The primary analysis employed a generalized linear mixed modelling framework , using an intent-to-treat approach

Participants who completed the baseline survey with responses consistent with the eligibility screener were randomized to immediately receive either the personalized feedback final report  or to just receive the educational materials . Those completing the three- and six-month follow-up surveys were provided with gift certificates from Amazon.ca . Participants were sent an email  containing a link to complete the follow-up survey online. In the current trial, an updated version of an existing personalized feedback intervention for risky cannabis use was employed . Briefly, the personalized feedback final report allows participants to compare their own risky cannabis use to other people in the general population of Canada. For this version, the general population norms were derived from the most recently available Canadian Tobacco, Alcohol and Drugs Survey. In the final report, norms regarding frequency of cannabis use were provided that compared the participant to others of the same gender and age group. If participants did not identify as male or female, mobile grow rack then feedback was generated based on the population norms of age . It should be noted that the norms generated were derived from population data collected prior to the legalization of cannabis in Canada and it was anticipated that they would report lower levels of cannabis use than was observed in the general population after legalization.

However, we believed that this was not necessarily a weakness for the proposed intervention as norms that show lower use of cannabis in the general population would accentuate the difference between the participant’s own use and that of other Canadians their age and gender, thus potentially making the intervention more impactful. As new population data is collected, the population norms could be updated. See Fig. 2 for an example of the personalized normative feedback component from the final report. New to this version of the intervention, participants were provided feedback from the Marijuana Problems Scale , a self-report questionnaire with 19 items asking how frequently the participant experienced a number of consequences related to their cannabis use. Following the feedback framework developed by Bertholet et al.  in their personalized feedback intervention for alcohol, relevant items from this scale were divided into three categories – Me & my body , Me and my relationships , and Me and my professional activities . A thermometer-type figure was provided in the feedback for each of these categories with the level on the thermometer marked based on the number of consequences experienced in the past three-months . The actual consequences endorsed were also listed after each thermometer . Next, if participants stated that they used cannabis with tobacco, with alcohol, or drove while under the influence of cannabis, they were provided with an explanation of why each of these activities increased their risk of harm.

The feedback then continued with a summary of their ASSIST cannabis subscale score that included a graphical depiction of their score mobile vertical grow racks. Finally, the personalized feedback was accompanied by educational material that had already been developed and pilot tested at the Centre for Addiction and Mental Health. Specifically, participants were provided with content from, “Canada’s Lower- Risk Cannabis Use Guidelines” . Missing data was handled using maximum likelihood estimation. Analyses of the primary hypothesis evaluated the effect of the personalized feedback intervention  versus the educational material only  on reductions in the variable, number of days consumed cannabis in the past 30, between baseline and three and six months. Time point  was entered as a within-subjects predictor and a dummy-coded contrast represented intervention versus control group conditions. The interaction between time point and condition was examined to determine if changes in cannabis consumption differed between the two conditions. Demographic or cannabis use characteristics that were significantly different across condition at baseline were included as covariates to address any potential differences between intervention and control groups. In addition, because the COVID pandemic was ongoing during the follow-up period for some participants of this study, we included a variable that recorded whether the pandemic was ongoing  as a covariate. As part of this analysis, a chi-square analysis was conducted to determine whether there was differential loss to follow-up between experimental groups. The analysis for secondary hypothesis 1 employed a manual step wise logistic regression, with risky use of cannabis at the three- or six-month follow-ups as the dependent variable.

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