Development of a Decisional Balance Scale for Young Adult Marijuana Use
This study describes the development and validation of a decisional balance scale for marijuana use in young adults. Scale development was accomplished in four phases. First, 53 participants (70% female, 68% freshman) provided qualitative data that yielded content for an initial set of 47 items. In the second phase, an exploratory factor analysis on the responses of 260 participants (52% female, 68% freshman) revealed two factors, corresponding to pros and cons. Items that did not load well on the factors were omitted, resulting in a reduced set of 36 items. In the third phase, 182 participants (49% female, 37% freshmen) completed the revised scale and an evaluation of factor structure led to scale revisions and model respecification to create a good-fitting model. The final scales consisted of 8 pros (α = 0.91) and 16 cons (α = 0.93), and showed evidence of validity. In the fourth phase (N = 248, 66% female, 70% freshman), we confirmed the factor structure, and provided further evidence for reliability and validity. The Marijuana Decisional Balance Scale enhances our ability to study motivational factors associated with marijuana use among young adults.
Marijuana is the most commonly used illegal drug in the United States; 16.5% of young adults (ages 18 to 25) have used marijuana within the past month (Substance Abuse and Mental Health Services Administration [SAMHSA], 2008). Despite its image as a relatively benign substance, some users experience negative consequences. As many as 9.4% of college freshman may have a marijuana use disorder (Caldeira, Arria, O’Grady, Vincent, & Wish, 2008), and approximately 35% of users meet at least one criterion for marijuana dependence (Nocon, Wittchen, Pfister, Zimmerman & Lieb, 2006). Moreover, heavy marijuana use compromises cardiovascular health (Aryana & Williams, 2007), increases susceptibility to cancer (Hashibe, Straif, Tashkin, Morgenstern, Greenland, & Zhang, 2005), and impairs short- and long-term memory functioning (Pope and Yurgelun-Todd, 1996; Kouri, Pope, Yurgelun-Todd, & Gruber, 1995).
Given the relatively high prevalence, young adults may consider marijuana use as having benefits as well as consequences. Better understanding of user-identified benefits and consequences of using marijuana may inform prevention efforts. Due to the relative stability of marijuana use over time (Perkonigg, Goodwin, Fiedler, Behrendt, Beesdo, Lieb et al., 2008), prevention efforts targeted to adolescents and young adults may avert establishment of long-term use patterns. Therefore, the purpose of this multi-phase study was to identify young adults’ perceptions of the benefits and consequences of marijuana use and to develop and refine a reliable and valid measure of the pros and cons (i.e., decisional balance) of marijuana use.
Decisional balance (DB) provides a motivational framework for understanding use (Janis & Mann, 1977). Janis and Mann outline four content domains of pros and cons that are typically addressed during this process: (a) gains/losses for oneself, (b) gains/losses for significant others, (c) self-approval or -disapproval, and (d) approval or disapproval from significant others. DB is often simplified into two categories of pros and cons, which may be anchored with respect to either continuing or changing a target behavior.
Pros and cons not only provide information about positive and negative attitudes toward a behavior, but together may serve as a marker for readiness to change. Specifically, individuals further along a change process tend to report more pros and fewer cons of change and more cons and fewer pros of the problem behavior. These patterns emerge with a range of problem behaviors (Prochaska et al., 1994), including young adult alcohol and cigarette use (Migneault, Velicer, Prochaska, & Stevenson, 1999; Migneault, Pallonen, and Velicer, 1997; Pallonen, 1998). Yet, no formal measure of DB specific to marijuana currently exists.
Pros and cons resemble other cognitive-motivational constructs, including motives and expectancies, but are distinct in important ways. Scales measuring motives for marijuana use have been developed (e.g. Simons, Correia, Carey, & Borsari, 1998; Lee, Neighbors, & Woods, 2007). Although motives may parallel the pros of DB, motivational assessments do not address the perceived costs or undesirable aspects of marijuana use, a dimension known to be predictive of behavior change (Prochaska, 1994). Outcome expectancies for marijuana use (Schafer & Brown, 1991) may also be related to pros and cons. Expectancies address generalized cognitions about likely outcomes of use behavior, whereas pros and cons address motivational factors specific to an individual’s decisions regarding future behavior (e.g. expectancies weighted by personal values). These conceptual differences suggest that a DB scale for marijuana may have unique predictive ability regarding marijuana use patterns. Pros and cons for using marijuana would describe what enhances or reduces interest in using marijuana, thus providing information on what features of marijuana are viewed as appealing versus aversive in young adults. For example, young adults may view enhanced relaxation as a motivator, or pro, and personality change as a deterrent, or con. Yet, no scale of this nature has yet been developed.
The purpose of this study was to create a DB scale for marijuana use targeted to young adults, and provide evidence for its factor structure, reliability, and validity. We accomplished this in four steps, all of which involved surveying undergraduates enrolled in introductory psychology courses in 2007–2008 attending a large urban private university in the northeastern United States. First, we obtained qualitative information about the perceived pros and cons of marijuana use. We transformed these responses into distinct statements for the scale, using the empirical literature as a secondary guide. Second, we administered these items to a new sample, conducted an exploratory factor analysis (EFA) on their responses to detect the underlying factor structure, and removed items with insufficient loadings. Third, we administered the reduced item pool to a new sample, and conducted confirmatory factor analyses of the measurement model. We assessed related constructs to provide evidence of validity. Finally, we administered the final scale to an independent sample to confirm its factor structure and extend validity testing.
Phase I: Item Generation
In phase I, young adults generated pros and cons of marijuana use, through unstructured and structured exercises. Items for our initial measure were derived from the responses provided.
Phase I Method
Fifty-three students (M age = 19, SD = 0.91; 70% female, 62% White, 68% freshman) completed the study for credit toward an undergraduate psychology class. Of the 53 participants, 30 (57%) reported lifetime marijuana use. Marijuana users were similar to abstainers in age, gender, and year, but were more likely to be White (χ 2 = 9.26, p 0.10.
Participants assembled in small groups (of approximately ten) in a classroom. Written informed consent was obtained, and participants were informed of the protection of their data, including the Certificate of Confidentiality obtained from the National Institutes of Health (NIH). Participants completed questionnaires regarding (a) demographics (gender, age, ethnicity, year in school) and (b) their non-prescription use of legal and illegal drugs during the last 30 days and in their lifetime. Next, participants were asked to generate pros and cons of marijuana use, considering their own experiences and observations of others’ experiences, along with speculations. Finally, participants were prompted to think about personal gains/losses from marijuana use, gains/losses for others, issues regarding self-approval and –disapproval, and approval and disapproval from others, consistent with Janis and Mann’s (1977) framework. Participants recorded responses on a worksheet.
Participants who had used marijuana were invited to provide additional information via individual interviews. Ten volunteers (out of 12 participants who expressed interest) completed a half-hour interview, consisting of further elaboration of their reported pros and cons, along with structured prompts regarding possible domains of pros and cons (e.g. academic, social, health). We used this information to aid in item writing.
Phase I Results
All pros and cons generated by participants were reviewed and written in item form, following recommended principles for item writing (Clark & Watson, 1995). Comprehensive lists of pros and cons were constructed, with frequency counts (available from the first author upon request). Responses that appeared multiple times or that converged on a theme were retained as items. Responses that were vague (e.g. “I enjoy doing it”), obscure (e.g. “I heard it’s good for your eyes”), or did not qualify as pros/cons (e.g. “There are no taxes on it”) were excluded. Other responses were excluded because they contradicted more common responses (e.g. “It smells wonderful”) or were more relevant for a medical population (e.g. “medicinal”). Items that occurred only once were not retained unless they reflected themes present in empirical articles on motivation for marijuana use. In total, 47 items were selected for our initial scale.
All items were reviewed and edited for clarity and conciseness. Items were phrased as statements regarding the positive and negative aspects of marijuana use. To assess the importance of each statement to participants’ decision of whether to use marijuana, items were formatted to conform to a 5-point response option (1 = not at all important; 5 = very important). A 5-point response option was selected to maximize variability within responses.
Phase II: Scale Development
In phase II, we elicited responses to the initial set of 47 items for the purposes of discerning factor structure and refining items based on feedback. Our objective was to retain a smaller number of items with interpretable factor structure for the DB scale.
Phase II Method
Students (N = 260; M age = 19, SD = 0.93; 52% female, 62% White, 68% freshman) participated in exchange for credit toward their undergraduate psychology class. Sample size was determined a priori to exceed a five to one participant to item ratio (Gorsuch, 1983).
Procedure and Measures
Small groups of participants (up to 20 participants per session) provided written informed consent and were reminded of confidentiality protections. All participants completed demographic and substance use measures from Phase I, and the preliminary 47-item Marijuana Decisional Balance (MDB) scale. When responding to the MDB scale, participants were encouraged to consider the reasons why they chose to use marijuana or not to use marijuana, and rate the importance of each item as it might influence their own decisions to use or not use. In order to interpret the meaning of the items, participants were told to use all information available to them, using their own experiences when relevant but also secondhand information. The investigators encouraged participants to make note of any items that were unclear.
Phase II Results
Most participants (70%) reported lifetime experience with marijuana. Marijuana users were similar to abstainers in age and year in school, but users were more likely to be male (χ 2 = 6.21, p 2 = 26.01, p 2 = 5.44, p 2 ). A lack of multivariate normality was found. Three participants’ data that were most problematic were excluded and the model was rerun; results were consistent with and without these data and thus responses were retained.
Confirmatory Factor Analysis Measurement Model
To assess how well the data fit with the proposed model of pros and cons, we ran a CFA measurement model using AMOS 16 (Arbuckle, 2006). Model fit was assessed using the comparative fit index (CFI), the root mean square error of approximation (RMSEA), and chi-square/degrees of freedom ratio (Kline, 1998). CFI compares the hypothesized model with the independence model, in which nothing is related (Byrne, 2001). A CFI of 0.95 or above indicates good fit. The RMSEA estimates how well the model fits with the estimated population covariance matrix (Byrne, 2001). RMSEA should be well under 0.10, and preferably under 0.06 (Tabachnick & Fidell, 2007). A good fitting model is assumed when chi-square is non-significant; however, chi-square is extremely sensitive to sample size. To minimize this problem, chi-square is divided by the degrees of freedom with a chi-square/df ratio of 3 or less indicating acceptable fit (Kline, 1998).
Fit of our initial model was poor (CFI = 0.87; RMSEA = .08; χ 2 = 722.86, df = 349, χ 2 /df = 2.02). Inspection of the standardized residual covariances indicated two problem items (“It could enhance my creativity and the creative process [e.g. making art or music, writing]”; “It’s an escape from reality and everyday life”). These two items were removed because several high standardized residual covariances indicated that they did not fit well in the model (standardized residual covariances fell over 2 with several other items). The respecified model was analyzed using CFA: CFI = 0.88; RMSEA = .08; χ 2 = 603.11, df = 298, χ 2 /df = 2.02. One additional item (“It could change how I act in social situations, for the worse [e.g. it would make me socially awkward, insensitive to others’ feelings]”) was then removed because of (a) high standardized residual covariances (over 2), (b) it was listed in several modification indices (when run with complete data), and (c) a high loading on the opposing factor. Fit statistics for the respecified model were: CFI = 0.89; RMSEA = .08; χ 2 = 554.42, df = 274, χ 2 /df = 2.02.
Note. Asterisks (*) indicate significance, p a Recent (past 30 day) marijuana users: Phase III, n = 75; Phase IV, n = 101; Combined n = 176.
All 182 participants completed the MEEQ-S, yielding data on positive and negative expectancies. As expected, pros and positive expectancies were positively correlated (r = 0.37, p 2 = 9.74, p 2 = 27.90, p 2 = 493.65, df = 244, p Table 1 , the bivariate validity analyses from Phase IV were generally consistent with previous findings (see Table 1 ). Phases III and IV data were then combined for the purpose of obtaining the most stable correlations with a larger sample. Additional evidence of validity emerged in the relationship of pros and cons to the new scales introduced in Phase IV. Behavioral intentions correlated positively with pros, r = 0.68, and negatively with cons, r = −0.59, both ps Figure 2 ). We restricted these analyses to lifetime users, and found that earlier stages were associated with more pros (F [4, 146] = 16.66, p 2 = .59, p 2 = .12, p 2 = .53, p 2 = .44, p 2 = 0.59) than one composed of positive and negative expectancies (R 2 = 0.22). Hierarchical regression was used to demonstrate incremental validity of pros and cons over and above positive and negative expectancies for behavioral intentions. Intentions was regressed first on positive and negative expectancies, and then on expectancies combined with pros and cons. All predictors in this model explained significant amounts of variance (ps ≤ .001); pros and cons increased R 2 from .22 to .60.
This series of studies developed a decisional balance scale to assess the costs and benefits of marijuana use among young adults. The final MDB scale contains 24 items, with 8 representing pros and 16 representing cons of marijuana use. Reliability analyses indicated strong internal consistency (α > 0.90 for both subscales). The MDB scale represents a new tool for understanding motivations to use marijuana.
Pros and cons were moderately but inversely correlated, suggesting that these constructs are related but represent independent sources of information. Though rarely reported in published studies, the correlations between pros and cons ranges from essentially zero to slightly positive for other behaviors such as drinking by college students (Migneault et al., 1999; Noar et al., 2003) and physicians’ implementation of smoking cessation programs (Park et al., 2001). Thus, pros and cons appear to be more strongly related for marijuana use, which differs from other behaviors studied with regard to its illegal status.
The majority of predictions about the presence and directionality of relationships with other known measures were supported. Endorsement of the pros of marijuana use was associated with greater frequency of use, intentions to use in the future, and more problems, as well as stronger positive expectancies and attitudes in favor of legalizing marijuana. These findings are consistent with a generally favorable evaluation of and/or greater involvement with marijuana. Endorsement of the cons of marijuana use was more likely by females, younger and non-White participants. Stronger endorsement of cons was associated with less frequent use and lower intentions to use, consistent with previous research showing a negative relation between use frequency and perceived risk (e.g. Kilmer et al., 2007). Participants who endorsed cons held stronger negative outcome expectancies and were unlikely to favor legalizing marijuana. Yet, cons were inconsistently related to marijuana-related problems. This finding is consistent with Noar et al. (2003) with regard to alcohol DB, and suggests that even users experiencing few problems acknowledge that there are cons to using marijuana. We observed the potential for social desirability bias on pros in one sample, but no relationship with cons. Thus, participants who tried to present themselves in a socially desirable light were reluctant to endorse pros of marijuana use, perhaps because it is an illicit substance. The potential for social desirability bias should be taken into account in future research using this scale.
The incremental validity of the MDB scales was supported with regard to positive and negative expectancies. Pros and cons predicted behavioral intentions better than expectancies did, most likely reflecting the personalized, motivational component of pros and cons not seen in expectancies. However, how well pros and cons predict future use remains untested.
The association between marijuana pros and cons across stages of change provides cross-sectional support for predictions from the transtheoretical model of change. Specifically, the “crossover effect” appears to occur on the cusp of the Preparation and Action phases. This is a late point in the stages according to some health behaviors (e.g., safe sex), but approximates the point at which the crossover occurs for other behaviors (e.g., exercising) (Prochaska et al., 1994).
Overall, analyses support the construct validity of the pros and cons subscales. In addition, the pros and cons in the final scale reflect several of the content domains suggested by Janis and Mann (1977), including personal gains/losses from marijuana use (e.g. “It would help me sleep”), gains/losses for others (e.g. “It may cause me to be a bad influence on others”), and issues regarding approval and disapproval from others (e.g. “It’s not accepted or approved of by people who are important to me”). Although no retained item directly references self –approval or –disapproval, many items imply approval or disapproval based on item valence.
Several strengths of this study enhance confidence in the findings. The MDB scale was developed based on the input and responses from four independent samples from a relevant population in a four-phase design. Samples included both marijuana users and abstainers, and thus addressed pros and cons associated with both decisional outcomes. Due to the advertisement of this project as a study of marijuana use, users were over-sampled; indeed, the prevalence of recent use exceeds that of national samples of young adults (Gledhill-Hoyt, Lee, Strote, & Wechsler, 2000). Validation and confirmation phases of the research employed well-validated, psychometrically sound measures, and revealed theoretically-consistent patterns of relationships.
As with any research, this study was subject to limitations. The sample sizes, though adequate according to published guidelines, fell at the low end of the target ranges. As a result, the marijuana users and abstainers were analyzed simultaneously; it is possible that they might produce distinct patterns in data that could have been detected with larger samples. Although sample size limitations preclude separate factor analyses by use subgroup, validity analyses by use subgroup provided generally consistent results (results available upon request from the first author). Though marijuana users were over-represented (relative to population prevalence), the inclusion of abstainers may have led to the retaining of some items less frequently endorsed by marijuana users, and may be related to the large number of cons in the final scale. Differing patterns in data may also even be detected between former versus current users, though this is likely to be less significant than the differences between users and abstainers. Additionally, the psychometric support for the scale has only been collected in self-selected undergraduate samples at a single university in the northeastern United States, potentially limiting external validity. The young adults of this sample represent a demographic (18–25 years) at high risk for marijuana use, and both genders and several ethnicities are represented in the sample; however, education and residence on a college campus may limit generalizability. Also, rates of recent use in the current samples clearly exceed most other young adult populations (SAMHSA, 2008); last month prevalence of use ranged from 40.7%-50.9%, compared to approximately 16.5% nationally among 18–25 year olds. Generalizability to non-college attending adults thus cannot be assumed; further research must be done before the scale is used with other populations.
Additional research would help to establish the utility of the MDB scale. First, replicating the factor structure with new populations (e.g., those with cannabis dependence) could shed light on its generalizability. Similarly, validating the scale with other populations would support its utility beyond young adults attending college. Second, the incremental validity of pros and cons could be further addressed by comparing them to generic drug use DB scales or other psychological constructs like motives; such designs could establish the extent to which the MDB scales provide unique or greater explanatory power than alternative measures. Third, tests for true predictive validity are needed to determine the degree to which pros and cons predict subsequent marijuana use throughout the life course. For example, MDB scales may predict the onset and/or escalation of use in a developmental context. Furthermore, among established users, the MDB scale may serve as a method of monitoring motivational changes over time; if scores on pros and cons do predict future use, then changes in DB components might reflect increases or decreases in motivation or readiness to change. Finally, test-retest reliability has not yet been assessed, thus we do not yet know how stable pros and cons remain over time.
A DB scale for marijuana use could be helpful within clinical contexts, stimulating discussions in therapy. Eliciting the pros and cons of use may aid an individual in identifying sources of ambivalence about marijuana use, a technique used in Motivational Interviewing (Miller & Rollnick, 2002). This exercise helps the client articulate personal reasons for behavior change, which may promote movement through stages of change. Although the therapeutic DB exercise often involves generation of ideas without suggestions, the MDB scale may facilitate more guided discussions. Additionally, the MDB scale could serve as a screening mechanism for interventions, such that individuals reporting many pros could be targeted for decisional balance based interventions designed to build motivation to change use.
DB, or the consideration of pros and cons, is a prominent concept in behavioral change (Prochaska et al., 1994). Its key role in decision making allows clinicians to gain insight into the willingness of the individual to embark on the process of change. This scale development project marks the initial step toward an empirical understanding of the pros and cons of marijuana use. The MDB scale allows for theory testing, exploration of the utility of the DB concept for marijuana use, and provides a potential marker of motivation for change. Additional research is needed to confirm structure in more diverse samples and explore predictive validity. In sum, we provide initial evidence that marijuana use can be assessed from a DB framework, and that such a framework may advance our ability to predict current and future marijuana use.
This study was based on the first author’s master’s thesis at Syracuse University. This research was supported by a grant to Kate B. Carey (K02-AA15574) from the National Institutes of Health. We thank Roxanna Pebdani, Julianne Pacheco, and Namita Varma for their assistance in data collection and data entry, and Stephen Maisto, Peter Vanable, and Alecia Santuzzi for their suggestions on the planning and analyses of this study.
(Promax) Rotated factor loadings from Phase II Exploratory Factor Analysis
|2||It’s illegal, and I could get caught.||0.62||−0.08||3.30 (1.51)|
|3||It’s not accepted or approved of by people
who are important to me.
|5||It could impair my performance in my daily
|7||It could reduce my ability to pay attention or
|10||It’s not socially acceptable.||0.56||−0.16||2.25 (1.39)|
|16||It could make me feel bad physically (e.g. dry
mouth, red eyes, racing heart).
|18||It could have unpleasant psychological effects
(e.g. mood swings, depression, paranoia).
|20||It could be laced with other drugs.||0.47||0.01||3.18 (1.65)|
|22||It might cause damage to my body (e.g. brain,
|24||It could impair my reaction time, vision, or
|25||It could serve as a “gateway drug,” leading to
more dangerous drug use.
|27||It could lead to dependency or addiction.||0.76||−0.03||2.97 (1.68)|
|29||It could change how I act in social situations,
for the worse (e.g. it would make me socially
awkward, insensitive to others’ feelings).
|32||It may cause me to be a bad influence on
|34||It could make me feel “burnt out” or less
|36||It could damage my current relationships.||0.70||−0.11||3.11 (1.59)|
|38||It could cause me to make the wrong type of
|40||It could make me fail drug tests, which could
disqualify me from jobs or other activities.
|44||It could give me a bad image (e.g. labeled as a
|45||It could impair my judgment, which may
endanger myself or others.
|4||I would feel happy when I’m high.||−0.25||0.65||2.88 (1.30)|
|6||I would feel good when I’m high.||−0.28||0.69||3.12 (1.39)|
|8||It would relieve stress, anxiety, or worry.||−0.12||0.64||3.19 (1.39)|
|11||It could create opportunities for social
activities (e.g., meeting new people, bonding,
or spending time with friends).
|15||It could enhance reality or change the way I
see the world.
|17||It could enhance my creativity and the
creative process (e.g. making art or music,
|19||Everyday activities would be more enjoyable
(e.g. watching TV or movies, listening to
music, playing video games).
|21||It is something fun and exciting to do,
especially if I’m bored.
|23||It would make me more relaxed or calm.||−0.19||0.70||2.87 (1.29)|
|26||It would help me sleep.||−0.15||0.57||2.43 (1.37)|
|28||It would make food taste better.||−0.13||0.56||2.00 (1.30)|
|31||I find the culture of those who smoke
|35||It would make things funnier.||−0.14||0.66||3.04 (1.36)|
|37||It could help me have deep, new thoughts.||0.03||0.71||2.28 (1.26)|
|43||It’s an escape from reality and everyday life.||−0.02||0.63||2.50 (1.37)|
|46||It could improve sex.||0.00||0.57||2.17 (1.35)|
|Items omitted a|
|1||It seems like a “safe” way to get high.||−0.19||0.51||2.77 (1.34)|
|9||It’s expensive.||0.38||0.20||2.92 (1.45)|
|12||It smells bad.||0.52||−0.12||2.05 (1.39)|
|13||Using marijuana would make others see me
more positively (e.g. cool, fun, or sociable).
|14||It would give me the munchies (increase my
|30||It’s against my morals or values.||0.62||−0.31||2.90 (1.72)|
|33||It would make me feel more rebellious or
|39||My friends want or expect me to smoke.||0.18||0.38||1.87 (1.19)|
|41||It could help get rid of headaches.||0.09||0.51||1.85 (1.09)|
|42||It could make me tired, sleepy, lazy, or slow.||0.55||0.12||2.88 (1.46)|
|47||It would help me to concentrate and be more
Note. N = 260. Italicized factor loadings indicate that the item loaded significantly on a single factor.
|I would feel happy when I’m high. (3)|
|It would relieve stress, anxiety, or worry. (6)|
|It could create opportunities for social activities (e.g., meeting new people, bonding,
or spending time with friends). (9)
|Everyday activities would be more enjoyable (e.g. watching TV or movies, listening
to music, playing video games). (12)
|It is something fun and exciting to do, especially if I’m bored. (15)|
|It would make me more relaxed or calm. (18)|
|It would help me sleep. (21)|
|It would make things funnier. (24)|
|It’s illegal, and I could get caught. (1)|
|It’s not accepted or approved of by people who are important to me. (2)|
|It could impair my performance in my daily activities. (4)|
|It could reduce my ability to pay attention or remember things. (5)|
|It could make me feel bad physically (e.g. dry mouth, red eyes, racing heart). (7)|
|It could have unpleasant psychological effects (e.g. mood swings, depression,
|It could contain other drugs. (10)|
|It could impair my reaction time, vision, or perception. (11)|
|It could serve as a “gateway drug,” leading to more dangerous drug use. (13)|
|It could lead to dependency or addiction. (14)|
|It may cause me to be a bad influence on others. (16)|
|It could make me feel “burnt out” or less energetic. (17)|
|It could damage my current relationships. (19)|
|It could cause me to make the wrong type of friends. (20)|
|It could give me a bad image (e.g. labeled as a “pothead”). (22)|
|It could impair my judgment, which may endanger myself or others. (23)|
Note. Item numbers in parentheses correspond with items in Figure 1 .
Development of a Decisional Balance Scale for Young Adult Marijuana Use Abstract This study describes the development and validation of a decisional balance scale for marijuana use in young
Marijuana: Pros and Cons Cheat Sheet (DRAFT) by [deleted]
The Cons Of Recreational Marijuana
While the upside reasons to legalizing pot can sound pretty convincing, they completely ignore the obvious downsides. The overwhelming reasons to not allow legal marijuana are related to public health and the health of individuals. Marijuana is a mind-altering, addictive drug. Too many people forget this fact and falsely assume that marijuana is not that detrimental to health simply because it isn’t as bad as heroin, meth or cocaine.
Regular marijuana use leads to addiction in about 10 percent of those who partake. There are more people in rehab for marijuana addiction than for any other single drug. Even without addiction, marijuana use is detrimental to health. Smoking pot can cause lung conditions and memory loss. Using pot can also lead to accidents. Driving under the influence of marijuana is dangerous and there is no way yet to accurately determine when someone is too impaired to drive.
Of all the cons of legalizing marijuana, perhaps the most serious is the effect it could have on young people. Teens with developing brains stand to suffer the most from using this drug, and while laws will ban them from using it, everyone knows that legalizing the drug will only increase access for young people
Negative Effects of 420
Medical Marijuana – THC And CBD
Medical marijuana can provide lots of health benefits, especially if it is not consumed by smoking but by vaping or ingesting. In this case, the patient avoids getting lung and throat damage by the hot particles that happens with tobacco smoking
Marijuana contains two prominent active ingredients, THC (tetrahydrocannabinol) and CBD (cannabidiol). THC is largely responsible for the mind-altering effects closely associated with marijuana/cannabis use. CBD does not have any substantial mind-altering impact. Both THC and CBD have potential usefulness in a medical context
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Marijuana: Pros and Cons Cheat Sheet from [deleted].