大麻使用量表的日常会话、频率、发病年龄和数量评估及其与大麻相关问题的关系

Cannabis (Albuquerque, N.M.) Pub Date : 2023-11-03 eCollection Date: 2023-01-01 DOI:10.26828/cannabis/2023/000161
Jordan A Gette, Andrew K Littlefield, Sarah E Victor, Adam T Schmidt, Sheila Garos
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引用次数: 0

摘要

新兴成年人中大麻的使用和大麻使用障碍的流行率正在上升。现存文献中提出了大麻使用的几个指标(如数量、频率),因为它们与负面结果有关。尽管研究了指标与大麻结果之间的联系,但很少对大麻使用指标进行评估。制定每日会议、频率、发病年龄和大麻使用量清单(DFAQ-CU)是为了评估一系列因素的大麻使用情况。然而,DFAQ-CU的因子结构尚未复制。此外,尽管形成策略在概念上是合适的,但DFAQ-CU是使用反思策略建模的。本研究利用主成分分析(PCA)和主轴因子分解(PAF)来评估DFAQ-CU的结构。主成分分析得出了四个组成部分的解决方案;PAF产生了一个五因素解决方案。线性回归发现PCA成分和PAF因素与CUD症状和大麻相关问题之间存在显著关系;然而,PAF的效应大小更大,这表明可能存在误定位。主成分分析证明了大麻和酒精行为测量的判别和收敛有效性。该研究通过完善大麻使用评估和增强我们对模型选择重要性的理解,为研究和临床工作提供信息。
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Evaluation of the Daily Sessions, Frequency, Age of Onset, and Quantity of Cannabis Use Questionnaire and its Relations to Cannabis-Related Problems.

Cannabis use and the prevalence of cannabis use disorder (CUD) among emerging adults are on the rise. Several indicators of cannabis use (e.g., quantity, frequency) as they relate to negative outcomes have been posited in the extant literature. Despite research examining links between indicators and cannabis outcomes, few assessments of cannabis use indicators exist. The Daily Sessions, Frequency, Age of Onset, and Quantity of Cannabis Use Inventory (DFAQ-CU) was developed to assess cannabis use across a range of factors. However, the factor structure of the DFAQ-CU has not been replicated. Further, the DFAQ-CU was modeled using reflective strategies despite formative strategies being conceptually appropriate. The present study utilized principal components analyses (PCA) and principal axis factoring (PAF) to evaluate the structure of the DFAQ-CU. PCA yielded a four-component solution; PAF resulted in a five-factor solution. Linear regression found significant relations between PCA components and PAF factors with CUD symptoms and cannabis-related problems; however, effect sizes were larger for the PAF suggesting possible misdisattenuation. The PCA components demonstrated evidence of discriminant and convergent validity with measures of cannabis and alcohol behavior. The study informs research and clinical work through the refinement of cannabis use assessment and enhancing our understanding of the importance of model selection.

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