Cannabis use breaks in young adults: The highs and lows of tolerance breaks.

BACKGROUND/PURPOSE: Cannabis tolerance breaks, or T-breaks, are believed to benefit persons who use cannabis (PUCs) by decreasing tolerance levels to cannabis. However, no prior research, to our knowledge, has compared the effects of T-breaks and other use breaks on cannabis use patterns and outcomes. The current study examined whether the occurrence of cannabis use breaks (tolerance and other use breaks), or the duration of these breaks, is associated with changes in hazardous cannabis use (CUDIT-R), CUD severity, cannabis use frequency, and withdrawal symptoms over a 6-month follow-up. METHODS: Young adults, who recreationally use cannabis, (N=170, 55.9% female, Mean age=21 yo) completed baseline and on-time 6-month assessments of hazardous cannabis use (CUDIT-R), CUD severity, cannabis use frequency, and withdrawal symptoms. The occurrence of cannabis use breaks and the duration of these breaks during the intervening period was assessed at 6 months. RESULTS: Taking a T-break was associated with an increase in hazardous cannabis use and CUD severity at 6 months. When considering cannabis use breaks for other reasons, a longer break was associated with a significant decrease in hazardous cannabis use (CUDIT-R), CUD severity, and cannabis use frequency at 6 months. CONCLUSION: Findings from our study suggest recreational PUCs who take a T-break may be at greater risk for problematic cannabis use. In addition, taking a longer cannabis use break for other reasons may have beneficial effects on cannabis-related outcomes. The ability to abstain from cannabis for other reasons may be protective while individuals who take T-breaks may be important targets for intervention and prevention.

A Systematic Review of Validated Measures of U.S.-Based Bystander Intervention-Related Constructs.

This review aimed to identify U.S.-based, construct-validated measures of bystander intervention. Following PRISMA-P guidelines, electronic databases were searched, and emails were solicited identifying 8,559 articles for title screening. Abstracts and full texts were double screened, resulting in 24 scales meeting inclusion criteria: (a) measured a bystander-related construct in a situation where there was a potential for actual or perceived imminent physical or emotional harm, (b) written in English, and (c) statistically validated on U.S. samples. Most scales addressed the domain of interpersonal violence (67%), with fewer relating to bias/bullying (8.2%), mental health crises (12.5%), and substance use (12.5%). Most scales (71%) assessed the "take action" step of the situational model. The modal construct represented was intent/willingness/likelihood to intervene (50%). The average number of items on a scale was 14, and most (79%) provided Likert-style response options. None of the validated scales assessing behavior first accounted for an opportunity. Sample sizes ranged from 163 to 3,397, with the modal setting from colleges. Overall, samples were young (21.8 years old), White (75%), women (64%), and heterosexual (89%). Results indicate the need to validate additional measures that capture the "interpreting the situation as problematic" step of the situational model. Scales also need to be validated using diverse samples, particularly within the mental health crisis domain. Across all domains, validated measures need to be developed that first account for an opportunity when measuring actual bystander behavior. The information gleaned can be used to assist researchers in selecting measures and guide future measure development.

Coming together in a digital age: Community twitter responses in the wake of a campus shooting.

Campus mass shootings have become a pressing policy and public health matter. Twitter is a platform used for processing events among interested community members. Examining the responses of invested community members to a mass shooting on a college campus provides evidence for how this type of violence affects the immediate community and the larger public. These responses may reflect either content (e.g. context-specific) or emotions (e.g. humor). Aims Using Twitter data, we analyzed the emotional responses as well as the nature of non-affective short-term reactions, in response to the April 2019 shooting at UNC Charlotte. Methods Drawn from a pool of tweets between 4/30/19-5/7/19, we analyzed 16,749 tweets using keywords related to the mass shooting (e.g. "shooting," "gun violence," "UNC Charlotte"). A coding team manually coded the tweets using content and sentiment analyses. Results Overall, 7,148 (42.67%) tweets contained negative emotions (e.g. anger, sadness, disgust, anxiety), 5,088 (30.38%) contained positive emotions (e.g. humor, hope, appreciation), 14,892 (88.91%) were communal responses to the shooting (e.g. prayers, healing, victim remembrance), 8,329 (49.73%) were action-oriented (e.g. action taken, policy advocacy), and 15,498 (92.53%) included information (e.g. death/injury, news). All tweets except positive emotions peaked one day following the incident. Conclusions Our findings point to peaks in most emotions in the 24 hours following the event, with the exception of positive emotions which peaked one day later. Social media responses to a campus shooting suggest college preparedness for immediate deployment of supportive responses in the case of campus violence is needed.