Greenbaum Paul E, Del Boca Frances K, Darkes Jack, Wang Chen-Pin, Goldman Mark S
Department of Child and Family Studies, Louis de la Parte Florida Mental Health Institute, University of South Florida, Tampa, FL 33620-8200, USA.
J Consult Clin Psychol. 2005 Apr;73(2):229-38. doi: 10.1037/0022-006X.73.2.229.
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, highdecreasing, 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.
F.K.德尔博卡、J.达克斯、P.E.格林鲍姆和M.S.戈德曼(2004年)研究了大学一年级期间饮酒的时间变化以及几个风险因素与这些变化的关系。在此,作者使用相同的数据,调查单一增长曲线是否能充分描述个体饮酒轨迹的变异性。潜在增长混合模型确定了5种饮酒轨迹类别:轻度稳定型、轻度稳定加节假日高饮酒型、中度增长型、高度下降型和重度稳定型。在多变量预测分析中,性别(即女性更多)和较低的酒精预期将轻度稳定型与其他轨迹区分开来;只有预期将高度下降型与重度稳定型和中度增长型区分开来。这些发现有助于更好地识别有发展成问题轨迹风险的个体,并有助于制定针对特定饮酒者类别的干预措施。