Introduction

While extensive research exists on the negative consequences of cannabis use, there is a noticeable gap in the literature regarding positive consequences on patterns of cannabis use. The goal of the present study was to develop and test the psychometric properties of a novel scale, the Positive Consequences of Cannabis scale (PCOC) to assess positive outcomes of cannabis use among current adult cannabis users.

Methods

Participants (n = 768) were recruited through online platforms. The sample was predominantly non-Hispanic (92.3 %) male (62.92 %) with an average age of 29.08 years (SD = 6.10). A split half validation method was used to assess the factor structure of the PCOC scale. Data analysis also included Exploratory factor analysis (EFA) to identify underlying factor structures of the PCOC, confirmatory factor analysis (CFA) to validate the factor structure, and the assessments of internal consistency and validity.

Results

The EFA identified a two-factor solution for the PCOC: Social and Psychological Consequences and Cognitive and Motivational Consequences. The CFA confirmed the validity of this factor structure with good model fit (χ2 = 321.33, p < 0.001; CFI = 0.95; TLI = 0.95; RMSEA = 0.038; SRMR = 0.048). Internal consistency coefficients for the PCOC subscales and total scale exceeded acceptable thresholds. A hierarchical regression model showed that both PCOC subscales were significantly associated with cannabis use frequency and quantity.

Discussion

The development and validation of the PCOC represent a significant advancement in assessing positive consequences in understanding cannabis use patterns, indicating that individuals who experience a range of positive effects are more likely to engage in more frequent and intense cannabis use.

中文翻译：

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大麻积极影响（PCOC）量表的开发和初步验证

介绍

虽然对大麻使用的负面影响存在广泛的研究，但关于大麻使用模式的积极影响的文献中存在明显的空白。本研究的目的是开发和测试一种新型量表的心理测量特性，即大麻的积极后果量表（PCOC），以评估当前成年大麻使用者使用大麻的积极结果。

方法

参与者（n = 768）是通过在线平台招募的。样本主要是非西班牙裔 (92.3%) 男性 (62.92%)，平均年龄为 29.08 岁 (SD = 6.10)。使用分半验证方法评估 PCOC 量表的因子结构。数据分析还包括探索性因素分析（EFA）以识别PCOC的潜在因素结构、验证性因素分析（CFA）以验证因素结构以及内部一致性和有效性的评估。

结果

EFA 为 PCOC 确定了一个双因素解决方案：社会和心理后果以及认知和动机后果。CFA 通过良好的模型拟合证实了该因子结构的有效性（χ2 = 321.33，p < 0.001；CFI = 0.95；TLI = 0.95；RMSEA = 0.038；SRMR = 0.048）。PCOC 子量表和总量表的内部一致性系数超出了可接受的阈值。分层回归模型显示，两个 PCOC 分量表均与大麻使用频率和数量显着相关。

讨论

PCOC 的制定和验证代表了在评估理解大麻使用模式的积极影响方面取得的重大进展，表明经历了一系列积极影响的个人更有可能更频繁和更强烈地使用大麻。