimates. Drinking varied considerably by week, apparently as a function of academic requirements and holidays. A 4-factor LGC model adequately fit the data. In univariate analyses, gender, race/ethnicity, alcohol expectancies, sensation seeking, residence, and data completeness predicted growth factors (ps < .05); gender, expectancies, residence, and data completeness remained significant when covariates were tested simultaneously. Substantive, methodological, and policy implications are discussed.

Two studies assessed perceived types of college students and associated stereotypes about drinking. In the first study, 64 university students responded to an open-ended probe asking them to list types of college students and then rated the amount of drinking done by each of a set of preselected types. In the second study, 236 students responded to the same open-ended item and directly rated a set of types that had been revised based on Study 1 in terms of drinking and involvement in the academic and sociosexual collegiate subcultures. As hypothesized, consensual responses to the open-ended probe reflected the college student culture. Also as hypothesized, types of students socially defined in terms of the sociosexual aspects of college (e.g., 'fraternity boy') were rated as likely to drink heavily, whereas types that were seen as being pulled away from college social life, through assumed involvement in academics (e.g., 'brain/straight As'), were rated as drinking relatively little. Finally, rated sociosexual involvement was positively correlated, and academic involvement was negatively correlated, with perceived drinking, which supports a central assumption of the framework guiding the research.

Objective: Researchers have devoted much attention to the influence of social support on positive health outcomes, including efficacy of alcoholism treatment. Yet relatively little research has examined the structure (e.g., quantity or frequency of interactions) and quality (e.g., subjective appraisals of value or adequacy) of alcoholics' relationships. In addition, few studies have explored adult alcoholics' friendships, despite research suggesting that friendships influence treatment outcomes. The current study examined friendship characteristics of Project MATCH participants. Method: Participants (1,183 men, 380 women) described the structure and quality of their friendships at baseline and posttreatment. Friendships with drinkers and nondrinkers were examined in relation to baseline and follow-up drinking levels, as well as participant gender, age and parental history of alcohol problems. Results: Significant changes were found in all aspects of friendships with both drinkers and nondrinkers from baseline to the 6-month posttreatment interview. For example, although approximately half of alcoholics' friendships were with drinkers at baseline, that proportion decreased following treatment, whereas the proportion of friendships with nondrinkers increased. There was support for the hypothesis that changes in both friendship quality and structure would predict follow-up drinking levels. Conclusions: This study offers persuasive evidence for the importance of quality and, especially, structure of friendships to adult alcoholics' drinking behavior. Results also suggest differences in friendships depending on gender and across the lifespan. Such individual differences may have implications for prevention and treatment. (J. Stud. Alcohol 62: 637-645, 2001), ********** INTERPERSONAL RELATIONSHIPS play an important role in the attainment and maintenance of health. Researchers have devoted most of their attention to the influence of social support on positive health [...]

Objective: This study was designed to compare the Timeline Follow-Back (TLFB) to daily and real-time assessments of drinking. Our purpose was to evaluate overall correspondence and day-to-day agreement between these two methods among both problem and moderate drinkers. Method: In Study 1, problem drinkers (n = 20) reported their alcohol consumption daily during 28 days of brief treatment. In Study 2, moderate drinkers (n = 48), recruited from the community, used a palm-top computer to record their drinking for 30 days. In both studies participants completed the TLFB covering the recording period. Results: Participants in Study 1 reported fewer drinking days, fewer drinks per drinking day and fewer total drinks per day on the TLFB, and those in Study 2 reported fewer drinks per drinking day, fewer ounces per drinking day, fewer total drinks per day and fewer total ounces per day. The magnitude of the difference, however, was modest. There was considerable between-person variation in day-to-day correspondence of TLFB and the daily and real-time reports. Neither person characteristics (gender, education and income) nor the distributional characteristics of drinking (including average consumption, variation) predicted concordance between TLFB and real-time reports. Conclusions: The Timeline Follow-Back method captured overall levels of drinking quite well compared to a 28-day daily diary and a 30-day electronic interview. Vast individual differences in day-to-day correspondence suggest that the TLFB may be less useful for detecting patterns of consumption. (J. Stud. Alcohol 59: 447-454, 1998), The Timeline Follow-Back (TLFB) drinking assessment, was evaluated for daily and real-time assessment. Problem drinkers recorded consumption day-to-day and moderate drinkers used a hand-held computer to record their drinking. The study determined that daily and real-time recording of consumption might be a unique methodological approach for examining patterning., The accuracy with which people recall their alcohol consumption is critical to our understanding of the causes, correlates and consequences of alcohol use, abuse and dependence (Babor et al., 1987, [...]

Although women generally have been subjects of alcohol research less often than men, gender can be used as a defining characteristic in subtyping schemes. Whether the sexes actually differ in [...]

Patients with psychiatric conditions and alcoholism may have varying degrees of success with in-hospital alcoholism treatment programs. Alcohol consumption and alcohol-related conditions were examined among 225 alcoholics with respect to the presence or absence of three psychiatric disorders after three years of alcoholism treatment. Patients with antisocial personality disorder had less treatment success. Patients who also abused other drugs tended to drink more heavily while those with major depression drank less. Male patients had less treatment success and had more alcohol-related conditions than female patients.

Open-label and placebo-controlled trials of fluvoxamine, a selective, serotonergic antidepressant, were conducted as an adjunct to relapse prevention psychotherapy in alcoholics. In the open trial, 16 inpatient alcoholics began a 12-week treatment program, with 10 patients dropping out during the first 4 weeks of treatment. In the controlled trial, 8 of 10 patients on fluvoxamine dropped out during the first 4 weeks of treatment, compared with only 1 of 9 patients on placebo. Baseline patient characteristics did not appear to explain the differential attrition in the controlled trial, although the placebo-treated patients were more alcohol dependent. In both trials, patients on fluvoxamine complained of a variety of adverse effects, which they often identified as the basis for early termination of treatment. These adverse effects appear to limit the usefulness of the medication for the treatment of alcoholism.

A recent expert committee report (National Institute of Mental Health 1990) stated that] America is wasting its most precious natural resource: its children and youth. More than 12 percent of […]

The distinction between "stereotype" as a cognitive structure and "stereotyping" as a complex set of intra- and inter-personal processes is discussed in light of relevant research in cognitive and social psychology. (Author/EB)