Research Article 82 Ved ABSTRACT Objective: Perseverative cognitive processes, such as rumination, may indirectly influence effects of personality traits on cannabis use and related problems. Understanding relations among personality, rumination, and cannabis use motives may lead to better understanding of problematic cannabis use. The present study examined personality traits’ influence on negative cannabis-related consequences via rumination and cannabis use coping motives. Methods: We tested a sequential path model across two independent samples such that the model was tested in one sample and replicated in the second sample. Participants were U.S. undergraduate students from multiple universities who reported using cannabis at least once in the prior thirty days. Results: Results partially supported hypotheses such negative urgency and distress tolerance were indirectly related to negative cannabis-related consequences via rumination and coping motives. Specifically, higher negative urgency and lower distress tolerance were related to higher rumination. Higher rumination was related to higher coping motives; which in turn was related to more negative cannabis-related consequences. Results indicate that rumination is a risk factor belying associations between personality and cannabis use to cope and negative consequences of use. Conclusions: Implementing techniques that attenuate rumination for individuals high in negative urgency or low in distress tolerance may reduce or prevent problematic cannabis and unintended outcomes. Key words: = sensation seeking; impulsivity; distress tolerance; emotion dysregulation; negative urgency Cannabis use rates continue to rise in the United States (Center for Behavioral Health Statistics and Quality [SAHMSA], 2015; Hasin et al., 2015, Mauro et al., 2018). Additionally, higher cannabis potencies have paralleled the increase in use frequency (ElSohly et al., 2016), which amplifies the risk of experiencing harmful outcomes (Brook et al., 2008; Hasin et al., 2015). Individuals who use cannabis are also more likely to experience downward social mobility and increased financial problems, as well as engage in more disruptive work-based behaviors (Cerdá et al., 2016; Trudeau et al., 2015). Those most at-risk for long-term cognitive impairment and negative Bradley T. Conner1, Adrian J. Bravo2, Naomi Win3, Ryan L. Rahm- Knigge1, Cross-Cultural Addictions Study Team**, Stimulant Norms and Prevalence (SNAP) Study Team*** 1Department of Psychology, Colorado State University 2Department of Psychological Sciences, William & Mary 3Department of Education and Human Development, University of Colorado, Denver Cannabis 2024, Volume 7 (1) © Author(s) 2024 researchmj.org 10.26828/cannabis/2024/000172 Examination of Rumination’s Mediating Role in the Relation Between Distal Personality Predictors, Cannabis Coping Motives, and Negative Cannabis-Related Consequences Corresponding Author: Bradley Conner, PhD, Colorado State University, 1876 Campus Mail, Fort Collins, Colorado, 80523. Phone: (970) 491-6197. Email: brad.conner@colostate.edu. Cannabis, A Publication of the Research Society on Marijuana 83 use consequences are youth (Jacobus & Tapert, 2015; Shrivastava et al., 2015) and emerging adults (SAHMSA, 2015; Wisk & Weitzman, 2016), particularly those enrolled in college (Meich et al., 2017). Given increases in prevalence, potency, and negative outcomes of cannabis use, identifying risk factors among adolescents and young adults is an important area of research and intervention. Personality Traits as Predictors of Use and Consequences Previous studies have established certain personality traits as distal antecedents of cannabis use and negative consequences of cannabis use (e.g., Dvorak & Day, 2014; Kentopp et al., 2019; Pearson et al., 2018). Such traits include sensation seeking, impulsivity, and emotion dysregulation. Sensation seeking is the desire for novel experiences and the willingness to take such risks (Conner, 2021). Impulsivity can be conceptualized via the UPPS-P five-factor model, which includes the factors of negative urgency (the tendency to act impulsively in response to negative emotions), lack of premeditation (acting without reflecting), lack of perseverance (not completing tasks), sensation seeking, and positive urgency (acting impulsively in response to positive emotions; Cyders et al., 2007; Whiteside & Lynam, 2001). Emotion dysregulation includes the inability to accept emotions, suppress emotion, problem-solve, redirect attention, or reappraise (Bonn-Miller et al., 2008). Related to emotion dysregulation is low distress tolerance, or a reduced coping threshold for negative emotions, which has been associated with coping motives for cannabis use (Semcho et al., 2016). Overall, these traits have robust relationships with increased cannabis use and undesirable use consequences (Brook, et al., 2016; Conner, 2021; Hayaki et al., 2011; Neugebauer et al., 2019; Pearson et al., 2018; Rinehart & Spencer, 2021; VanderVeen et al., 2016). Further, negative consequences of use often exacerbate cannabis use (Day et al., 2013; Martin-Santos et al., 2017). In addition to these relationships, previous research highlights the importance of considering differential pathways between personality traits and cannabis use and unintended outcomes of use. For example, one study distinguished between traits comprising behavioral self-regulation, such as sensation seeking and self-control, and emotion self-regulation, including distress tolerance and emotional instability (Dvorak & Day, 2014). Behavioral self-regulation was associated with increased cannabis use, while emotional self- regulation and urgency were associated with increased cannabis use problems. Because of the differential paths from personality traits to cannabis use and unintended outcomes of use, further exploration of distinct variables that influence (i.e., mediate) these relationships may inform clinical invention (Dvorak & Day, 2014). Rumination Perseverative cognitive processes, such as rumination, may influence the effects of personality traits on cannabis use and related problems. Response Styles Theory defines rumination as a preoccupation on symptoms of distress that interferes with solving the problem causing the distress (Nolen-Hoeksema, 2012). Rumination not only fails to down-regulate, but actively prolongs and exacerbates the experiencing of the negative emotion. It is the tendency to focus repetitively on the symptoms of emotional stress, as well as the potential meaning, causes, and consequences of the symptoms, without solving the contributing problems (i.e., it is a focus on the problem, as opposed to solutions; Nolen-Hoeksema & Jackson, 2001). Multiple theories postulate rumination to comprise mechanisms of brooding, reflection, and emotional self-awareness (Johnson & Whisman, 2013; Nolen-Hoeksema, 2012). In support of negative affect models (e.g., Baker et al., 2004), rumination mediates relations between negative affect and cannabis motives and consequences (Bravo et al., 2019). However, research examining the mediating role of rumination in linking personality traits to cannabis motives and outcomes is limited. Personality and Rumination While research has established links between personality and cannabis consequences (e.g., Dvorak & Day, 2014; Kentopp et al., 2019; Pearson et al., 2018), the pathway is not expressly understood, and, given that personality is difficult to change (Wagner et al., 2020), this information does not inform effective interventions to stop individuals from experiencing these Personality, Rumination, Coping Motives, and Marijuana Problems 84 consequences. Personality first forms during childhood, solidifies in adolescence and young adulthood, and typically remains stable across the rest of the lifespan. Thus, personality is a fairly static variable that provides boundaries for potential behavioral responses (Robinson et al., 2019). Cognitive processes, such as rumination, likely play a role in determining how personality traits influence behavior in the moment. Having a better understanding of the mechanisms through which specific personality traits influence motives, behavior and outcomes will identify leverage points for intervention to disrupt the link between personality and consequences. In other words, it is quite difficult to change personality. So, if intermediate steps that can be addressed, in this case rumination, can be identified, then effective interventions to lower the probability of experiencing negative health outcomes, such as negative consequences from cannabis use, can be identified and changed. For instance, individuals who score high on impulsivity due to their inability to tolerate negative affect (i.e., negative urgency) and who also tend to ruminate may use cannabis as a coping motive to stop ruminating, and thus may increase their chances of experiencing negative cannabis consequences. Present Study The present study examined personality traits’ influence on negative cannabis-related consequences via rumination and cannabis use coping motives. Specifically, we examined a sequential mediation model such that personality factors (i.e., impulsivity, sensation seeking, distress tolerance, and emotion regulation facets) would associate with rumination. In turn, higher rumination would be associated with higher endorsement of cannabis coping motives, which would be associated with more negative cannabis- related consequences. Given that the field of psychology is currently undergoing a rather strong indictment regarding effects that are not reproducible (e.g., Simmons et al., 2011), we examined the proposed comprehensive model across two independent samples of college students (Project CMS, Project SNAP). Specifically, we first tested the comprehensive model in the Project CMS sample and based on results of the model, we then trimmed the model (i.e., removed non-significant direct effects [but kept those variables in as covariates]) and examined if significant results replicated within the Project SNAP sample (as well as tested for model fit). METHODS Participants/Procedures Project CMS Sample The participant sample for this present study was comprised of college students from eight universities across five countries (the U.S., Spain, Argentina, Uruguay, and the Netherlands). Participants completed an online survey exploring risk and protective factors of cannabis use and subsequent outcomes (for more information, see Bravo et al., 2019). Due to the design of the parent study, several constructs (i.e., distress tolerance, emotion regulation) assessed in the present study were only collected at the U.S. institutions. Given the aims of the present study, the analytic sample was limited to 698 students across multiple U.S. universities located in four states (Colorado, New Mexico, New York, Virginia) who reported using cannabis at least once in the past 30 days. The majority of participants identified as being non- Hispanic White (60.2%), female (64.5%), freshman (53.9%) and reported a mean age of 19.53 (Median = 19.00; SD = 2.72) years. Study procedures were approved by the institutional review boards for each participating university. Project SNAP Sample Participants were college students recruited to participate in an online survey (standardized across sites) from psychology department research participant pools at seven universities across six U.S. states (Colorado, New Mexico, New York, Virginia [2 sites], Texas, and Wyoming) between Fall 2019 and Spring 2020 (for more information, see Looby et al., 2021). Given the aims of the present study, the analytic sample was limited to 1,447 students who reported using cannabis at least once in the past 30 days. The majority of participants identified as being non- Hispanic White (47.6%), female (69.7%), freshman (48.6%) and reported a mean age of 19.61 (Median = 19.00; SD = 2.55) years. This study was Cannabis, A Publication of the Research Society on Marijuana 85 conducted after receiving single-site IRB approval. Measures For all measures (unless specified), composite scores were created by first reverse-coding items when appropriate such that higher scores indicate higher levels of the construct and then averaging across items. All measures (except for coping cannabis motives) were assessed in both Project CMS and Project SNAP samples. Distress Tolerance Distress tolerance was assessed using the 15- item Distress Tolerance Scale (Simons & Gaher, 2005). The items measure participants’ expectations and evaluations of negative emotional states along four dimensions that constitute the meta-emotion construct of distress tolerance, namely: tolerance, appraisal, absorption, and regulation of negative emotional states. Participants respond to items using a 5- point Likert response scale (1 = Strongly agree, 5 = Strongly disagree). The total score was found to be internally consistent across both samples (Project CMS Sample, α = .94; Project SNAP sample, α = .93). Impulsivity Positive urgency, negative urgency, premeditation, and perseverance were assessed as facets of impulsivity, using the 20-item Short UPPS-P Impulsive Behavior Scale (Cyders et al. 2014). Participants respond to items using a 4- point Likert response scale (1 = Agree strongly, 2 = Agree some, 3 = Disagree some, and 4 = Disagree strongly). Reliability for the current study was excellent: Positive urgency (Project CMS Sample, α = .89; Project SNAP sample, α = .89), negative urgency (Project CMS Sample, α = .86; Project SNAP sample, α = .82), premeditation (Project CMS Sample, α = .90; Project SNAP sample, α = .88), and perseverance (Project CMS Sample, α = .82; Project SNAP sample, α = .83). Note that a separate scale was used to assess sensation seeking, so the sensation seeking subscale of the SUPPS-P was not used in the present study. Sensation Seeking The Sensation Seeking Personality Trait Scale (Conner, 2021) was used to assess experience seeking (the desire for novel experiences) and risk seeking (the willingness to take risks for those experiences). Sample items from the experience seeking subscale include: “I think it is important to try as many new things as I can” and “I like to experience anything and everything I can,” whereas sample items from the risk seeking subscale include: “I think that excitement is more important than safety” and “I enjoy participating in unsafe activities.” Experience seeking (Project CMS Sample, α = .83; Project SNAP sample, α = .80) and risk seeking (Project CMS Sample, α = .86; Project SNAP sample, α = .80) exhibited good internal consistency in the present study. Emotion Regulation Emotion regulation was assessed using the 10- item Emotion Regulation Questionnaire (Gross & John, 2003), a self-report measure assessing use of cognitive reappraisal and expressive suppression as emotion regulatory strategies. Reliability for the current study was acceptable- excellent: Cognitive Reappraisal (Project CMS Sample, α = .92; Project SNAP sample, α = .91) and Emotional Suppression (Project CMS Sample, α = .76; Project SNAP sample, α = .73). Rumination Rumination was assessed using the Ruminative Thought Style Questionnaire (RTSQ; Brinker & Dozois, 2009). This measure assesses participants’ overall tendency toward ruminative thinking via self-report. It comprises 20-items and uses a 7-point response scale (1 = Not at all, 7 = Very Well). Reliability for the current study was excellent: Project CMS Sample, (α = .95); Project SNAP sample, (α = .95). Cannabis Coping Motives In Project CMS, the Marijuana Motives Measure Short Form (MMM-SF, Simons et al, 1998) was used to assess coping cannabis motives (α = .89). In Project SNAP, the Comprehensive Marijuana Motives Questionnaire (Lee et al., Personality, Rumination, Coping Motives, and Marijuana Problems 86 2009) was used to assess cannabis coping motives (α = .84). Cannabis Use and Consequences Typical cannabis use frequency and quantity (covariates in our models) were assessed using the Marijuana Use Grid (Pearson & Marijuana Outcomes Study Team, 2021). Specifically, each day of the week was broken into six 4-hour blocks of time (12a-4a, 4a-8a, 8a-12p, etc.), and participants were asked to report at which times they used cannabis during a “typical week” in the past 30 days, as well as the quantity of grams consumed during that time block. We calculated typical frequency of cannabis use by summing the total number of time blocks for which they reported using during the typical week (ranges: 0- 42). We calculated typical quantity of cannabis use by summing the total number of grams consumed across time blocks during the typical week (quantity estimates >3 SDs above the mean were Winsorized). Negative cannabis-related consequences were assessed using the 21-item Brief Marijuana Consequences Questionnaire (Simons et al., 2012). Answers to specific items are summed across facets for a single consequence score. Reliability for the current study was as follows: Project CMS Sample, (α = .87); Project SNAP sample, (α = .89). Statistical Analyses To test the study aims, a fully saturated path model (see Figure 1) in which personality variables were modeled as predictors of negative cannabis-related consequences via rumination and cannabis coping motives was estimated using Mplus 8.3 (Muthén & Muthén, 1998-2017) within the CMS sample. Figure 1. Significant standardized direct effects of the comprehensive mediation model in Project CMS sample. Note. The covariances among distal antecedents and effects of covariates (i.e., marijuana use frequency, marijuana use quantity, social motives, enhancement motives, conformity motives, and expansion motives) are not depicted for parsimony but are available upon request. Significant associations were determined by a 99% bias-corrected standardized bootstrapped confidence interval (based on 10,000 bootstrapped samples) that does not contain zero. Cannabis, A Publication of the Research Society on Marijuana 87 Within this model, cannabis use frequency, cannabis use quantity, social motives, enhancement motives, conformity motives, and expansion motives were entered as covariates. Based on results of the model tested in Project CMS sample, we then trimmed the model (i.e., removed non-significant direct effects but kept variables as covariates) and examined if significant results replicated within the Project SNAP sample (as well as tested for model fit to determine if the trimmed model was adequate) using Mplus 8.3 (Muthén & Muthén, 1998-2017). For both models, missing data were handled using full information maximum likelihood (Muthén & Muthén, 1998-2017). We examined the total, direct, and indirect effects using bias- corrected bootstrapped estimates (Efron & Tibshirani, 1993), which provides a powerful test of mediation (Fritz & MacKinnon, 2007) and is robust to small departures from normality (Erceg- Hurn & Mirosevich, 2008). Statistical significance was determined by 99% bias-corrected bootstrapped confidence intervals not containing zero in both models. RESULTS Comprehensive Mediation Model in Project CMS Bivariate correlations and descriptive statistics of study variables in Project CMS are presented in Table 1. Table 1. Bivariate correlations of variables in the mediation model in Project CMS sample. Note. Significant correlations are bolded and were determined by a 99% bias-corrected standardized bootstrapped confidence interval (based on 10,000 bootstrapped samples) that does not contain zero. ER = Emotion Regulation. Correlations with covariates (i.e., marijuana use frequency, marijuana use quantity, social motives, enhancement motives, conformity motives, and expansion motives) are available upon request. The total, total indirect, specific indirect, and direct effects of the comprehensive mediation model are summarized in Table 2 and Figure 1. Within the model, only distress tolerance (negative association) and negative urgency (positive association) were significantly associated with rumination after controlling for effects of other personality predictors and covariates. Several personality variables and rumination were significantly directly associated with cannabis coping motives: distress tolerance (negative association), negative urgency (positive association), risk seeking (negative association), emotion regulation - suppression facet (positive association), and rumination (positive association). Negative urgency (positive association) and cannabis coping motives (positive association) were the only variables significantly associated with negative cannabis-related consequences after controlling for effects of all other variables. 1 2 3 4 5 6 7 8 9 10 11 12 M SD 1. Distress Tolerance --- 3.22 0.81 2. Negative Urgency -.35 --- 2.09 0.78 3. Positive Urgency -.22 .54 --- 1.88 0.75 4. Perseverance -.05 .08 -.05 --- 3.08 0.66 5. Premeditation .06 -.22 -.20 .41 --- 3.12 0.73 6. Risk Seeking .04 .14 .34 -.06 -.28 --- 2.89 0.72 7. Experience Seeking .21 -.15 -.05 .20 .06 .42 --- 3.58 0.58 8. ER – Reappraisal .27 -.18 -.11 .14 .19 -.07 .26 --- 4.70 1.12 9. ER – Suppression -.13 .12 .20 -.03 -.05 .10 .00 .26 --- 4.11 1.26 10. Rumination -.33 .37 .15 .10 -.11 .00 -.04 -.04 .12 --- 4.11 1.30 11. Coping Marijuana Motives -.25 .32 .16 -.03 -.12 -.02 -.12 -.05 .17 .28 --- 2.29 1.24 12. Marijuana Consequences -.11 .22 .09 -.02 -.12 .07 -.06 -.02 .01 .14 .28 --- 3.51 4.01 Personality, Rumination, Coping Motives, and Marijuana Problems 88 Table 2. Summary of total, indirect, and direct effects of distal antecedences, rumination, and marijuana coping motives on negative marijuana-related consequences in a comprehensive mediation model in Project CMS sample. Outcome Variables Rumination Coping Marijuana Motives Negative Marijuana- related Consequences Predictor Variable: Distress Tolerance β 95% CI β 95% CI β 95% CI Total -.245 -0.36, -0.13 -.136 -0.23, -0.05 -.056 -0.16, 0.04 Total indirecta --- --- -.039 -0.07, -0.02 -.036 -0.08, 0.000 Specific indirect: Rumination --- --- -.039 -0.07, -0.02 -.015 -0.05, 0.02 Coping Marijuana Motives --- --- --- --- -.015 -0.04, -0.001 Rumination à Coping Marijuana Motives --- --- --- --- -.006 -0.02, -0.002 Direct -.245 -0.36, -0.13 -.097 -0.19, - 0.001 -.020 -0.13, 0.08 Predictor Variable: Negative Urgency β 95% CI β 95% CI β 95% CI Total .307 0.19, 0.43 .230 0.12, 0.34 .210 0.10, 0.32 Total indirecta --- --- .049 0.02, 0.09 .054 0.01, 0.11 Specific indirect: Rumination --- --- .049 0.02, 0.09 .019 -0.02, 0.06 Coping Marijuana Motives --- --- --- --- .028 0.01, 0.06 Rumination à Coping Marijuana Motives --- --- --- --- .008 0.002, 0.02 Direct .307 0.19, 0.43 .181 0.08, 0.29 .156 0.04, 0.28 Predictor Variable: Positive Urgency β 95% CI β 95% CI β 95% CI Total -.060 -0.20, 0.07 -.026 -0.13, 0.08 -.053 -0.17, 0.07 Total indirecta --- --- -.010 -0.04, 0.01 -.008 -0.03, 0.01 Specific indirect: Rumination --- --- -.010 -0.04, 0.01 -.004 -0.03, 0.004 Coping Marijuana Motives --- --- --- --- -.002 -0.02, 0.02 Rumination à Coping Marijuana Motives --- --- --- --- -.001 -0.01, 0.001 Direct -.060 -0.20, 0.07 -.016 -0.12, 0.09 -.045 -0.17, 0.07 Predictor Variable: Perseverance β 95% CI β 95% CI β 95% CI Total .074 -0.04, 0.19 .005 -0.09, 0.09 .033 -0.07, 0.13 Total indirecta --- --- .012 -0.01, 0.04 .005 -0.01, 0.03 Specific indirect: Rumination --- --- .012 -0.01, 0.04 .005 -0.004, 0.03 Coping Marijuana Motives --- --- --- --- -.001 -0.02, 0.01 Rumination à Coping Marijuana Motives --- --- --- --- .002 -0.001, 0.01 Direct .074 -0.04, 0.19 -.007 -0.10, 0.08 .027 -0.07, 0.13 Predictor Variable: Premeditation β 95% CI β 95% CI β 95% CI Total -.110 -0.23, 0.01 -.051 -0.14, 0.04 -.039 -0.15, 0.07 Total indirecta --- --- -.018 -0.05, 0.001 -.015 -0.05, 0.004 Specific indirect: Rumination --- --- -.018 -0.05, 0.001 -.007 -0.03, 0.01 Coping Marijuana Motives --- --- --- --- -.005 -0.03, 0.01 Rumination à Coping Marijuana Motives --- --- --- --- -.003 -0.01, 0.000 Direct -.110 -0.23, 0.01 -.034 -0.13, 0.06 -.024 -0.13, 0.08 Cannabis, A Publication of the Research Society on Marijuana 89 Predictor Variable: Risk Seeking β 95% CI β 95% CI β 95% CI Total -.053 -0.17, 0.06 -.126 -0.24, -0.01 .052 -0.06, 0.16 Total indirecta --- --- -.009 -0.03, 0.01 -.023 -0.06, -0.002 Specific indirect: Rumination --- --- -.009 -0.03, 0.01 -.003 -0.02, 0.004 Coping Marijuana Motives --- --- --- --- -.018 -0.05, -0.002 Rumination à Coping Marijuana Motives --- --- --- --- -.001 -0.01, 0.001 Direct -.053 -0.17, 0.06 -.117 -0.23, -0.01 .075 -0.04, 0.19 Predictor Variable: Experience Seeking β 95% CI β 95% CI β 95% CI Total .044 -0.07, 0.17 .013 -0.09, 0.13 -.042 -0.15, 0.08 Total indirecta --- --- .007 -0.01, 0.03 .005 -0.02, 0.03 Specific indirect: Rumination --- --- .007 -0.01, 0.03 .003 -0.004, 0.02 Coping Marijuana Motives --- --- --- --- .001 -0.02, 0.02 Rumination à Coping Marijuana Motives --- --- --- --- .001 -0.002, 0.01 Direct .044 -0.07, 0.17 .006 -0.10, 0.12 -.046 -0.16, 0.07 Predictor Variable: ERQ – Reappraisal β 95% CI β 95% CI β 95% CI Total .048 -0.07, 0.17 -.030 -0.12, 0.07 .064 -0.04, 0.16 Total indirecta --- --- .008 -0.01, 0.03 -.002 -0.02, 0.02 Specific indirect: Rumination --- --- .008 -0.01, 0.03 .003 -0.004, 0.02 Coping Marijuana Motives --- --- --- --- -.006 -0.03, 0.01 Rumination à Coping Marijuana Motives --- --- --- --- .001 -0.002, 0.01 Direct .048 -0.07, 0.17 -.038 -0.13, 0.06 .066 -0.04, 0.16 Predictor Variable: ERQ – Suppression β 95% CI β 95% CI β 95% CI Total .053 -0.05, 0.17 .115 0.03, 0.20 -.060 -0.16, 0.03 Total indirecta --- --- .008 -0.01, 0.03 .021 0.004, 0.05 Specific indirect: Rumination --- --- .008 -0.01, 0.03 .003 -0.004, 0.02 Coping Marijuana Motives --- --- --- --- .016 0.003, 0.04 Rumination à Coping Marijuana Motives --- --- --- --- .001 -0.001, 0.01 Direct .053 -0.05, 0.17 .107 0.02, 0.20 -.081 -0.18, 0.01 Predictor Variable: Rumination β 95% CI β 95% CI β 95% CI Total --- --- .160 0.07, 0.25 .085 -0.04, 0.20 Indirect effect via Coping Marijuana Motives --- --- --- --- .025 0.01, 0.05 Direct --- --- .160 0.07, 0.25 .061 -0.06, 0.18 Note. Significant associations are in bold typeface for emphasis and were determined by a 99% bias-corrected standardized bootstrapped confidence interval (based on 10,000 bootstrapped samples) that does not contain zero. a Reflects the combined indirect associations within the model. Within the model, Coping Marijuana Motives was significantly positively associated with Negative Marijuana-related Consequences (β = .15). Effects of covariates (i.e., marijuana use frequency, marijuana use quantity, social motives, enhancement motives, conformity motives, and expansion motives) are available upon request. Personality, Rumination, Coping Motives, and Marijuana Problems 90 As expected based on the direct effects, only negative urgency and distress tolerance were indirectly related to negative cannabis-related consequences via rumination and coping motives. Specifically, higher negative urgency and lower distress tolerance were related to higher rumination. Higher rumination was in turn related to higher coping motives, which in turn was related to more negative cannabis-related consequences. It is important to note that cannabis coping motives uniquely statistically significantly mediated the associations between both risk seeking and distress tolerance and negative cannabis-related consequences (both negative indirect effects), as well as between both negative urgency and emotion regulation (suppression facet) and negative cannabis-related consequences (both positive indirect effects). Replication Mediation Model in Project SNAP Bivariate correlations and descriptive statistics of study variables in Project SNAP are presented in Table 3. Table 3. Bivariate correlations of variables in the mediation model in Project SNAP sample. Note. Significant correlations are bolded and were determined by a 99% bias-corrected standardized bootstrapped confidence interval (based on 10,000 bootstrapped samples) that does not contain zero. ER = Emotion Regulation. Correlations with covariates (i.e., marijuana use frequency and marijuana use quantity) are available upon request. Based on the results of the model tested in the Project CMS sample, we trimmed the model for Project SNAP such that only significant direct effects found in Project CMS (see Figure 1) were entered as predictors of the mediation effects (all other variables were entered as covariates) in the replication model. It is important to note that other motives were assessed but not included in the replication mediation model, given discrepancies across cannabis motives measures. The replication mediation model provided an acceptable fit to the data based on most fit indices (Hu & Bentler, 1999), CFI=.987, RMSEA=.045, 90% CI [.026, .064], SRMR=.019. The total, total indirect, specific indirect, and direct effects of the replication mediation model in Project SNAP are summarized in Table 4 and Figure 2. 1 2 3 4 5 6 7 8 9 10 11 12 M SD 1. Distress Tolerance --- 3.10 0.83 2. Negative Urgency -.44 --- 2.24 0.77 3. Positive Urgency -.25 .45 --- 2.04 0.79 4. Perseverance -.07 .10 -.05 --- 3.04 0.67 5. Premeditation -.01 -.16 -.11 .53 --- 3.11 0.63 6. Risk Seeking -.01 .17 .40 -.13 -.24 --- 2.82 0.66 7. Experience Seeking .12 -.15 -.03 .17 .10 .38 --- 3.45 0.58 8. ER – Reappraisal .33 -.34 -.14 .17 .24 -.02 .30 --- 4.55 1.26 9. ER – Suppression .01 -.02 .04 -.05 -.00 .03 .01 .28 --- 4.05 1.34 10. Rumination -.26 .25 -.00 .15 -.06 .06 .14 .05 .24 --- 4.53 1.28 11. Coping Marijuana Motives -.31 .29 .22 -.05 -.13 .18 -.04 -.11 .18 .24 --- 2.29 1.19 12. Marijuana Consequences -.14 .19 .18 -.07 -.14 .17 -.05 -.02 .08 .13 .40 --- 4.27 4.59 Cannabis, A Publication of the Research Society on Marijuana 91 Table 4. Summary of total, indirect, and direct effects of distal antecedences, rumination, and marijuana coping motives on negative marijuana-related consequences in replication mediation model in Project SNAP sample. Outcome Variables Rumination Coping Marijuana Motives Negative Marijuana-related Consequences Predictor Variable: Distress Tolerance β 95% CI β 95% CI β 95% CI Total -.278 -0.49, -0.05 -.234 -0.35, -0.11 --- --- Total indirecta --- --- -.041 -0.10, -0.004 -.067 -0.11, -0.03 Specific indirect: Rumination --- --- -.041 -0.10, 0.004 --- --- Coping Marijuana Motives --- --- --- --- -.056 -0.10, -0.02 Rumination à Coping Marijuana Motives --- --- --- --- -.012 -0.03, -0.001 Direct -.278 -0.49, -0.05 -.193 -0.32, -0.05 --- --- Predictor Variable: Negative Urgency β 95% CI β 95% CI β 95% CI Total .263 0.03, 0.49 .120 -0.01, 0.24 .090 -0.03, 0.21 Total indirecta --- --- .039 0.002, 0.11 .034 -0.003, 0.07 Specific indirect: Rumination --- --- .039 0.002, 0.11 --- --- Coping Marijuana Motives --- --- --- --- .023 -0.02, 0.07 Rumination à Coping Marijuana Motives --- --- --- --- .011 0.001, 0.03 Direct .263 0.03, 0.49 .081 -0.06, 0.22 .055 -0.06, 0.17 Predictor Variable: Risk Seeking β 95% CI β 95% CI β 95% CI Total --- --- .121 -0.03, 0.26 --- --- Total indirecta --- --- --- --- --- --- Specific indirect: --- --- --- --- Rumination --- --- --- --- --- --- Coping Marijuana Motives --- --- --- --- .035 -0.01, 0.08 Rumination à Coping Marijuana Motives --- --- --- --- --- --- Direct --- --- .121 -0.03, 0.26 --- --- Predictor Variable: ERQ – Suppression β 95% CI β 95% CI β 95% CI Total --- --- .143 0.04, 0.25 --- --- Total indirecta --- --- --- --- --- --- Specific indirect: --- --- --- --- Rumination --- --- --- --- --- --- Coping Marijuana Motives --- --- --- --- .041 0.01, 0.07 Rumination à Coping Marijuana Motives --- --- --- --- --- --- Direct --- --- .143 0.04, 0.25 --- --- Predictor Variable: Rumination β 95% CI β 95% CI β 95% CI Total --- --- .148 0.01, 0.27 --- --- Indirect effect via Coping Marijuana Motives --- --- --- --- .043 0.002, 0.09 Direct --- --- .148 0.01, 0.27 --- --- Note. Significant associations are in bold typeface for emphasis and were determined by a 99% bias-corrected standardized bootstrapped confidence interval (based on 10,000 bootstrapped samples) that does not contain zero. a Reflects the combined indirect associations within the model. Within the model, Coping Marijuana Motives was significantly positively associated with Negative Marijuana-related Consequences (β = .29). Effects of covariates (i.e., marijuana use frequency, marijuana use quantity, positive urgency, perseverance, premeditation, experience seeking, and emotion regulation - reappraisal) are available upon request. Personality, Rumination, Coping Motives, and Marijuana Problems 92 Figure 2. Standardized direct effects of the replication mediation model in Project SNAP sample. Note. Depicts the standardized direct effects of the replication mediation model in Project SNAP sample. The covariances among distal antecedents and effects of covariates (i.e., marijuana use frequency, marijuana use quantity, positive urgency, perseverance, premeditation, experience seeking, and emotion regulation - reappraisal) are not depicted for parsimony but are available upon request. Significant associations are in bold and were determined by a 99% bias-corrected standardized bootstrapped confidence interval (based on 10,000 bootstrapped samples) that does not contain zero. Findings in the replication mediation model in Project SNAP largely replicated findings from the comprehensive mediation model in Project CMS. Specifically, rumination was indirectly associated with more negative cannabis-related consequences via higher cannabis coping motives (even when using a different measure of cannabis coping motives). Regarding indirect effects of distress tolerance and negative urgency on negative cannabis-related consequences, findings were consistent with those found in Project CMS. Specifically, higher negative urgency and lower distress tolerance were associated with more negative cannabis-related consequences via higher rumination and higher coping motives. The significant indirect effects of negative urgency and risk seeking on negative cannabis-related consequences via cannabis coping motives did not replicate between Project CMS and Project SNAP. However, the indirect effects of emotion regulation (suppression facet) and low distress tolerance via cannabis coping motives did replicate across samples. DISCUSSION Past research indicates that cognitive processes (such as cannabis refusal self-efficacy, cognitive reappraisal of emotions, and premeditation) are strategies effectually moderating predictive associations between high- risk traits, coping use motives, and negative use consequences (Bonn-Miller at al., 2008; Brook et al., 2016.; Cerdá et al., 2016; Dvorak & Day, 2014; Kentopp at al., 2019; Pearson at al., 2018; Prosek et al., 2018; VanderVeen, 2016). Given this, we sought to further understand the potential effect of rumination (as a form of cognitive processing), potentially linking the associations between distal predictors, cannabis coping motives, and negative use consequences. Our results across two independent samples were consistent with our hypotheses, in that we found that rumination is a Cannabis, A Publication of the Research Society on Marijuana 93 risk factor belying associations between personality (particularly distress tolerance and negative urgency) and cannabis use to cope and negative consequences of use. A possible explanation for these results might lie in sense of engaged-avoidance caused by low distress tolerance and negative urgency. The inability to cope with negative emotional states and the likelihood of having a rash behavioral reaction simultaneously express a need to avoid and a need to engage. Cognitive and emotional processes that increase distress tolerance, reduce negative urgency, and are associated with reduction in substance use and use-related problems (Aldao et al., 2010; Cooper et al., 1988; Hayaki et al., 2011; Lynch, et al., 2007) require engagement with the problem at hand and appraisal of the distress it’s causing. Rumination, however, mimics the sense of engagement in this dynamic but redirects it towards distress, avoiding the problem. Circumventing the problem leads to a positive feedback loop of engaged- avoidance, where the problem is not reduced and distress from the problem is exacerbated. This redirection away from the problem towards fixation on the distress may act in tension with the need to alleviate the distress, which may lead individuals to seek alternative (maladaptive) coping strategies such as using cannabis. Clinical Implications Results of the current study imply that interventive techniques targeted to disrupt ruminative mechanisms in individuals with increased negative urgency and lower distress tolerance may disrupt pathways to negative cannabis use consequences via decreasing use of cannabis to cope. Put conversely, the implication is that individuals with low distress tolerance and higher negative urgency are more likely to engage in rumination, which encourages the likelihood of using cannabis to cope with ruminative thoughts and, in turn, experience negative consequences from use. Research on alcohol use suggests personality-targeting interventions can manage high-risk traits with regard to drinking-to cope (Conrod et al., 2006), but this line of thinking has been less documented with regard to cannabis use. Although preliminary, our results support the empirical pursuit of interventions targeting high-risk trait management as a disruption of pathways leading to negative cannabis use consequences. Specifically, our results suggest that individuals, screened for low distress tolerance and higher negative urgency, may benefit from interventions designed to replace rumination with cognitive processes such as reappraisal, refusal self-efficacy, and premeditation. Due to the preliminary nature of this study, rumination was considered as a single-factor construct in order to retain focused scope. Further empirical work examining its mediating role in associations with cannabis use and use-related outcomes might consider examining rumination as a multidimensional construct. It’s been suggested that different kinds of rumination (e.g., angry rumination vs. depressed rumination) have a role in which of the aforementioned facets are most engaged with (Ciesla et al., 2011). Further research examining facets of rumination as mediators of associations between cannabis use motives and negative use consequences may further refine data informing the design of interventions aimed to reduce negative cannabis use consequences. Limitations A limitation of this study is the potential for recall bias in the self-report measures used, due to them being retrospective in nature. Further empirical work might benefit from using ecological momentary assessments in order to reduce this bias and provide more insight into any temporal ordering that might be present in the studied associations. Relatedly, the use of the cross-sectional survey design in our study means we’re unable to demonstrate temporal precedence with regard to mediation of associations, and therefore we cannot make causal inferences. Lastly, the present study’s use of convenience samples may also limit the generalizability of the present study’s findings. Conclusions The rise in cannabis use and use-related problems are positively correlated, with the differentiated pathways between use motive variables and negative use consequences impacted by antecedent personality traits and temperament factors. Given that cognitive Personality, Rumination, Coping Motives, and Marijuana Problems 94 processes (e.g., reappraisal, premeditation) interrupt associations between multiple trait factors and cannabis use-related outcomes, we sought to better understand the role of rumination, a perseverative cognitive coping process, in mediating these associations. Our multidimensional approach yielded results indicating that to no small effect, rumination plays a role in influencing an individual’s use of cannabis to cope and subsequent experiences of negative use consequences, especially among those high in negative urgency and low in distress tolerance. We therefore conclude that rumination is a mechanism catalyzing some high-risk distal predictors of use towards negative use consequences, via higher use of cannabis to cope. Thus, interventions designed to decouple rumination from distal factors contributing to negative emotional states (i.e., distress tolerance and negative urgency) implicates reduction in negative use consequences via lower use of cannabis to cope. REFERENCES Aldao, A., Nolen-Hoeksema, S., & Schweizer, S. (2010). Emotion-regulation strategies across psychopathology: A meta-analytic review. Clinical Psychology Review, 30, 217-37. https://doi.org/10.1016/j.cpr.2009.11.004 Baker, T. B., Piper, M. E., McCarthy, D. E., Majeskie, M. R., & Fiore, M. C. (2004). Addiction motivation reformulated: an affective processing model of negative reinforcement. Psychological Review, 111, 33- 51. https://doi.org/10.1037/0033- 295X.111.1.33 Bonn-Miller, M. O., Vujanovic, A. A., and Zvolensky, M. J. (2008). Emotional Dysregulation: Association with coping- oriented marijuana use motives among current marijuana users. Substance Use & Misuse, 43, 1653-1665. https://doi.org/10.1080/10826080802241292 Bravo, A. J., Pearson, M. R., Pilatti, A., Mezquita, L., & Cross-Cultural Addictions Study Team. (2019). Negative marijuana-related consequences among college students in five countries: Measurement invariance of the Brief Marijuana Consequences Questionnaire. Addiction, 114, 1854-1865. https://doi.org/10.1111/add.14646 Bravo, A. J., Sotelo, M., Pilatti, A., Mezquita, L., Read, J. P., & Cross-Cultural Addictions Study Team (2019). Depressive symptoms, ruminative thinking, marijuana use motives, and marijuana outcomes: A multiple mediation model among college students in five countries. Drug and Alcohol Dependence, 204, 107558. https://doi.org/10.1016/j.drugalcdep.2019.107 558 Brinker, J. K., & Dozois, D. J. (2009). Ruminative thought style and depressed mood. Journal of Clinical Psychology, 65, 1-19. https://doi.org/10.1002/jclp.20542 Brook, J. S., Stimmel, M. A., Zhang, C., & Brook, D. W. (2008). The association between earlier marijuana use and subsequent academic achievement and health problems: A longitudinal study. The American Journal on Addictions, 17(2), 155-60. https://doi.org/10.1080/10550490701860930 Brook, J. S., Zhang, C., Leukefeld, C. H., & Brook, D. W. (2016) Marijuana use from adolescence to adulthood: developmental trajectories and their outcomes. Social Psychiatry and Psychiatric Epidemiology, 51(10), 1405-1415. https://doi.org/10.1007/s00127-016-1229-0 Center for Behavioral Health Statistics and Quality. National Survey on Drug Use and Health. (2016). 2015 National survey on drug use and health. Substance Abuse and Mental Health Services Administration. https://www.samhsa.gov/data/data-we- collect/nsduh-national-survey-drug-use-and- health Cerdá, M., Moffitt, T. E., Meier, M. H., Harrington, H., Houts, R., Ramrakha, S., Hogan, S., Poulton, R., & Caspi, A. (2016). Persistent cannabis dependence and alcohol dependence represents risks for midlife economic and social problems: A longitudinal cohort study. Clinical Psychological Science: A Journal of the Association of Psychological Science, 4(6) 1028-1046. https://doi.org/10.1177/2167702616630958 Ciesla, J.A., Dickson, K.S., Anderson, N.L., & Neal, D.J. (2011). Negative repetitive thought and college drinking: Angry rumination, depressive rumination, co-rumination, and worry. Cognitive Therapy and Research, 35(2), 142 – 150. https://doi.org/10.1007/s10608-011- 9355-1 Cannabis, A Publication of the Research Society on Marijuana 95 Conner, B. T. (2021). Re-operationalizing sensation seeking through the development of the sensation seeking personality trait scale. Measurement and Evaluation in Counseling and Development, 1-16. https://doi.org/10.1080/07481756.2021.201865 8 Conrod, P.J., Steward, S.H., Comeau, N., & Maclean, A.M. (2006). Efficacy of cognitive- behavioral interventions targeting personality risk factors for youth alcohol misuse. Journal of Clinical Child & Adolescent Psychology, 35, 550-563. Cooper, M. L., Russell, M., & George, W. H. (1988). Coping, expectancies, and alcohol abuse: A test of social learning formulations. Journal of Abnormal Psychology, 97(2), 218-230. https://doi.org/10.1037/0021-843X.97.2.218 Cyders, M. A., Littlefield, A. K., Coffey, S., & Karyadi, K. A. (2014). Examination of a short English version of the UPPS-P Impulsive Behavior Scale Addictive Behaviors, 39, 1372- 1376. https://doi.org/10.1016/j.addbeh.2014.02.013 Cyders, M. A., Smith, G. T., Spillane, N. S., Fischer, S., Annus, A. M., & Peterson, C. (2007). Integration of impulsivity and positive mood to predict risky behavior: Development and validation of a measure of positive urgency. Psychological Assessment, 19(1), 107–118. https://doi.org/10.1037/1040- 3590.19.1.107 Day, A. M., Metrik, J., Spillane, N. S., & Kahler, C. W. (2013). Working memory and impulsivity predict marijuana-related problems among frequent users. Drug and Alcohol Dependence, 131(1-2), 171-174. https://doi.org/10.1016/j.drugalcdep.2012.12.0 16 Dvorak, R. D., & Day, A. M. (2014). Marijuana and self-regulation: Examining likelihood and intensity of use and problems. Addictive Behaviors, 39(3), 709-12. https://doi.org/10.1016/j.addbeh.2013.11.001 Efron, B. & Tibshirani, R. J. (1998). An Introduction to the Bootstrap. Boca Raton, Florida: CRC Press. Original work published 1993. ElSohly, M. A., Mehmedic, Z., Foster, S., Gon, C., Chandra, S., & Church, J. C. (2016). Changes in cannabis potency over the last two decades (1995-2015): Analysis of current data in the United States. Biological Psychiatry, 79(7), 613-619. https://doi.org/10.1016/j.biopsych.2016.01.004 Erceg-Hurn, D. M., & Mirosevich, V. M. (2008). Modern robust statistical methods: an easy way to maximize the accuracy and power of your research. The American Psychologist, 63, 591-601. https://doi.org/10.1037/0003- 066X.63.7.591 Fritz, M. S., & MacKinnon, D. P. (2007). Required sample size to detect mediated effect. Psychological Science, 18(3), 233-239. https://doi.org/10.1111/j.1467- 9280.2007.01882.x Gross, J. (2015). Handbook of Emotion Regulation (2nd ed). The Guilford Press. https://www.guilford.com/books/Handbook-of- Emotion-Regulation/Gross- Ford/9781462549412 Gross, J. J., & John, O. P. (2003). Individual differences in two emotion regulation processed: Implications for affect, relationships, and well-being. Journal of Personality and Social Psychology. 85(2), 348- 362. https://doi.org/10.1037/0022- 3514.85.2.348 Hasin, D. S., Saha, T. D., & Kerridge, B. T. (2015). Prevalence of marijuana use disorders in the United States between 2001-2002 and 2012- 2013. JAMA Psychiatry, 72(12), 1235-1242. https://doi.org/10.1001/jamapsychiatry.2015.1 858 Hayaki, J., Herman, D. S., Hagerty, C. E., De Dios, M. A., Anderson, B. J., & Stein, M. D. (2011). Expectancies and self-efficacy mediate the effects of impulsivity on marijuana use outcomes: An application of the acquired preparedness model. Addictive Behaviors, 36(4), 389-396. https://doi.org/10.1016/j.addbeh.2010.12.018 Jacobus, J., & Tapert, S. F. (2014). Effects of cannabis on the adolescent Brain. Current Pharmaceutical Design, 20(13), 2186-2193. https://doi.org/10.2174/1381612811319999042 6 Johnson, D. P., & Whisman, M. A. (2013). Gender differences in rumination: A meta-analysis. Personality and Individual Differences, 5(4), 367-374. https://doi.org/10.1016/j.paid.2013.03.019 Kentopp, S. D., Johnson, N., Fresquez, C., Prince, M. A., Conner, B. T., & Marijuana Outcomes Personality, Rumination, Coping Motives, and Marijuana Problems 96 Study Team. (2019). Risk seeking moderates the association between emotion dysregulation and cannabis-related consequences. Journal of Drug Issues, 49(3), 559-569. https://doi.org/10.1177/0022042619847220 Lee, C. M., Neighbors, C., Hendershot, C. S., & Grossbard, J. R. (2009). Development and preliminary validation of a comprehensive marijuana motives questionnaire. Journal of Studies on Alcohol and Drugs, 70(2), 279-287. https://doi.org/10.15288/jsad.2009.70.279 Looby, A., Prince, M. A., Villarosa-Hurlocker, M. C., Conner, B. T., Schepis, T. S., Bravo, A. J., & Stimulant Norms and Prevalence (SNAP) Study Team. (2021). Young adult use, dual use, and simultaneous use of alcohol and marijuana: An examination of differences across use status on marijuana use context, rates, and consequences. Psychology of Addictive Behaviors, 35(6), 682-690. https://doi.org/10.1037/adb0000742 Lynch, T. R., Trost, W. T., Slasman, N., & Linehan, M. M. (2007). Dialectical Behavior Therapy for Borderline Personality Disorder. Annual Review Clinical Psychology, 3, 181- 205. https://doi.org/10.1146/annurev.clinpsy.2.022 305.095229 Martín-Santos, R., De Souza Crippa, J., & Bhattacharyya, S. (2017). Neuroimaging and genetics of the acute and chronic effects of cannabis. Handbook of Cannabis and Related Pathologies, e42-e52. https://doi.org/10.1016/b978-0-12-800756- 3.00040-5 Mauro, P.M., Carliner, M., Brown, Q.L., Hasin, D.S., Shmulewitz, D., Rahim-Juwel, R., Sarvet, Al.L., Wall, M.M., Matrins, S.S. (2018). Age differences in daily and nondairy cannabis use in the United States 2012-2014. Journal of Studies on Alcohol and Drugs, 79(3), 423-431. https://doi.org/10.15288/jsad.2018.79.423 Muthén, L.K. and Muthén, B.O. (1998-2017). Mplus user’s guide. Eight Edition. Muthén & Muthén. Nolen-Hoeksema, S. (1991). Responses to depression and their effects on the duration of depressive episodes. Journal of Abnormal Psychology, 100(4), 569-582. https://doi.org/10.1037//0021-843x.100.4.569 Nolen-Hoeksema, S., & Harrell, Z. A. (2002). Rumination, depression, and alcohol use: Tests of gender differences. Journal of Cognitive Psychotherapy: An International Quarterly, 16(4), 391-403. https://doi.org/ 10.1891/jcop.16.4.391.52526 Nolen-Hoeksema, S. (2012). Emotion regulation and psychopathology: The role of gender. Annual Review of Clinical Psychology, 8, 161- 187. https://doi.org/10.1146/annurev-clinpsy- 032511-143109 Nolen-Hoeksema, S., & Aldao, A. (2011). Gender and age differences in emotion regulation strategies and their relationship to depressive symptoms. Personal Individual Differences, 51, 704-708. https://doi.org/ 10.24193/subbpsyped.2018.1.01 Nolen-Hoeksema, S., & Jackson, B. (2001). Mediators of the gender difference in rumination. Psychology of Women Quarterly, 25(1), 37-47. https://doi.org/10.1111/1471- 6402.00005 Neugebauer, R. T., Parnes, J. E., Prince, M. A., Conner, B. T., & Marijuana Outcomes Study Team. (2019). Protective behavioral strategies mediate the relation between sensation seeking and marijuana-related consequences. Substance Use & Misuse, 54(6), 973-979. https://doi.org/10.1080/10826084.2018.155525 6. Pearson, M. R., & Marijuana Outcomes Study Team. (2020). Marijuana Use Grid: A brief, comprehensive measure of marijuana use. Journal of the Society of Psychologists in Addictive Behaviors, 33(4), 412–419. https://doi.org/10.1037/adb0000445 Pearson, M. R., Hustad, J. T. P., Neighbors, C., Conner, B. T., Bravo, A. J., & Marijuana Outcomes Study Team (2018). Personality, marijuana norms, and marijuana outcomes among college students. Addictive Behaviors, 76, 291-297. https://doi.org/10.1016/j.addbeh.2017.08.012 Prosek, E. A., Giordano, A. L., Woehler, E. S., Price, E., & McCullough, R. (2018). Differences in emotion dysregulation and symptoms of depression and anxiety among illicit substance users and nonusers. Substance Use & Misuse, 53(11), 1915-1918. https://doi.org/10.1080/10826084.2018.143656 3 Cannabis, A Publication of the Research Society on Marijuana 97 Rinehart, L., & Spencer, S. (2021). Which came first: Cannabis use or deficits in impulse control? Progress in Neuro- Psychopharmacology and Biological Psychiatry, 106, 110066. https://doi.org/10.1016/j.pnpbp.2020.110066 Robinson, M. D., Klein, R. J., & Persich, M. R. (2019). Personality traits in action: A cognitive behavioral version of the social cognitive paradigm. Personality and Individual Differences, 147, 214-222. https://doi.org/10.1016/j.paid.2019.04.041 Semcho, S., Bilsky, S.A., Lewis, S.F., & Leen- Felder, E.W. (2016). Distress tolerance predicts coping motives for marijuana use among treatment seeking young adults. Addictive Behaviors, 58(1), 85-89. https://doi.org/10.1016/j.addbeh.2016.02.016 Shrivastava, A., Johnston, M., & Tsuang, M. (2011). Cannabis use and cognitive dysfunction. Indian Journal of Psychiatry, 53(3), 187-191. https://doi.org/10.4103/0019- 5545.86796 Simmons, J. P., Nelson, L. D., & Simonsohn, U. (2011). False-positive psychology: Undisclosed flexibility in data collection and analysis allows presenting anything as significant. Psychological Science, 22(11), 1359-1366. https://doi.org/10.1177/0956797611417632 Simons. J., Correia, C.J., Carey, K.B., & Borsari, B.E. (1998). Validating a five-factor marijuana motives measure: relations with use, problems, and alcohol motives. Journal of Counseling Psychology, 45(3), 265–273. https://doi.org/10.1037/0022-0167.45.3.265 Simons J. S., & Carey, K. B. (2002). Risk and vulnerability for marijuana use problems: The role of affect dysregulation. Psychology of Addictive Behavior, 16(1), 72-75. https://doi.org/10.1037/0893-164X.16.1.72 Simons, J. S., & Gaher, R. M. (2005). The distress tolerance scale: Development and validation of a self-report measure. Motivation and Emotion, 29(2), 83-102. https://doi.org/10.1007/s11031-005-7955-3 Simons, J. S., Dvorak, R. D., Merrill, J. E., & Read, J. P. (2012). Dimensions and severity of marijuana consequences: Development and validation of the Marijuana Consequences Questionnaire (MACQ). Addictive Behaviors, 37(5), 613-621. https://doi.org/10.1016/j.addbeh.2012.01.008 Trudeau, L., Spoth, R., Mason, W. A., Randall, G. K., Redmond, C., & Schainker, L. (2015). Effects of Adolescent Universal Substance Misuse Preventive Interventions on Young Adult Depression Symptoms: Mediational Modeling. Journal of Abnormal Child Psychology, 44(2), 257-268. https://doi.org/10.1007/s10802-015-9995-9 VanderVeen, D. J., Hershberger, A. R., & Cyders, M. A. (2016). UPPS-P model impulsivity and marijuana use behaviors in adolescents: A meta-analysis. Drug and Alcohol Depend,ence 168, 181-190. https://doi.org/10.1016/j.drugalcdep.2016.09.0 16 Wagner, J., Orth, U., Bleidorn, W., Hopwood, C. J., & Kandler, C. (2020). Toward an integrative model of sources of personality stability and change. Current Directions in Psychological Science, 29(5), 438-444. https://doi.org/10.1177/0963721420924751 Whiteside, S. P., & Lynam, D. R. (2001). The five factor model and impulsivity: Using a structural model of personality to understand impulsivity. Personality and Individual Differences, 30(4), 669-689. https://doi.org/10.1016/s0191-8869(00)00064-7 Wisk, L. E., & Weitzman, E. R. (2016). Substance use patterns through early adulthood. American Journal of Preventive Medicine, 51 (1), 33-45. https://doi.org/10.1016/j.amepre.2016.01.029 Funding and Acknowledgements: Dr. Bravo was supported by a training grant (T32-AA018108) from the National Institute on Alcohol Abuse and Alcoholism (NIAAA) in the United States during the duration of data collection for Project CMS. Data collection was supported, in part, by grant T32-AA018108. NIAAA had no role in the study design, collection, analysis or interpretation of the data, writing the manuscript, or the decision to submit the paper for publication. The author(s) thank the Office of the Provost of W&M University for a faculty summer research grant to Dr. Bravo in support of this work. **This project was completed by the Cross- cultural Addictions Study Team (CAST, castresearcher@gmail.com), which includes the following investigators (in alphabetical order): Personality, Rumination, Coping Motives, and Marijuana Problems 98 Adrian J. Bravo, William & Mary, USA (Coordinating PI); James M. Henson, Old Dominion University, USA; Manuel I. Ibáñez, Universitat Jaume I de Castelló, Spain; Laura Mezquita, Universitat Jaume I de Castelló, Spain; Generós Ortet, Universitat Jaume I de Castelló, Spain; Matthew R. Pearson, University of New Mexico, USA; Angelina Pilatti, National University of Cordoba, Argentina; Mark A. Prince, Colorado State University, USA; Jennifer P. Read, University at Buffalo, USA; Hendrik G. Roozen, University of New Mexico, USA; Paul Ruiz, Universidad de la República, Uruguay ***This project was completed by the Stimulant Norms and Prevalence (SNAP) Study Team, which includes the following investigators (in alphabetical order): Adrian J. Bravo, William & Mary (Co-PI); Bradley T. Conner, Colorado State University; Mitch Earleywine, University at Albany, State University of New York; James Henson, Old Dominion University; Alison Looby, University of Wyoming (Co-PI); Mark A. Prince, Colorado State University; Ty Schepis, Texas State University; Margo Villarosa-Hurlocker, University of New Mexico. Copyright: © 2024 Authors et al. 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