Participants who felt that they did not look or act like members of their racial/ethnic group demonstrated increased odds of marijuana use. Young adults who feel marginalized by family members or friends may seek to find a way to belong and connect with other young adults and marijuana use may be a way to find belonging within a group. This parallels research, which suggests that the decision to engage in marijuana use comes from an internal need for emotional connection and friendship and as an opportunity to connect and create a sense of belonging . Other research has identified marijuana as a more acceptable substance viewed as superior and safer than other substances . Marijuana may be the substance of choice to build connection with others and combat feelings of intragroup marginalization. If marijuana use is perceived as a means for social connection, it may help to explain the findings between Factor 1 and cigar use. When controlling for demographic characteristics, participants who felt as though they did not look or act like members of their racial/ethnic group had decreased odds of cigar use. Cigars were the least frequently used product within the sample retained for analysis.National averages parallel this trend with current cigar use having lower prevalence than to cigarettes and marijuana for young adults .If marginalized young adults seek to connect with others via substance use, cigar use may not be the best mechanism by which to connect with others and therefore they may be less likely to use cigars. The combination of low rates of use and potential lack of opportunity to build social connection may help explain the decreased odds of cigar use. This finding is unexpected and further research is needed to better understand the relationship between intragroup marginalization and cigar use. Similarly, cigarette, e-cigarette, blunt use, pipp racking system and hookah use had lower rates compared to marijuana use. While unexpected, cigarette, e-cigarette, and blunt use were not associated with experiences of intragroup marginalization.This may be due in part to the lower rates of use.

It is worth noting that blunt use was examined independently, although it is often associated with marijuana use and in this sample most blunt users also reported concurrent marijuana use .Additionally, the use of these substances may be less tied to social use and therefore their use may not be linked to developing ways of belonging. Past research has differentiated between ‘social smoking’ and smoking alone . Studies have suggested that young adults not in college may be less likely to be social smokers and social smoking may not be prevalent across racial/ethnic groups .This study did not differentiate between social smoking and smoking alone and may be another important factor to better understand the role of intragroup marginalization and tobacco use. Intragroup marginalization was associated with higher hookah use; however, when controlling for race/ethnicity this association was no longer significant due to racial/ethnic differences. While hookah use has been noted as a means for socializing and is often smoked in a group setting , this may be population specific. Hookah use is common in Middle Eastern countries and has strong cultural underpinnings . Middle Eastern young adults experiencing intragroup marginalization may use hookah as a means to connect and fit in within their cultural group. Furthermore, African Americans have been found to have lower rates of use compared to other ethnic/racial groups . Additional research may be needed to further investigate differential impacts of intragroup marginalization on hookah use ethnic/racial group. Factor 1 captures the challenges young adults face when they feel they do not fit in with members of their ethnic/racial group. While Factor 1 focuses on difficulties in belonging and membership, Factor 2 centered on shared values and dreams. Feeling marginalized due to a lack of similar hopes and dreams was not associated with tobacco or marijuana use. This finding supports the theory that young adults use these substances as a means of building belonging and connection . While having dissimilar hopes and dreams may be stressful, it may not necessarily indicate one does have any connection to others.

Given these findings, the scale may be able to be further abbreviated by dropping Factor 2, particular when examining tobacco and marijuana use. Future research may be needed to further investigate impacts of Factor 2 on other health outcomes. Despite the strengths of this research, there are important limitations to note.This study focused on young adults in the San Francisco Bay Area, and findings may be not be generalizable to all young adults. However by using population-based sampling, we were able to obtain a representative sample, which past research has noted the difficulty in reaching urban young adults . This study also utilized a cross-sectional design, preventing any potential inference concerning causality. Tobacco and marijuana use were measured using self-report data and use was not biochemically verified. While past research has demonstrated the reliability and validity of self-reported smoking in anonymous surveys with young adults this validation has not extended to non-cigarette tobacco products;this may be a potential area for future research. This study examined intragroup marginalization among Mixed Race young adults; a population often overlooked in intragroup marginalization studies. Mixed Race participants were not required to identify which group served as the primary source of intragroup marginalization. However, it is possible that different cultural norms around tobacco and marijuana use could influence whether intragroup marginalization impacted behavior. Oyserman and colleagues have demonstrated the identity-based motivation of health behaviors, with racial/ ethnic minorities more likely to identify unhealthy behaviors with their group. Additional research may be needed with Mixed Race individuals to better understand how different groups may impact the relationships between intragroup marginalization and tobacco use. A final limitation is that we did not directly assess reasons or motivations for use. Future qualitative research is needed to explicitly examine motivations for use as a result of experiences of intragroup marginalization. This study provides the first quantitative examination of intragroup marginalization with tobacco and marijuana use. Results respond to recent calls to better understand motivations for young adult marijuana use , with findings demonstrating an association between intragroup marginalization and increased marijuana use.These findings are especially relevant given the changing climate regarding the legalization of marijuana, with California just recently voting to legalize marijuana . Results reaffirm existing arguments that drug policy must attend to the social and cultural contexts of use Additionally, findings respond to existing calls in the literature to better understand how culture impacts use . Past intervention research has highlighted the importance of attending to peer smoking behavior and norms, providing further support for the need to attend to social dynamics when addressing young adult tobacco and marijuana use . Additional research is needed to further investigate the relationship between intragroup marginalization and marijuana use, which can help in the tailoring and development of targeted health education programs.The period from middle school to high school is associated with important developmental changes that occur physically, socially and mentally . Initiation of alcohol and/or marijuana during this time period can significantly affect functioning, especially if youth initiate at a younger age. For example, AM use during this time period is associated with academic problems, poorer mental health, use of other illicit drugs in the future , and a higher likelihood of abuse or dependence in adulthood . Furthermore, given that the brain is still developing, adolescents can still have memory, attention, and reaction time deficits even after they stop using compared to youth who have never used AM . Studies in the United States examining trajectories of alcohol use during adolescence have shown a consistent pattern.

Initiation typically occurs in early adolescence , with drinking rates increasing steadily during mid- and late adolescence before peaking in early young adulthood . Marijuana use trajectories follow a similar pattern, albeit with a later average age of initiation . However, pipp vertical racks not all individuals follow this general pattern; thus research has focused on identifying distinct developmental trajectories of AM use. Most of these studies identify a group of persistent or high users, a declining group where use starts off heavy and gradually declines over time, an increasing group where use gradually escalates over time, and a moderate/infrequent group that uses occasionally over time . Persistent or high AM users typically have the worst outcomes . For example, youth in high marijuana use groups during high school also reported higher rates of both mental health and drug problems at age 21 ; membership in higher alcohol use groups in 6th grade was associated with greater use of other substances and violent behavior in 8th grade ; youth in the heaviest drinking trajectory group at age 18 had more problems with verbal memory and monitoring two years later ; and youth who initiated alcohol and cigarettes concurrently early on reported worse physical health, a higher likelihood of selling drugs and the highest rates of self-reported problems compared to groups that did not initiate use in early adolescence . Few studies have examined trajectories of alcohol and other drug use among diverse ethnic and racial samples across middle school and high school . However, the face of the United States is changing. During the next 15 years, Asian American, Hispanic American, African American, and Native American populations are expected to rapidly grow in size, with each of these cultures subsequently comprising a significant proportion of the nation . In addition, multi-racial Americans are the fastest growing population under age 18 . Research has shown that non-Whites often have worse health outcomes and more interpersonal problems and other negative outcomes from AOD use compared to Whites, even with less AOD use. To date, there are no studies that longitudinally address when these disparities may start; for example, whether we may see disparities in functioning due to AOD use begin as early as adolescence. It is imperative that we assess when disparities in functioning may begin to occur and in what domains so that clinicians and providers can better determine the best time and way to intervene. A small body of research has assessed racial/ethnic differences in AOD use; however, studies typically focus on one substance and do not address potential disparities in outcomes. Results indicate that Whites and Hispanics are more likely than Blacks and Asians to drink alcohol , smoke cigarettes , and use marijuana . Four recent studies have examined racial/ethnic differences in more than one substance across adolescence into adulthood . They all used data from the National Longitudinal Study of Adolescents and Adults to examine use of cigarettes, alcohol, and marijuana. Although these studies examined use from adolescence to adulthood, they only had four waves of data that were spaced over a 14 year period. Setoh and colleagues examined differences between Whites and Hispanics; Keyes et al. compared Whites and Blacks; Chen and Jacobson compared Whites, Blacks, Hispanics and Asians; and Evans-Polce et al. examined differences between Whites, Hispanics and Blacks. Three studies found that White youth had higher rates of AOD use initially and increased their use more rapidly over time than non-White youth; however, racial/ethnic differences lessened as youth aged indicating that non-White youth “caught up” in their 20’s and 30’s. Chen and Jacobson found that Hispanics had the highest rate of use for all substances at age 12, with Whites increasing the most rapidly, and peak levels of use for Blacks occurring at later ages. These studies have significantly advanced our knowledge in this area; however, several gaps need to be addressed. First, few longitudinal studies examine trajectories for more than one substance. Given that AM are the two substances initiated and used most frequently during adolescence, it is important to examine how trajectories of AM use during this time period may differentially affect outcomes. Second, none of these studies measure AM use with regular assessments during both middle school and high school. These are important developmental time periods to measure consistently. More regular assessments allow examination across critical transitions, including from age 11 to 14 when use rates increase dramatically , and from 14 to 17 when youth begin to gain more independence from parents and may have more opportunities to engage in risk behaviors . Third, studies tend to focus on differences between just a few racial/ethnic groups.

Some of these factors were also supported in previous research examining parental perceptions and beliefs of marijuana use and discussions with their children , and now could extend to include unhealthy eating discussions as well. By addressing these constructs, we were able to propose an adapted theoretical model that could possibly decrease unhealthy eating and marijuana use in children and adolescents. According to descriptive analyses, a good proportion of parents had trouble engaging in discussions with their children centered on unhealthy eating and marijuana use . As a result, this project further proposed and developed three parenting framed messages aimed at promoting parent and child discussion of the health risk behaviors . The authoritative parenting-framed message was rated as most effective in motivating discussions about unhealthy eating and marijuana use compared to authoritarian parenting-framed message and permissive parenting-framed message. Relatedly, several studies have found that authoritative parenting styles are associated with decreased health risk behaviors in adolescents, including lower use of alcohol, tobacco, and illicit drugs . These conclusions were utilized to develop discussion tools that included the authoritatively-framed message, in order to further motivate effective discussions about unhealthy eating and marijuana use between parents and their children . It is believed that parents who struggle to have these discussions might benefit from tools on how to have these conversations with their children.The study findings partially supported the moderating effects of parenting styles on discussion tools and some of the cognitive factors. Future studies could target the implementation of additional discussion tools, aside from unhealthy eating and marijuana use discussion tools.

This could gather information on ways to improve the utility of the authoritative parenting-framed message within the context of the discussion tool by closely examining the effects on several health-risk behaviors. It could be that some behaviors respond better to the authoritative parenting component compared to others. Although, vertical air solutions these tools were still effective in motivating discussions, and so, the discussion tools could be modified and tested in a bigger sample.Limitations of the present research project requires consideration when interpreting the results and point to directions for future research. Initially, the results may not be representative of all parents across the nation or in other countries, as Study 1 and 3 consisted predominantly of Non-Hispanic White and well-educated participants, whereas Study 2 consisted of participants between the ages of 18 to 20 years old. More research is needed to measure the practicality of the discussion tools with more ethnically diverse groups. With regards to marijuana use, utilizing random samples of parents in the United States could prove to be beneficial in interpreting results, as the landscape is constantly changing with regards to marijuana legalization laws, and could impact discussion of marijuana use between parents and their children. Another important limitation to consider is the lack of fathers within the samples. Fathers may have differing responses to the discussion tools compared to mothers. As noted by the participant characteristics , the parents were primarily mothers of children ages 10 to 17 years old and 80% of responses by children were about their mothers . It warrants the need to investigate the impact of perceived parenting styles of fathers on motivating discussion of unhealthy eating and marijuana use. The comparability of responses across mothers and fathers could lead to the incorporation of richer parenting practices to consider when designing future discussion tools. Moreover, all three studies relied on self-report on specific questions, which may lead to bias of participant responses.

The partial support of the moderating effects of parenting styles indicates the need for further investigation of how these practices could influence discussion outcomes, and copiously recognize the implications in promoting future discussions about unhealthy eating and marijuana use. Additional studies of parents of children in younger age groups , and children of younger age groups are needed. This could provide more information on what elements are most effective when designing discussion tools for varying child age groups, and if differences exist. The present research project provided preliminary findings to consider on the moderating effects of authoritative, authoritarian, and permissive parenting styles, and could be expanded to include additional traits and temperaments specific to parents and children. It may be beneficial to measure more acceptability items after participants view the discussion tools, by including more items than perceived effectiveness, perceived interpretability, motivations to address the behavior after the discussion tools, in order to provide additional ways of testing the effectiveness of the tools. This could possibly elicit richer qualitative data onthe differing effects of the discussion tools on parents and their children. By identifying key words and phrases that were utilized in their discussions, it could lead to the consideration of other parent-child practices or characteristics that could influence the discussions of these behaviors.To conclude, the present research project explored the associations of a revised theoretical framework of parenting styles and PWM factors on parent motivations to discuss unhealthy eating and marijuana use with their child , tested the acceptability of authoritative parenting-framed messages of unhealthy eating and marijuana use by youth , and then, designed and developed discussion tools to encourage parent conversations about unhealthy eating and marijuana use with their child . This study contributes new data, in a sample of American parents with children between the ages of 10 to 17 years old, on the moderating effects of the discussion tools about unhealthy eating and marijuana use in parent-child discussions about these behaviors. The partial support of the moderating effects of parenting styles validates the need for further exploration of how these qualities could impact discussion outcomes of health risk behaviors.

Subsequent research should be directed at a longitudinal, nationwide study on whether parental motivations to discuss unhealthy eating and marijuana with their children could extend to random samples of parents in the United States and elsewhere. Additional studies could also include other risky behaviors in order to test whether authoritatively-framed discussion tools extend to other behaviors as well.Marijuana is the most widely used illicit substance worldwide . In 2010, more US high school students used marijuana in the prior 30 days than tobacco . Co-use with tobacco is of increasing interest . Smoking marijuana with tobacco, either in a tobacco leaf or mixed with tobacco, is an increasingly common practice among adolescents thought by some users to prolong the effects and/or increase the high from marijuana . A recent national online, anonymous survey of young smokers reported that roughly half also smoked marijuana in the past 30 days . Co-use of marijuana and tobacco may contribute to the development of nicotine dependence and thus, is an important area of research for the investigation. Adult co-users of tobacco and marijuana have an increased risk of developing nicotine dependence and have worse tobacco cessation outcomes . While overall rates of tobacco use and co-use with marijuana are lower in adolescents compared with adults , most addicted adults develop nicotine dependence during adolescence. Therefore, vertical weed grow adolescence is a critical period to study the effects of marijuana on tobacco.Although the transition from experimentation with tobacco to addiction is likely multifactorial, marijuana use may play a role for some adolescents and has been identified as a risk for nicotine addiction in a study of young adults . Possible mechanisms of action include common routes of administration ; hence, one behavior may reinforce the other. Furthermore, both nicotine and cannabis affect similar pathways within the mesolimbic addiction pathways, suggesting similar and overlapping mechanisms for addiction . Finally, smoking cues are also similar between the two substances, which may contribute to the poorer tobacco cessation outcomes observed in adult co-users of marijuana . Despite the increasing prevalence of marijuana use in adolescents, particularly among smokers, and evidence of harm from marijuana-tobacco co-use in adults, little is known about the interaction between marijuana and tobacco in adolescents. The goal of this study was to examine the severity of nicotine addiction among teen smokers as a function of co-occurring marijuana use. Given the literature on adult smokers, we hypothesized marijuana would contribute to symptoms of nicotine dependence among adolescents.Adolescents between the ages of 13-17 from the San Francisco Bay Area who smoked at least 1 cigarette in the past 30 days were recruited as part of an ongoing smoking trajectory study detailed elsewhere . Adolescents responding to online, school and clinic based advertising were invited to complete the study visit. Females with positive pregnancy tests were excluded from the study.Adolescent tobacco smokers completed in-person surveys of smoking behaviors and dependence scales. Tobacco use was measured by asking adolescents how many cigarettes they smoked on each day of the week. Participants who reported smoking on fewer than 30 of the previous 30 days were considered intermittent smokers .

Given the lack of consensus regarding optimal measurement of nicotine dependence in adolescents, the study administered the following four measures at study entry: the modified Fagerström Tolerance Questionnaire , the Hooked on Nicotine Checklist , the Nicotine Dependence Syndrome Scale , and the International statistical classification of diseases and related health problems, 10th revision criteria for nicotine dependence . All of the nicotinedependence measures were scored continuously with the total score on each measure used to quantify nicotine addiction.Frequency of marijuana use was categorized as: 1) never or no use in past 3 months, 2) Once a month or less plus once a week or less, 3) one or more times a week, and 4) every day. Spearman’s rho correlations examined associations between frequency of our ordinal measure of marijuana use with demographic variables, cigarettes per day and alcohol use. To examine the association between marijuana and measures of nicotine addiction , we ran general linear models with key variables that in the literature have been associated with nicotine addiction and marijuana use .Two hundred adolescents were consented into the study and completed the baseline visit. Of those, 28 denied smoking cigarettes in the past 30 days and 7 declined to answer the question about marijuana use and were thus excluded from the analysis. The resulting sample had a mean age of 16.1 years and was racially diverse, with 28% participants identifying as White, 19% African American, 19% Hispanic and 34% other. Participants averaged 3.01 CPD for a duration of 1.98 years . Fifty-one participants reported daily cigarette smoking and 111 reported non-daily smoking . Mean scores were 2.56 on the mFTQ , 4.52 on the HONC , -1.75 on the NDSS , and 10.13 on the ICD-10 . Most participants reported marijuana use in the past 30 days with 43 using weekly, and 62 reporting daily use. Frequency of marijuana use was correlated with CPD , but not with the frequency of alcohol use . Participant CESD scores were not associated with frequency of marijuana use or cigarette use . In general linear models controlling for age, years of smoking, and daily versus non-daily smoking, frequency of marijuana use was significantly and positively associated with nicotine addiction . The findings were consistent across all four measures of dependence and remained significant for the mFTQ after removing the question on CPD. When examining the NDSS subscales, only the drive and priority subscales were significantly associated with marijuana frequency. Older age, more years smoking, and daily smoking were associated with greater nicotine dependence in all models. The total percent of variance predicted ranged from 25% for the HONC to 44% for the mFTQ and NDSS. Illicit drug use may co-occur across substances, and follow-up analyses sought to examine whether the finding of an association with nicotine dependence was specific to marijuana. Therefore, we also assessed co-use with other illicit substances. In the past 3 months, 40 participants reported ecstasy use. A small number of participants reported use of cocaine/crack , methamphetamine , mushrooms/ mescaline , heroin , Percocet/Vicodin , or LSD , preventing inclusion in analyses. Ecstasy, included as a covariate in the fully adjusted general linear models, was not a significant contributor with p-values ranging from .24-.99 and the effects for marijuana remained largely unchanged.Marijuana smoking was prevalent in this adolescent sample of tobacco smokers: 80% reported past month marijuana use and more than a third smoked marijuana daily. Notably, among adolescent tobacco smokers who also smoked marijuana, the frequency of marijuana use was associated with greater levels of nicotine addiction on all three major scales used in studies with adolescents plus the ICD-10.

A significant association for only unhealthy eating was for lower authoritarian parenting style and higher self-efficacy of unhealthy eating, which is consistent with previous literature testing these relationships in similar contexts . Greater perceived risks of harms of unhealthy eating and marijuana use were associated with higher coherence and higher worry of unhealthy eating and marijuana use. These findings are consistent with, and extend, prior research on the influence of higher perceived risks of the harms of marijuana use in predicting higher worry of marijuana use , and now can extend to unhealthy eating. For unhealthy eating, another significant association included more negative prototypes of unhealthy eating and higher worry of unhealthy eating. Relatedly, negative prototypes have been shown to predict higher worry about one’s child using marijuana . Interestingly, there was not an association between negative prototypes of marijuana users and worry of marijuana use in this study, however, there was a positive association with higher coherence of marijuana use. Parental worry of unhealthy eating and marijuana use were positively associated with stronger intention motivations to discuss these behaviors with their child. This adds to prior research of worry in motivating a protective response , e.g., discouraging marijuana use . For unhealthy eating, there was also an unpredicted association between authoritative parenting style with intentions to discuss unhealthy eating with child. While for marijuana use, vertical growing weed higher coherence was associated with discussion intentions of marijuana use, as well as an unpredicted association with more parent-child communication, and greater levels of perceived risks and coherence of marijuana use.

These findings provide further support for the positive relationship of intention motivations in predicting discussions of unhealthy eating and marijuana use. There was also a significant association between higher worry of unhealthy eating with higher willingness to discuss unhealthy eating with one’s child. Additionally, unpredicted associations of higher levels of parent-child communication and perceived risks of harms of unhealthy eating, and more negative prototypes of unhealthy eating were positively associated with discussion willingness of unhealthy eating. For marijuana use, there was also an unpredicted association for higher self efficacy of marijuana use with higher willingness to discuss marijuana use with child. For intentions of unhealthy eating and marijuana use, there was a positive association with past discussion of unhealthy eating and marijuana use. These findings are in line with the substantial body of evidence that intentions are associated with health related behaviors . The predicted path of willingness did not associate with past discussion of unhealthy eating or marijuana use. Previously, willingness has been found to predict risky health behaviors , however, this might not extend to the behavior of discussing unhealthy eating and marijuana use with one’s child. Other studies have also found this to be true for willingness and discussion behavior path for marijuana use . One possibility is that parents may be more likely to participate in premeditated discussions with their child about health-related behaviors as compared with impulsive discussions . There were also unpredicted associations of higher levels of authoritative and authoritarian parenting styles, and more negative prototypes of unhealthy eating with past discussion of unhealthy eating with child. While, greater perceived risks of harms and coherence of marijuana use were associated with past discussion of marijuana use with child.

These present study findings can possibly serve as useful standards for developing discussion tools that include measures of parenting styles and PWM framework factors, in an effort to assist discussions of unhealthy eating and marijuana use with one’s child. Since, attachment styles did not predict any of the behaviors, it will not be included in the parenting-framed messages developed in Study 2 or the tools developed in Study 3. Although, they did have associations with parenting styles , and this association will be tested with correlational analyses in Study 2. These results support the potential utility of framing discussion tools with authoritative parenting style, but given that authoritarian style was also associated with motivations for discussion behaviors, it could be that an authoritarian-framed message might be helpful as well. Therefore, all three of the parenting styles will be further tested in Study 2 with youth. Lastly, this study evaluates child-age group differences in discussions of unhealthy eating and marijuana use . Discussion levels varied by child’s age, with parents of younger children discussing unhealthy eating, and parents of older children discussing marijuana use. With support of our hypothesis, it could be that parents may not discuss unhealthy eating with older kids, as they may feel that they are independent and can make their own decisions . However, adolescents across all age groups are likely to eat unhealthy , and respond well to recommendations on diet . In contrast, parents may be less likely to think that their children use marijuana at younger ages. In recent years, marijuana initiation is more likely to begin at younger ages with a decrease in perceived likelihood of harm of marijuana use . Given the changing landscape of marijuana legalization, it is imperative to continue to consider all age groups. Therefore, implementing a discussion tool that could provide parents with the necessary guidance to engage in communication about risky behaviors, regardless of their child’s age group, is an important first step.

Strengths of the present study include its focus on parent motivations to discuss unhealthy eating and marijuana use and its contributions to further inclusion of the parenting styles and revised PWM factors in motivating discussions about these behaviors, in a sample of parents in the United States. The association of parenting styles in motivating protective responses is essential to consider for development of discussion tools, and will be further tested in Study 2 and 3. Another strength is the use of MTurk, which has become a popular method used for recruiting large heterogeneous samples such as parents of adolescents from across the nation as has been demonstrated in several published psychological studies . Limitations of this study require consideration when interpreting the results and point to directions for future research. First, the results may not be representative of all parents across the nation or in other countries, as it consisted predominantly of NonHispanic White and well-educated participants. Second, the findings may not be generalizable to all parents, particularly as we focused primarily on parents of children ages 10 to 17 years old. Further research utilizing random samples of parents in the United States is needed especially with the changing landscape of marijuana legalization laws. A last limitation is that discussion behavior is measured as a past behavior rather than future behavior. However, the observed relationships of predictor variables with past behavior are likely to hold for future behavior as an individual’s behavior is fairly consistent, and typical behaviors, are more predictive than uncommon behaviors . Nevertheless, additional research is needed to test the predictive associations of the PWM factors on discussion behavior in the future.This chapter begins with a description of Study 2 including aims and hypotheses; methods ; discussion of manipulations ; detailed list of measures; overview of statistical analyses; results, discussion, and conclusion of study. Results from Study 1 suggested that parent motivations to discuss unhealthy eating and marijuana use with their child may be influenced by their parenting dynamics , and other cognitive factors . There is a need to develop a discussion tool that could be used by parents to engage in discussions with their children centered on these behaviors. One of the unique aspects of this tool is the inclusion of parenting-framed messages that was developed using characteristics of the parenting styles of authoritative, authoritarian, and permissive . These messages were assessed in order to figure out which parenting framed message was rated most effective to be used in the discussion tool for Study 3 with parents. The parenting-framed messages were developed by an attribute list of the three parenting styles so that each characteristic was addressed with each respective message . Prior to testing these parenting-framed messages with parents, commercial cannabis growers it is important to test the acceptability of these messages with youth. The focus is on youth ages 18 to 20 years old, as this age group is close to minors. More so, they will better articulate responses to parenting-framed messages with a more enhanced perspective as compared to a younger age group.

Importantly, Study 2 tests the receptivity by a child to the parenting-framed message based on the perceived parenting styles of one’s parent, whereas Study 3 tests the receptivity of the parent to use the discussion tools of unhealthy eating and marijuana use based on the parent’s perceived parenting style. This study also tests the associations of the attachment styles and parenting styles as perceived about one’s parent. Therefore, in Study 2 we assessed the parenting styles of one’s parent, while in Study 1 and Study 3 we assessed parenting styles with one’s child. Since, there may be variation across parent and child populations with regards to reports on parents’ parenting styles, it was important to test them both. The study aims were to: test the associations of the perceived attachment styles with a parent and the perceived parenting styles of the parent; test the associations of attachment styles and parenting styles on perceived effectiveness, perceived interpretability, motivations to discuss behavior, and discussion similarity; and test the relationship of parenting-framed messages of unhealthy eating and marijuana use on perceived effectiveness, perceived interpretability, motivations to discuss behavior, and discussion similarity. For Aim 1, we tested hypotheses that: lower attachment anxiety and lower attachment avoidance will be associated with higher authoritative parenting style; and higher attachment anxiety and higher attachment avoidance will be associated with higher authoritarian parenting style and permissive parenting style. For Aim 2, we tested hypotheses that: lower attachment anxiety, lower attachment avoidance, and higher authoritative parenting style will be associated with higher perceived effectiveness, higher perceived interpretability, higher motivations to discuss behavior, and higher discussion similarity for authoritative messages of unhealthy eating and marijuana use compared to lower authoritarian parenting style and lower permissive parenting style; and higher attachment anxiety, higher attachment avoidance, higher authoritarian parenting style, and higher permissive parenting style will be associated with higher perceived effectiveness, higher perceived interpretability, higher motivations to discuss behavior, and higher discussion similarity for authoritarian and permissive messages of unhealthy eating and marijuana compared to lower authoritative parenting style. For Aim 3, we tested hypotheses that: higher perceived effectiveness, higher perceived interpretability, higher motivations to discuss behavior, and higher discussion similarity will be associated with higher authoritative parenting-framed messages for unhealthy eating and marijuana use compared to lower authoritarian parenting-framed messages and lower permissive parenting-framed messages; and these message differences will be stronger for authoritative parenting style than for authoritarian parenting style or permissive parenting style.2.1. Participants The university’s institutional review board approved the study protocol. Participants were recruited through the university online research participation site to undergraduate students who are between the ages of 18 to 20 years old at the University of California, Merced. In total, 393 participants provided informed consent and were able to complete the study. Overall, participants were approximately 19 years of age on average and predominantly Hispanic with over 72% identifying as women and lower classmen . 2.2. Design The study utilized a 3 X 2 within-subjects design, with parenting-framed messages and the conditions of unhealthy eating and marijuana use. After completing measures of demographic and personal characteristics, and unhealthy eating and marijuana use, participants viewed a series of parenting-framed messages through counterbalancing. 2.3. Procedure Participants responded to questions about measures of demographic and personal characteristics, unhealthy eating and marijuana use, attachment and parenting styles, and parenting-framed messages. Participants viewed a total of six messages, and then rated the messages on perceived effectiveness, perceived interpretability, and motivations to discuss the behavior, and discussion similarity. The six messages were counterbalanced, with every participant viewing all three parenting-style message within in each of the two behavior conditions. Participants were randomly assigned to one of two behavior conditions , which were presented in random order to each group. For instance, one group was tested with unhealthy eating followed by marijuana use, and the second group was tested with marijuana use followed by unhealthy eating, and vice versa. Following the survey completion, participants read a brief explanation of the study and received links to websites of national health organizations with information about unhealthy eating and marijuana use. They then received SONA credit for their participation in the survey.

As part of the broader study, at baseline and annual follow-up visits, participants completed a comprehensive battery including neuropsychological assessment , self-reports of behavior, psychiatric symptoms and substance use, and a multimodal neuroimaging session . Exclusionary criteria for study entry included current use of psychoactive medication, current or persistent major Axis I psychiatric disorders, significant learning or developmental disorders and serious major medical conditions . While a majority of participants had limited drug and alcohol exposure at enrollment, a small proportion were recruited who exceeded age-specific alcohol and marijuana low-use thresholds . Additional recruitment, demographic and procedural details have been published previous . The NCANDA project employs an accelerated longitudinal design, and the current study used all available data across the first seven waves of data collection, from November 2012 to December 2020.To measure the Big Five personality dimensions, participants were administered the TenItem Personality Inventory , a brief measure shown to have convergence with longer Big Five measures as well as good test-retest reliability . The TIPI consists of 10 questions, with two questions for each subscale: extraversion, agreeableness, conscientiousness, emotional stability, and openness. Each question asked participants to rate on a scale of 1 to 7 how much a pair of words applied to them. The responses on the two questions for each subscale were averaged and served as the primary outcome measure for all analyses. Due to protocol changes during the fifth wave of data collection, pipp racks the study moved from administering the TIPI during all annual visits, to only administering it if participants were completing their age 24 or 27 visit .

Alcohol and marijuana use were measured using the Customary Drinking and Drug Use Record . At all visits, participants self-reported the number of days they drank and used marijuana during the past year. That is, participants were asked: “During the past year, , how many days did you drink alcohol?”, with an identical question asked regarding marijuana use. Due to non-normal distributions, past-year alcohol and marijuana use variables were log-transformed prior to all analyses.Previous studies investigating the development of personality across adolescence often used linear growth models with polynomial effects . However, when examining development across a broad age range, it is possible that data do not always conform to this restricted parametric growth model, and when examining group-level effects , different groups may not necessarily demonstrate similar developmental trajectories. Generalized Additive Mixed Models , an extension of generalized linear mixed models, do not assume the shape of developmental growth a priori, but instead allow for age-related non-linear smooth functions that best represent the relationship between predictor variables and outcomes . Similar to traditional linear mixedeffects models , GAMMs allow for appropriate modeling of the within-subject correlation of longitudinal data, as well as other important random effects . Here, we modeled changes in personality development as a function of sex using both GAMMs and LMEs, and present findings side-by-side in order to assessthe impact of modeling choice. All tested models can be found in Table S1. Analyses were conducted using R 4.1.1 .Generalized Additive Mixed-effects Models —To assess the effects of age and sex on personality development, we fit GAMMs using the ‘mgcv’ package in R and carried out a series of model comparisons, similar to the approach taken in recent neurodevelopmental studies . For each of the 5 TIPI scales, we fit three successive models that included age-related development across the whole sample , a main effect of sex , and differences in the age-related personality development by sex . All models included a random intercept per participant, family, and data collection sites.

When interpreting sex effects on personality development, it is important to note when first fitting Model 3, sex was included as a ‘factor’, resulting in the estimation of a separate smoothed age trajectory in male and female participants. While this has the benefit of producing interpretable smoothed terms for each group, a traditional interaction term, such as that seen in linear modeling, is not produced. Therefore, to test the statistical significance of sex-specific developmental trajectories, standard hypothesis testing was used to compare the log-likelihood values from each model . Then, to provide additional statistical support, Model 3 was refit with sex coded as an ‘ordered factor.’ Here, a smoothed age trajectory is calculated for the ‘reference’ group only , and a smooth term representing the difference between the developmental trajectories of the reference group and the other group was estimated. While this method provides less information regarding the shape of the age-related trajectory for each group, it produces an estimate and significance-testing for the difference between groups, akin to traditional linear interaction terms, and has also been used previously in developmental studies . Finally, to assess the association between substance use and personality, we modeled time invariant , linear time-varying , and quadratic time-varying associations of past year alcohol and marijuana use. These three potential associations could occur for alcohol use, marijuana use, or both, resulting in a combination of nine different models . Additionally, to capture potential sex-specific associations of alcohol use, marijuana use, our both, a total 27 additional models were necessary to exhaustively explore these relationships . These predictors were added to the best fitting developmental model compared using standard hypothesis testing. Linear Mixed-effects Models—To compare developmental GAMM results to models with more traditional polynomial growth parameters, we fit a series of LME models. Unlike GAMMs, which allow for the assessment of sex differences in non-linear personality development with only 3 models, the LME framework requires the iterative addition of consecutive higher-order polynomial age predictors to statistically assess the benefit of added model complexity. Here, we chose to assess the effect of 3 orders of polynomial effects , along with their potential interaction with sex, using the ‘nlme’ package in R . Starting with a linear age effect, we compared three successive models to assess the pattern of age-related development across the whole sample , a main effect of sex , and differences in the age-related effects by sex . This process was then repeated for quadratic , cubic polynomial age effects. For each interaction model, the effect of sex was assumed to interact with all lower-order polynomial age effects. Finally, the best fitting model for each polynomial age effect was then compared, to determine the final model. Identical to GAMMs, all models included a random intercept per participant, family, and data collection sites. Briefly, female and male participants identified as either white non-Hispanic , African American/Black , Hispanic/ Latino , Asian , multi-racial , Pacific Islander , or Native American/ American Indian . At baseline, 20% reported parents with education below a college degree, 27% with at least one parent attaining a college degree, and 53% with at least one parent with education greater than a college degree; annual family income ranged from below $12,000 to greater than $200,000. The TIPI was completed during at least one visit for 829 of the 831 subjects, with a total of 3,402 case observations across the 7 waves. However, 24 cases included incomplete reporting of substance use measures, and 4 cases included inconsistencies in reported substance use . To provide direct statistical comparison of nested models, only subjects’ timepoints with complete data were included in the final analyses.

Notably, all developmental findings remain unchanged when those timepoints with missing substance use values were included. In total 3,374 case observations across 829 subjects were included in final analyses; the breakdown by wave follows: Baseline , Year 1 , Year 2 , Year 3 , Year 4 , Year 5 , Year 6 . As expected alcohol and marijuana use both increased with age . Overall, 68% of the sample reporting drinking, and 48% of the sample reported using marijuana during at least one wave of data collection. Of those reporting substance use, vertical growing racks over the course of the study to-date, past year alcohol use ranged from 1 to 365 days with an average of 28.5 days per year, and past year marijuana use ranged from 1 to 365 days with an average of 49.1 days per year. Fit statistics and model comparisons for GAMMs examining age- and sex-related effects on personality development, and the association between personality and past year substance use can be found in Table S2. Parameter estimates of the final best-fitting GAMMs can be found in Table 1. All significant findings reported herein are from the final best fitting models, including the effects of past year alcohol and marijuana use. For models with sex-by-age and/or substance use-by-age interactions, models were refit with their intercepts adjusted to ages 13, 16, 19, 22 and 25 in order to provide added interpretation to underlying main-effects of sex and substance use. In the absence of standard parametric age coefficients in GAMMs, we report the effective degrees of freedom , which sheds light on the degree of nonlinearity for a given developmental trajectory . Effect sizes for all parametric coefficients are reported as standardized regression coefficients for continuous predictors and Cohen’s d for categorical predictors .The current study sought to flexibly model developmental trajectories of personality in adolescence and young adulthood as a function of sex and explore the association between substance use and personality across age. We report three general conclusions: 1) there were linear increases in agreeableness and conscientious and decreases in openness, across this age range, the slope of which did not differ developmentally by sex, and significant sex-specific non-linear developmental differences in extraversion and emotional stability; 2) male participants reported lower agreeableness, conscientiousness,and openness across the entire age range, less extraversion at all ages except during midadolescence , and more emotional stability in all but early adolescence ; 3) alcohol use was associated with greater extraversion and openness across the entire age range, and less conscientiousness in adolescence , while marijuana use was associated with less agreeableness throughout the entire age range, less conscientiousness in early adolescence and young adulthood , less extraversion in young adulthood , and less emotional stability throughout the entire developmental age range in female youth, and in young adulthood in male youth. Developmentally, our findings provide partial support for the maturity principle , as we found both conscientiousness and agreeableness to increase linearly from ages 12 to 25. This is consistent with at least one report that found agreeableness and conscientiousness increased consistently across adolescence and young adulthood , with non-linear effects occurring primarily in other traits . Meanwhile, another study found the lowest levels of agreeableness and conscientiousness occurred around ages 12–13 . Our data provide strong replication of these results, in a large multi-site cohort, and suggest that any “disruptions” seen in agreeableness and conscientiousness may take place during childhood, prior to their continued maturation in adolescence and young adulthood. Contrary to this effect, we note decreases in openness across the entire age range. While openness has been shown to decline in late childhood and early adolescence , there is no evidence, to our knowledge, of self-reported decreases in openness in late adolescence and young adulthood, though parent-reported adolescent personality findings suggest decreases in openness in this age range . Interestingly, out of all five personality traits, when assessed in adolescence, openness has been shown to have the lowest internal consistency, and replicability across multiple samples and cultures . Thus, it’s possible that our 10-item question of personality could be less sensitive to true mean-level changes in openness in this population. Our findings also provide partial support for the disruption hypothesis , as we found extraversion in both male and female youth, and emotional stability in female youth, decreased in early adolescence . However, unlike previous reports , we found extraversion never increased during late adolescence and young adulthood. Instead, male participants continued to show linear declines in extraversion, while female participants showed a leveling off of extraversion. This is where we believe our flexible analytic strategy helps clarify past results. For example, Borghuis, Denissen et al. tested only linear and quadratic growth parameters, and found U-shaped trajectories for extraversion. Similarly, when quadratic growth parameters were fit to our data , we replicated this previously observed effect in female participants; however, more flexible modeling suggest, this quadratic growth does not best fit the data.

Cumulative marijuana use over the 8-year follow-up period significantly predicted attention performance above and beyond effects accounted for by baseline attention scores, age, and practice effects. Another longitudinal investigation that covaried for baseline functioning before marijuana initiation found that, among individuals with prenatal exposure to cannabis, heavy marijuana users demonstrated poorer overall IQ, processing speed, and immediate and delayed memory compared with controls. One critique of previous research is that the observed neuropsychological deficits may be due to polysubstance use , family history of substance use disorders , or comorbid psychiatric disorders . Furthermore, cognitive deficits among marijuana users may be attributable to acute or subacute cannabis withdrawal . Therefore, the goal of this study was to characterize the neuropsychological effects of adolescent marijuana users without comorbid psychiatric disorders after approximately 1 month of abstinence. It was hypothesized that adolescent marijuana users would demonstrate significantly poorer cognitive function in areas associated with frontal, cerebellar, and hippocampal functioning , including processing speed,complex attention, new learning, and executive function compared with demographically similar control adolescents following at least 23 days of monitored abstinence. Adolescents were primarily recruited from local high schools and universities via distribution of flyers and ads. To assess for study eligibility, comprehensive telephone screening measures were administered to both adolescents and parents0 guardians. Inclusion criteria required that youth were between 16 and 18 years of age, vertical farming equipment fluent in English, and had a parent or legal guardian available to consent and provide medical and psychiatric history.

Exclusionary criteria included history of Diagnostic and Statistical Manual for Mental Disorders-Fourth Edition Axis I disorder or use of psychoactive medications; history of chronic medical illness, neurological condition , or head trauma with loss of consciousness . 2 min; significant prenatal alcohol or drug exposure; complicated delivery or premature birth ; learning disability or mental retardation; first-degree relative with history of bipolar I or psychotic disorders; left-handedness; and non-correctable vision, colorblindness, or hearing impairments. If at any time during the 28-day abstinence period a teen reported or tested positive for any substance use, he0she was excluded from study and not included in any data analyses . All participants and their parents0guardians underwent written informed consent in accordance with the University of California, San Diego Human Research Protections Program. Teens were classified into two groups: a marijuana using or a drug-free group. A priori classification criteria for the MJ-user group included .60 lifetime marijuana experiences; past month marijuana use; ,100 lifetime uses of drugs other than marijuana, alcohol, or nicotine; and not meeting Cahalan criteria for heavy drinking status . Control group classification criteria were ,5 lifetime experiences with marijuana , no previous use of any other drug except nicotine or alcohol, and not meeting criteria for heavy drinking status .All participants from the current study completed the larger ongoing study . Initial youth and parent0guardian screening interviews were administered separately by trained laboratory assistants to assess eligibility criteria. Participants were informed of the purpose of the study, procedures, potential risks and benefits, and confidentiality. Both parents and youth were informed that all study data are confidential . If eligible after the initial screens, teens and parents were administered detailed interviews assessing demographic and psychosocial functioning, Axis I psychiatric disorders, and substance use history.

To facilitate open disclosure, parents and youths were interviewed by different lab assistants, and confidentiality was guaranteed within ethical and legal limits. Youths were monitored with supervised urine and breath samples every 3– 4 days for 4 weeks. Youths with positive urine samples or breath alcohol concentrations or who appeared intoxicated were offered the option of restarting the toxicology screen procedure at a later time or to discontinue the study. If toxicology results indicated cessation and maintenance of abstinence, the adolescent received an evaluation between Day 23 and 27. Of MJ using youth who initiated monitored abstinence, 5 individuals had data suggesting substance use during the 4-week period, leaving 31 abstinent MJ users for this study. Youth who did not maintain abstinence were discontinued and compensated for their time. Upon completion of the study, youth and parents0guardians received financial compensation for participation.The detailed screening interview included the Structured Clinical Interview measuring psychosocial functioning, activities, estimated pubertal stage, last menstruation , health history, and handedness, and the computerized NIMH Diagnostic Interview Schedule for Children excluded participants with major psychiatric disorders, including DSM-IV Axis I mood, anxiety, attention deficit hyperactivity disorder, and conduct disorders. Parallel modules of the computerized Diagnostic Interview Schedule were used for 18-year-olds who lived independently. Family history of psychiatric and substance use disorders was also assessed . Youth were then administered the Customary Drinking and Drug Use Record to assess lifetime and past 3-month use, withdrawal symptoms, DSM-IV abuse and dependence criteria, and substance-related life problems .

Youth were administered the modified Time-Line Followback to obtain detailed information regarding type, quantity, and frequency of drug use during the past month. The TLFB provides a detailed substance use pattern using a calendar format with temporal cues to aid recall. Teens were asked how much they used each of the following drugs: marijuana, alcohol, nicotine, stimulants , opiates , dissociatives0hallucinogens , sedatives , and misuse of other prescription or over-the counter medications. If the youth continued to be eligible, a parent or guardian underwent a detailed screening interview using the parent version of the SCI, including information on prenatal0 infant development, childhood behavior, age of developmental milestones, parental socioeconomic status , family history of psychiatric and substance use disorders and youth and family medical and psychiatric history. Parents0guardians were also administered the parent version of the C-DISC-4.0 and the TLFB to improve the reliability of the youth diagnostic and substance use reports. At the neuropsychological session, youth were administered the Beck Depression Inventory and the Spielberger State–Trait Anxiety Inventory to assess mood .The intent of the current study was to examine whether group status or extent of marijuana use was associated with neuropsychological functioning in a sample of adolescents who demonstrated approximately 1 month of abstinence. The primary finding was that, after controlling for alcohol use and depressive symptoms, adolescent marijuana users demonstrated poorer Complex Attention, Sequencing Ability, and Verbal Story Memory, and slower Psychomotor Speed compared with non-drug using control adolescents. Furthermore, dose-dependent relationships were observed between lifetime marijuana use and poorer cognitive performance in these same cognitive domains, even after controlling for lifetime frequency of alcohol use. In general, post hoc analysis revealed that composite score differences were primarily driven by a pattern of slightly poorer performance among the MJ-users across several individual subtests within a cognitive domain. More specifically, after correcting for multiple comparisons, MJ-users significantly differed from controls on both sequencing and error subtest scores from the Sequencing Ability composite score . MJ-users performed marginally poorer than controls on several other subtests, including the TMT Number Sequencing ; CVLT-II trial 1 recall, Digit Symbol, Digit Span backwards, PASAT 2-second trial ; and WMS-III Logical Memory first recall, immediate recall, delayed recall, and recognition scores. This finding is consistent with longitudinal research following adolescents with substance use disorders over 8 years, also finding dose-dependent relationships between cumulative marijuana use and attentional and executive functioning . These findings lend further evidence to the literature that marijuana use during adolescence is associated with poorer attention, memory, and executive functioning . This neuropsychological profile is consistent with the hypothesis, based on adult studies, that marijuana is primarily associated with frontal, hippocampal, and cerebellar dysfunction . Additional structural and functional neuroimaging research focused on abstinent adolescent marijuana users is necessary to confirm this hypothesis. The current neuropsychological findings differ from those of Pope and colleagues , vertical grow who found that deficits in attention, short-term memory, and psychomotor speed were no longer measurable among adult marijuana users following 28 days of abstinence. One possible explanation for this discrepancy is that marijuana use during adolescence may negatively impact neuromaturation and cognitive development, resulting in more severe cognitive consequences compared with use during adulthood. For example, introduction of cannabis during adolescence may interrupt pruning of gray matter or disruption of white matter myelination, especially in the prefrontal cortex , which continues to develop into early adulthood . The current findings are consistent with animal studies that found more severe cannabis-induced learning impairments among adolescents compared with adults and findings that early onset use is associated with increased morphometric, electrophysiological, and cognitive abnormalities among adult marijuana users .

It is unknown whether continued abstinence from marijuana results in neurocognitive recovery or subsequent healthy neurodevelopment among adolescents. Therefore, longitudinal studies are necessary to investigate the long term trajectory of cognitive and brain functioning in adolescent marijuana users. Greater lifetime alcohol use was unexpectedly related to better performance on psychomotor speed and complex attention, primarily among the marijuana users. Of note, individuals who met Cahalan and colleagues’ criteria for Heavy Drinker were excluded, so adolescents with regular heavy binge drinking histories were not included in the current study. Still, this finding is in conflict with previous studies demonstrating dose-dependent relationship between increased alcohol use and poorer attention and sequencing ability . One possible explanation is that some other unknown moderating factors may explain the relationship between increased moderate alcohol use and improved cognitive function in this sample. Another possible explanation is that marijuana use could be somewhat neuroprotective in combination with moderate alcohol use during adolescence. For example, we have found that alcohol using adolescents demonstrated significantly smaller left hippocampal volumes, while combined marijuana and alcohol using adolescents had volumes similar to nonusers . However, the combined users had significantly weaker correlations between hippocampal morphometry and verbal learning compared with healthy control adolescents, suggesting abnormal memory system functioning. Among adults, simultaneous use of cannabidiol and alcohol actually reduced blood alcohol levels compared with an alcohol-only condition , and combined marijuana and alcohol dependent adults have performed better than alcohol-only dependent adults on an overall mean efficiency score derived from a computerized battery of cognitive tasks . Thus, there is some evidence in the adult literature that the combined effects of marijuana and alcohol may not be as damaging as alcohol alone. Due to high rates of concurrent alcohol and marijuana use , we were unable to recruit a sizable sample of heavy marijuana users with no history of drinking for the current study, hindering our ability to tease apart the independent contributions of each substance. Additional animal and human research is necessary to further examine the independent and interactive effects of alcohol and marijuana use on neurocognitive function in adolescents. As with any neuropsychological study, it is important to consider the clinical implications of these findings. Marijuana users performed 0.62 standard deviations poorer than controls on the Sequencing Ability composite, but less than half a standard deviation worse on other composite scores. However, considering that almost half of high school seniors have tried marijuana and 5% use it daily , any observed differences in cognitive functioning is of concern. Notably, these group differences and dose dependent relationships were observed among adolescent marijuana users who may be considered high functioning, with high SES and parental income , good physical and neurologic health, above average intelligence and reading ability, and the ability to abstain from substances for at least 1 month. Furthermore, the marijuana users in this sample did not have comorbid conditions associated with neurocognitive impairments, such as conduct disorder or attention deficit hyperactivity disorder , groups were similar on family history of substance use disorders , and abnormalities were observed after nearly a month of monitored abstinence. Thus, the current results may underestimate cognitive difficulties among the general population of adolescent marijuana users, who are more likely to be current users with comorbid psychiatric conditions. Still, even subtle cognitive difficulty may result in negative consequences in school and work . Students may miss information presented in class due to poorer processing speed, initial learning, and complex attention and working memory. Indeed, although their verbal intelligence and reading ability were comparable, the marijuana users obtained significantly lower grade point averages and were more likely to demonstrate behavioral problems in school compared with controls. This finding may be a direct result of subtle cognitive difficulties, or due to effects of intoxication, sleep alterations, poor mood, withdrawal effects, and preexisting neurobehavioral problems for which the marijuana users are at increased risk.

Decreased yields or prices for transgenic rice, ceteris paribus, would reduce the gross rents from the technology. Furthermore, the seller of the transgenic seed is likely to charge a premium of up to 60 percent of total per-acre seed costs, depending on the pricing structure of the technology. Roundup Ready® and Bt seed for commercially produced transgenic crops has historically been priced from 30 to 60 percent higher than non-transgenic varieties, and price premia for LibertyLink® corn seed range from 0 to 30 percent, although average chemical costs per acre are typically greater . Furthermore, growers will likely pay at least part of the burden of the fees assessed by the CRCA. Assuming that these effects are constant per cwt of output, they can all be represented as a unit increase in costs in terms of net returns. Increased unit costs of this form, ceteris paribus, would alter the distribution of the rents between stakeholders but not dissipate gross rents. As points of reference, base assumptions on price and yields are $6.50 per cwt and 80 cwt per acre, so gross revenues from sales of rice output are assumed to be $520. A price premium of $0.25 per cwt for conventional rice as compared to transgenic rice with no associated change in yields would thus have the equivalent effect on net returns to the grower of a fee of about $20 per acre. Note that changing output prices does not affect the cost structure of the average farm operation and, thus, there is a direct, linear relationship between net returns and price. To calculate the impact of these effects, weed curing simple subtraction of the product of the price change and yield from the baseline scenario is appropriate. On the other hand, both a technology fee and the CRCA assessments directly enter the cost structure and, as such, affect interest costs as well.

Tables 4 and 5 lay out these effects. A 30 to 60 percent technology fee, assuming a seeding rate of 1.5 cwt per acre and price of conventional seed of $14 per cwt, is equivalent to $6.30 to $12.60 per acre. Total fees assessed as a result of the CRCA would currently be $8.50 per acre at identical seeding rates and yields of 80 cwt per acre, although it is unlikely that 100 percent of these assessments would be passed to the grower. Table 4 assumes no pass-through to growers of the legislated fees while Table 5 assumes the maximum pass-through, thus bounding the estimates. Both conservatively assume two applications of glufosinate per growing season. Without the CRCA legislation, adoption of LibertyLink® rice is profitable for a technology fee of $6.30 regardless of any realistic yield assumptions and profitable at a technology fee of $12.60 per acre so long as yield drag is no greater than 8.9 percent . With zero yield gains, net returns per acre in this range of seed price premium increase by between 21 and 25 percent over conventional rice returns with even greater benefits for those experiencing positive yield gains. If we assume a small price premium of, say, $0.25 per cwt, the technology is profitable for either yield losses of 7 percent with no technology fee or no yield change with an unrealistic $25.89 technology fee. This highlights the importance of yield and price assumptions on the calculation of net benefits. However, it is clear that, even with a small output price premium and a seed price premium at the upper end of the observed range, the most likely adopters will benefit from increased returns over costs. Allocation of maximum CRCA assessments to the grower slightly changes the per-acre benefits but does not affect the qualitative conclusions . Net returns over the baseline scenario with a $6.30 technology fee are no longer positive with an 8.6 percent yield drag nor for a $12.60 technology fee and a 6.7 percent yield drag. However, identical yields still result in net benefits of between $24.50 and $30.80 per acre, more than enough to cover a $0.25 price premium for conventional rice.

To bound the per-acre benefits, we assume a lower bound of $0.25 per cwt price premium and an upper bound of no price premium with no CRCA pass-through. Under these assumptions, we conclude that the per-acre benefits of the transgenic HT technology are between –$7.22 and $58.10 for any given California rice grower with a midpoint estimate of $21.90. However, if we restrict attention to those producers most likely to adopt, as defined by at least zero difference in net returns, yield drag at the lower end of the range can be as high as 1.2 percent and they will still adopt.The preceding deterministic sensitivity analysis accounts for heterogeneity in land, weed infestation, and management ability as well as for the distribution of the rents generated by the technology. However, the magnitude of these rents is determined primarily through assumptions regarding yield and the price of rice as well as base assumptions on the price of alternative herbicide systems. While these point estimates are based on the best information available, another approach is to parameterize the distributions of those variables, which can be perceived as stochastic, and use Monte Carlo simulations to estimate the distribution of the surplus benefits of the transgenic rice technology. We take the specification in the equation and estimate distributions for a transgenic yield premium, the transgenic-rice price, and a conventional-rice price premium. Yields for the HT cultivar are assumed to vary according to a symmetric triangular distribution centered around 80 cwt per acre with a minimum value of 72 cwt and a maximum value of 88 cwt . This distribution allows for the possibility of yield gains and losses and, with symmetry, tends to be very conservative given the state of weed infestation and resistance across the state. Prices for California rice are essentially determined on the world market and thus are not influenced by the individual producer.

Using historical data from USDA for 1986 through 2002, we assume a log normal distribution for output price with a mean of $6.50 per cwt and a standard deviation of 1.67. Finally, the price premium for conventional rice is assumed to be distributed as a skewed triangular with a most-likely value of $0.25 , a minimum value of zero, and a maximum value of $0.52 or about 8 percent. These values are consistent with experience with corn, soybeans, and canola cited previously . To run the simulations, the technology fee and all CRCA assessments are set equal to zero and 10,000 draws from the distributions are made for each of four scenarios, depending on which parameters are assumed random. This gives an estimate of the gross surplus generated by the technology before pricing and assessment policies determine the distribution of those benefits. The first and second simulations assume no price premium with yields only and with both yields and price random; the third assumes that yields and the price premium are stochastic with the output price fixed at $6.50 per cwt, and the fourth assumes that all three parameters are random. As peracre benefits do not vary with output price alone, this scenario is excluded. In addition, each simulation is run for two groups—one that exhibits yields across the entire range of the distribution, labeled “all producers,” and one in which attention is restricted to those growers who are expected to increase their yields with adoption of the transgenic crop. This group is labeled “yield gainers” and yields are distributed as a non-symmetric triangular distribution with a most-likely and minimum value of 80 cwt per acre and a maximum value of 88 cwt . The yield gainers are most likely to adopt the new technology, and results from these simulations may more accurately represent the distribution of benefits among those who actually grow transgenic rice. Results from the Monte Carlo analyses are reported in Table 6. Under these assumptions, gross benefits from the technology are generally positive except on the lower end of the distributions. Yield gainers, on average, see a return of between $9.84 and $11.60 per acre more than the overall average producer with a slightly smaller variance due to the smaller yield variance assumed for this group. For both groups, indoor cannabis grow system introduction of the price premium increases the variability of the benefits by more than the introduction of output price variability. The price premium also reduces the magnitude of the surplus gains by approximately $20 at the median. Table 6 does not account for CRCA assessments or technology fees, generally bounded between $6.30 per acre and $21.10 per acre . Although not exact, a “back of the envelope” calculation suggests that median farm-level benefits, after accounting for these fees, are expected to be positive; however, not all farmers will see increased returns. The same is true for yield gainers in that median benefits are greater than $21.10 for each scenario but the lower end of the distribution may experience negative returns from adoption. The majority in each group, however, will benefit. More specifically, the exact probabilities of net returns greater than zero can be calculated. Assuming all three parameters are stochastic and bounding the fees according to the preceding assumptions, the probability that net returns are greater than zero for all producers is between 60.14 and 85.8 percent. For yield gainers, this range increases to between 89.4 and 100 percent, once again highlighting the importance of yield assumptions on net returns and hence on adoption.To further test the potential adoption impacts of the LibertyLink® transgenic rice variety, we apply the preceding methodology to the results of a three-year field study conducted by Fischer . The study covered growing seasons between 1999 and 2001 and was funded by DPR. The exercise uses the weed-management regimes and corresponding yield measures of the Fischer study, together with the pricing assumptions previously maintained, to estimate net returns for a hypothetical farm using identical herbicide rotations. To elaborate, Table 7 describes the rice-variety and herbicide-treatment regime used in each year of the Fischer study. The project was implemented on a rice field in Glenn County, California, on which watergrass was found to be resistant to molinate, thiobencarb, and fenoxaprop—three of the four chemicals registered in the state to control grass weeds at the time of the study . Four treatment regimes were analyzed: continuous molinate each year, an intensive combination of several chemicals each year, a rotate-mode-of-action regime in which chemicals with differing properties were rotated from year to year, and a continuous transgenic regime resistant to glufosinate. Each regime was applied to four plots of 0.57 acres each, and indicator measures such as yields were averaged for each treatment group . It is important to note that the choice of treatment regime was not related to economic considerations but, rather, to evaluation of the effi- cacy of differing treatment regimes under resistance conditions .To estimate potential returns over operating costs, the yield and herbicide regime data are used in conjunction with the structure presented in Table 2 to estimate per-acre costs and revenues on a hypothetical farm unit. Herbicides, custom operations, contract operations, interest on operating capital, assessments, and yields vary according to the experimental data while the remainder of the cost components are held constant at the levels presented in the first table. Again, to provide a basis for comparison, we set output prices for the transgenic variety equal to the conventional product and the CRCA assessments and technology fee equal to zero. Table 8 reports the results of the exercise. The first year of the trial included eight plots planted with LibertyLink® M-202 seed treated once with varying levels of glufosinate mixed with ammonium sulfate and eight plots planted with conventional M-202 seed, four of which were treated once with molinate and the remainder of which were treated once with propanil. The continuous-molinate treatment served as a baseline for the entire experiment as the field had demonstrated watergrass resistance to this particular chemical . From an economic standpoint, the intensive-combination regime was slightly superior to the two transgenic regimes with net returns per acre approximately 4 to 10 percent greater but less than the yield advantages of 8 to 13 percent. As operating costs for this treatment were higher than those for the transgenic rice, the difference in returns is explained primarily through yield advantages.

Beyond portfolio support, VC ecosystems also vary in scale from local to international. When VCs evaluate which startups to fund, the criteria depend on the stage of the startup. During the seed stage, VCs judge startups based on their technology, team, and the extent to which the startup has a believable market opportunity. Moving towards the Series A funding round, VCs begin to care about unit economics, proof of traction through contracts and letters of intent, and revenue. At the Series B stage, VCs continue to value revenue metrics and begin to look for established customer pipelines, go-to-market strategies, and proof of high growth companies. All three stakeholder groups had varying opinions of the role of government in the precision weeding ecosystem. Under existing conditions, growers viewed the government as offering little support and being out-of-touch with grower needs. Because specialty crops are a small percentage of America’s total agricultural production due to large commodity crops like rice, soy, and corn, government intervention for specialty crops would not provide as positive of a return on investment. While the government is slow, some of the growers did commend effective government funding for irrigation. However, in the future, because hand labor for weeding is arduous, some growers have hope for increased government support because of precision weeding’s positive social implications. Startups viewed government involvement as limited to grants and the USDA’s agronomy advice. Though pushed by local politicians, drying cannabis particularly in Salinas Valley, R&D tax credits remain trivial. In addition, startups and VCs noted the role of regulatory agencies such as CalOSHA and the Department of Pesticide Regulation.

In connection to the government, all three stakeholder groups mentioned government funding for land grant university research and the UC Extension system in a positive light. Some startups mentioned that they want to become more involved with universities to influence the curriculum and develop two-year technical degrees to combat workforce constraints in agtech implementation. However, some interviewees, such as V2, voiced that the Extension has lost grower influence and that now, Extension advisors may not be the farmer’s first call or key advisor anymore. Similarly, growers felt that though Advisors are helpful in educating and advising, a lack of funding and relatively low salaries have prevented the UCCE from gaining more influence over grower behavior and precision weeding adoption. In total, the thirteen concepts mentioned most in the interviews were identified to measure the overlap of themes between the stakeholder groups, . Five described current limitations preventing precision weeding from proliferating, four involved the role of government in promoting precision weeding, three concerned the interactions between startups and VCs, and one was about the role of large corporate farms. Some concepts were more polarizing than others, as demonstrated by the color imbalances between the bars for each concept. Concepts about the limited involvement of government and the role of the government in funding land-grant university research and the UCCE were agreed upon by all three stakeholder groups. However, only VC interviewees addressed the concept of ‘Big Ag is looking for strategic returns/outsourcing innovation’ to explain why large corporate farms engage with precision weeding startups. Additionally, the complexity of the machines to operate was brought up as a blocker by the startups and the growers, but not VCs.To visualize the results about the third objective, interviewee responses were mapped onto a user experience template, colloquially known as ‘swim lanes.’ After overlaying the results for growers, the most common touch points identified in the growers’ awareness phase were social media, in-person networking through conferences and conventions, and collaborations with universities.

Growers perceived startups to be concerned about their lack of connection to the agricultural community, the risk of wasting time with unideal pilots, and ensuring the grower has the right field conditions for what the startup needs feedback for . On the other hand, startups perceived their concerns to be the dual marketing of value propositions towards growers as well as their investors, supply chain issues that may limit their technical execution of commercializing manufacturing, and large growers having bureaucratic issues that prevent demonstrations and pilot projects from becoming recurring customer relationships . During the pilot phase, growers perceived themselves to be concerned about the risk of crop damage, support staff, startup longevity, and startup quality and capabilities . During the piloting and purchasing phases, startups perceived growers’ concerns to be the price model, logistics, weeding quality, and the startup quality and capabilities . While many startups were concerned about matching customer expectations because imitating human dexterity and vision is technically challenging, one grower explicitly did not have concerns in the piloting phase because they have realistic expectations: “I don’t expect it to be like a John Deere tractor that’s just going to come out and be perfect and do everything that’s expected. I get it with technology companies that when it’s going to come out, it may suck.” Most of the areas of improvement brought up were in the consideration/piloting phase. Growers felt that points of improvement in their user journey included the prioritization of larger growers over smaller growers: smaller growers should have the same access as larger growers have to new technologies . In addition, smaller growers may value other pain points, such as food safety, over precision weeding . Startups felt that improvements could be made by educating growers about misperceptions about a lack of equipment availability within weeding-as-a-service business models. Some startups were also concerned about the ability of dealers to devalue the primary piece of farmers’ equipment, such as a tractor; the additional implements, such as precision weeding add-ons, could devalue the equipment .

The most common motivations for precision weeding technology adoption were labor concerns, environmental sustainability, costs, and return on investments. Growers were the most vocal and detailed about the shortage of labor motivating their interest in precision weeders. Because of the increase in minimum wage and AB 1066 qualifying farmworkers for overtime pay, growers and producers are growingly concerned about labor regulations . These increased labor expenses push producers to increase on-farm efficiency and mechanization, particularly on vegetable and organic farms. California growers’ issues with labor scarcity and thus increased labor costs has been a long-standing trend that also contributed to early mechanization during the 20th century. Because of California’s niche growing conditions, there was the advent of new gasoline tractors and mechanical pickers and harvesters . Now, labor scarcities are especially pressing because of California’s large production of specialty crops. Labor expenses are also especially pertinent because of the state’s strong organic sector and its associated costs. In 2019, data from the California Department of Food and Agriculture’s State Organic Program found that California’s organic sector is growing: organic acreage has increased from 1.8 million acres in 2014 to 2.6 million acres in 2019, and in 2019, organic products in the state sold for more than $10.4 billion . Additionally, California’s organic production made up 40 % of all organics in the U.S., indicating the state’s importance as the trailblazer of organic agriculture . This increase in organic production has arguably been fueled by support from the State Organic Program, a regulatory and educational department within CDFA which has, for example, implemented cost share programs for USDA certification . In addition, the consumer preference for organics has driven this trend: multiple studies have demonstrated consumers’ willingness to pay premiums for organics, with the market demand influencing grower decision making . However, organic farms face logistical and operational challenges because they employ more workers per acre. A survey of organic farms revealed that farms that have less than half of their land in organic production have fewer direct-hire workers per acre, 0.58, in comparison to farms with more than half, 0.84 . Similarly, another study found that compared to conventional farms, organic farms have both more workers per acre and a higher proportion of full-time employees to seasonal contractors . Interestingly, despite copious literature on the positive correlation between increased costs—particularly from labor—and organic farming, pipp racking the results of this study align more closely with literature suggesting that digital technologies are often closely adapted to conventional/industrial farming practices. All the growers we interviewed produce both organic and conventional crops and most startups we interviewed still included herbicides in their weed management regimes. The trend of agricultural technologies being more suitable for conventional agriculture has been shown in the use of big data, an aspect of digital agriculture defined as large sets of heterogeneous data. While harnessing big data has proven environmental and economic benefits, access may not be realistic for small-scale farmers, further widening the accessibility gap between industrial players and more vulnerable ones .

Elaborating on this accessibility gap, a review of digital agriculture revealed that top-down technological development, as opposed to farmer-driven initiatives, often are designed for very specific production systems . In addition, agricultural machinery exhibits economies of scale at the farm level, favoring larger-scale farms . Beyond the larger farm size associated with conventional growers, technological solutions may not target the needs of organic growers. A study found that digital technology use for production was underrepresented on organic farms because of a mismatch in the technology solution and the grower needs . For example, GPS deployment may help a conventional grower save on diesel, fertilizer, and weed killer, but it will only help an organic grower save on diesel. As a result, literature suggests that digital agriculture, including precision weeding technologies, may be adapted more towards conventional agriculture despite the labor stresses felt by smaller-scale, organic growers. All venture capitalists interviewed were motivated by environmental sustainability while only one grower mentioned it. This venture capital emphasis on environmental concerns such as soil quality, water quality and quantity, and unsustainable practices spur agtech investments. Investors not only valued financial returns but were also motivated by social impact and environmental returns . Because of the venture capital emphasis on environmental concerns, startups may align themselves similarly to raise funding. In a study examining how agrifood tech startups pitch themselves to venture capital firms, researchers found that VC firms make investment decisions not only on the substance and hard facts, but also based on the performance and cultural signaling of the pitch . Therefore, precision weeding startups may drive narratives of social entrepreneurship and sustainability to develop ‘visions of desirable futures’ and add moral justifications to their technologies . Paralleling such startup pitches are the mission statements of agri-food tech investors, which often combine profit and purpose . Despite both stakeholder groups emphasizing sustainable stances, these aspirations may fall short: ‘techno-fixes’ are overly simplistic and cannot realistically correct global food system challenges and the investors’ ROI requirements may curb ambitions .Considering the varying views of government conveyed by the interviewees, the political identities of the interviewees may influence their views on the effectiveness and ideal roles of government. Growers also expressed an increased distrust of government, a trend consistent with the general population . A study using ANES survey data found a shift from democratic identification to independent and conservative ideologies . In addition, in Imperial County, the most impactful work-related stressor for farmers and ranchers were unpredictable factors like government regulations . Though growers felt that the government did not understand the realities of agriculture, many actively advocated and were involved in agricultural leadership efforts . These generally negative sentiments from growers towards the government are juxtaposed by the involvement of the public sector in the digitization of agriculture. A case study examining precision dairy farming in Australia found that public R&E played the largest roles, relative to private R&E, in market formation and the creation of legitimacy . In particular, the public sector galvanized a community of interest around precision dairy farming and developed the National Livestock Identification Program to establish industry standards . In addition, an example of public action promoting digital agriculture is the regulatory pressure against glyphosate use incentivizing industry players to decrease chemical inputs . Considering the parallels for all on-farm technology adoption, existing literature about digital agriculture and the public sector can be contrasted by our case study of precision weeding in which the startup-VC-grower matrix does not consider government interactions as a factor for technology adoption.

The onus is on those producing the evidence to actively engage governments, stakeholders and policymakers, and outline the human and economic advantages of preventive strategies like behavioral interventions over a treatment-focused model of healthcare provision. Related to this, behavioral scientists need to better demonstrate how theory-based behavioral interventions that work in lab and field experiments, and have been shown to be effective in larger randomized controlled trials and in real world contexts, can be implemented in practice. Such evidence should be the focus of evidence presentations to government and policymakers advocating investment in, and implementation of, behavioral interventions. The expanding discipline of implementation science focuses on translation of research findings into evidence-based practice, and is receiving increased attention in the fields of behavioral science, public health, health promotion, and health policy . In the context of behavioral interventions, implementation science examines the pathways and strategies necessary for the uptake and implementation of interventions by policymakers and providers. Evidence on how behavioral interventions can be developed by key workers within existing networks, who will ultimately be responsible for implementing the intervention , and how users of the intervention can be involved in the implementation, is important to ensure that interventions are practically relevant and sensitive to the contextual and cultural characteristics of target populations. In addition, equipment for weed growing research on how theory-based behavioral interventions can be upscaled so their reach within target populations is maximized and the changes in health behavior and health outcomes promised by formative research realized.

Research is needed to identify the conditions necessary to up-scale behavioral interventions in real world contexts, including identifying the partnerships needed to fund, implement, monitor, and maintain interventions; engaging stakeholders to assess the feasibility and acceptability of implementing the intervention in the target community or setting; assisting governmental agencies in developing multi-level and multi-sectorial plans to implement interventions; and developing ways to embed interventions in existing networks throughout development from inception to implementation.In conclusion, interventions based on behavioral theory have been shown to be effective in changing health behavior. However, there is still need for more research on interventions that systematically and precisely map intervention content with theoretical determinants, and the need for greater transparency in the reporting of intervention content and protocols. Arguments that such behavioral interventions do not work in the real world based on observations that pandemics of non-communicable disease continue to rise, and large scale interventions have not shifted population-level participation in health behavior, as my colleague contends, are specious and miss the point. The issue is not that interventions based on behavior theory do not work in changing behavior in ‘real world’ contexts, they do, rather, it is a lack of investment in, and inadequate upscaling and implementation of, these interventions that has failed to translate their efficacy into sustained, long-term change at the population level.Over 50 years positive population behaviors or health outcomes for nutrition and physical activity have fallen or flatlined globally, and in individual countries. Data shows: rising global obesity since 1975 , and inindividual countries including England, Chile, and Australia; falling or flatlining fruit and vegetable consumption in USA since 1994, and in Japan and Brazil since 1965; and rising physical inactivity globally since 2001, in Spain since 1995, in USA since 1997, and in China since 1989.

For health outcomes, European Environment Agency data show loss of healthy life years attributable to non-communicable diseases has grown by more than 20% since 1990. These data illustrate global trends, and their replication in individual countries. Something isn’t working! Noting disjoint between a body of behavioral theory literature that appears to show promise at the individual level, and global and national data that shows no change in population behaviors and health outcomes for half a century, Hallal et al. argue “after more than 60 years of scientific research… more of the same will not be enough” . It appears behavioral theory has a case to answer, and some fundamental questions to face. But is the problem scale-up of behavioral theory in population level interventions and policies, is it intervention designs that act as the vehicle for behavioral theory, or is it simply that behavioral theory itself does not work in the real world?For decades fields such as exercise physiology, public health, epidemiology and the behavioral sciences have undertaken research showing that if behavioral theory is deployed “under scientifically controlled circumstances, behavior change is achievable for increasing physical activity” . However, many “so-called effective physical activity interventions” are small-scale, controlled efficacy trials that do not demonstrate effectiveness or ecological validity, and leave gaps in the chain of evidence between participants, theory, behavior and health outcomes . An intervention is efficacious if it works in cohorts who receive it, whereas it is effective if it works in cohorts who have been offered it. This is confused in the literature, and interventions based on behavioral theory claim effectiveness when available evidence demonstrates only their efficacy. Many trials of interventions based on behavioral theory do not venture beyond controlled environments of phase I-III trials, which seek to establish, respectively, concept, efficacy and comparative efficacy. Thus, at best, evidence demonstrates that impact on those who receive the intervention exceeds impact on those who receive alternative interventions. But still, this shows only that an intervention is comparatively efficacious for those who receive it, not that it is effective, or comparatively effective, in cohorts that are offered it.

The problem is this: the features of design and implementation associated with good phase I-III trials to establish concept, efficacy, and comparative efficacy, have important limitations for informing practice and policy decisions, which require more generalizable information relating to outcomes of societal consequence, such as a sustained impact on health outcomes at population level. Such impact, or the potential for it, must relate to real world effectiveness “as evaluated in an observational, non-interventional trial in a naturalistic setting” . To establish effectiveness, phase IV trials require a more diverse set of methods than those required to establish concept, efficacy and comparative efficacy in phase I-III trials, and must involve a diversity of settings, participants and deliverers . However, in reviewing studies purporting to examine effectiveness of physical activity interventions in the real world , Beedie, Mann and Jimenez found that many still tried to adopt laboratory style methods and controls that would be impractical or uneconomic in real-world settings. Some authors have advocated the RE-AIM framework as a Phase IV tool to develop the effectiveness of interventions shown to be efficacious at phases I-III. But, with its focus on ensuring reach, adoption, implementation integrity, and maintenance of the features of the intervention over time, RE-AIM merely attempts to deliver effectiveness by maintaining the controlled environment of phase I-III trials in the real world, which as well as being impractical or uneconomic, is also likely to be futile. Establishing effectiveness in phase IV trials is difficult, and requires longer timescales, and greater scale and resources than establishing concept, efficacy and comparative efficacy in phase I-III trials. As such, it is not surprising that, in an area where research funding is relatively sparse, pipp horticulture and doctoral studies are often the bricks contributing to edifices of knowledge, genuine phase IV effectiveness trials are rare. Nevertheless, there is a moral obligation to conduct them, otherwise advocacy for behavioral theory interventions based only on efficacy evidence risks wasting participants time and taxpayers money on unproven interventions in unproven populations.Analyses of national participation data suggest interventions based on behavioral theory may be recipients of individual behavior change, rather than the stimulus for it. This is because populations’ behaviors are qualitatively different to individual behaviors, and incorporate individual behavioral volatility within their steady state. Forexample, in England two national surveys, Active People and Taking Part, show population participation in sport and related physical activity has flatlined for 10 years, with no sustained change beyond +/− 2%. Furthermore, data synthesis across six surveys shows falling or flatlining participation for 25 years. However, both cross-sectional retrospective report data and panel time-series data from the surveys also shows considerable individual behavioral volatility, with circa 20% of the population dropping out or doing less sport, 20% taking up or doing more sport, 20% maintaining participation, and 40% consistently doing no sport.

Consequently, within any 1 year circa 40% of the population change their sport participation behavior, but aggregate population level participation is unchanged. Thus, steady state population behaviors incorporate considerable individual behavioral change. This suggests behavioral theory interventions are reflecting and facilitating individual behavior changes that take place as part of the steady state behaviors of populations, with participants often presenting as already motivated to change [88, 93]. Sport England’s Get Active: Get Healthy first-year pilots, for example, claimed to be the stimulus for more than 30,000 people becoming active, but the evaluation showed the majority of participants were “ready to change” when they joined. This suggests the interventions were the recipients rather than the stimulus for individual behavioral changes, which are to be expected as a normal part of steady state population behaviors. It is known that poor health outcomes, particularly non-communicable diseases, correlate with social deprivation, low employment, poverty, poor housing, and other indices of multiple deprivation. Behavioral theory provides neither the explanation nor, through interventions targeting individuals, the solution to such problems, which must focus on wider causal systems that underpin the social practice and economy of behaviors such as low physical activity and poor diet. Undoubtedly, it is the focus on the individual rather than the population that undermines the real-world effectiveness of behavioral theory. The etiological model on which it is based – that poor health outcomes are caused by exposure to a substance, for example, sugar, and that health outcomes can be improved by modifying or moderating individual behaviors to remove or reduce exposure – is fundamentally flawed. This is because solutions – interventions based on behavioral theory – have no relationship to causes – the factors that lead to behaviors in the first place. Furthermore, behavioral theory is assumed to be universal: that is, it is assumed the same behavioral theory can address any behavior, be that smoking, alcohol consumption, poor diet, or low physical activity – the transtheoretical model, which was developed for smoking cessation, is a case in point. Cleary these behaviors are underpinned by different antecedents, so why would we assume they can all be addressed by the same theory? Furthermore, categories of behavior are not homogenous – the existence of health inequalities is, in itself, evidence that the factors that lead to behaviors in relation to, for example, diet, differ across the population, and so poor diet is an agglomeration of behaviors rather than a single behavior. Why would we expect that these multiple complex behaviors could all be addressed by the same theory?I have argued that while interventions based on behavioral theory have been shown to be efficacious in the controlled environments of phase I-III trials, there is no evidence from genuine phase IV effectiveness trials to demonstrate they work in the real world. However, crucially, I argue that evidence from controlled trials of behavior change interventions simply capture individual behavioral volatility that is a normal part of steady state population behaviors. Furthermore, such interventions fail in shifting population behaviors because they focus on individuals rather than on the multiple complex factors that drive the distribution of behaviors in the population. As such, behavioral theory within such interventions is not an active ingredient, rather it is a dormant recipient of behavior change. Put simply, behavioral theory has no active influence on changing behaviors in the real world.I am grateful to my colleague for raising important points on the implementation of theory-based behavioral interventions and the need for more evidence for the effectiveness of behavioral interventions in ‘phase IV’ trials. These are good points that have been made many times elsewhere, including my opening statement. However, as an argument against the proposal, his statement is not fit-for-purpose. As I predicted, my colleague claims that interventions based on behavioral theory do not work in changing behavior in ‘real world’ contexts because there has been no year-on-year change in rates of non-communicable diseases and health-related behavior participation at the population level. He also suggests that behavior theory focuses solely on individual behavior, targets only the motivated, and fails to incorporate structural determinants of behavior. Here I illustrate how his arguments reflect a poor understanding of behavioral theory, and are not based on appropriate evidence, or, in some instances, any evidence at all.

The study has characterized the magnitude and the range of possible emissions impacts as compared to multiple baselines . A clear message that emerges is that decision-makers must avail themselves of better foresight and informed decision-making on near-term and longer-term timescales. More comprehensive awareness of vehicle use cases, and energy needs in time and space will help small businesses and utilities predict and plan for EV charging events. This research suggests that when marginal emissions can be at or below the weighted average values, environmental benefits stand to be greater. A unique attribute of this study compared to prior efforts is that its scope speaks more directly to small business owners and vehicle fleet operators. These stakeholder groups and their associated applications are known to realize a few advantages in comparison with individual vehicle owners driving LDVs. The reason is that the selected categories of service vehicles largely return to a central base and navigate similar, standardized routes on a recurring basis. They also travel sufficient but not overly excessive distances: a factor that may help approach the Goldilocks state. Finally, and perhaps not coincidentally, this audience seems to be targeted by automakers of late, given a limited growing number of new EV models entering the market. Though both LDV and MD use cases have societal implications involving decisions around the generation mix and utility infrastructure, it is the potential to leverage an EV to save money that could pull the technology quickly ahead and spur scale up in other vehicle sectors. The study has implications for policy and public investment, cannabis drying including an even more urgent need for managed and coordinated charging, and greater attention to resource planning.

This is especially relevant for infrastructure funding, for which the Federal Government has deployed upwards of $7.5B to states and set a goal to realize 500,000 chargers by the year 2030. The report concludes with a few suggestions for future work, including the need to leverage this methodology to quantify the monetized value of CO2 emissions in conjunction with other investment costs for capital and operations. Finally, the research team believes the model has relevance and can be scaled and adapted for conducting similar analyses in other regions.In short, it is imperative to not only manage EV charging events in time and space, but also consider our latitude to control or influence other large loads on the grid in conjunction with EV deployment growth. This study reveals that several Medium and Heavy-Duty EV use cases can offer significant benefits, but also makes it clear that decisions around charging operations, infrastructure and grid support must be conducted at a system level that considers vehicles, their use cases, as well as the temporal nature of grid generation. In this way, the electrification of transportation is more likely to result in measurable decarbonization gains, substantive environmental and health benefits, and reasonable returns on investment.Vehicle electrification not only continues to garner momentum, but also public and private funding, and is considered a viable means of growing the national economy and decarbonizing major segments of the transportation sector. A growing body of evidence demonstrates that substitution of gasoline-consuming vehicles with electrified alternatives eliminates tailpipe emissions contributing to reductions in CO2 emissions as well as in criteria pollutants. Whereas CO2 reductions can favorably affect global climate change trends, pollutant emissions reductions can improve urban air quality on a more local scale, and by extension improve public health.

A key advantage of Electric Vehicles compared to internal combustion engine vehicles is that their carbon and emissions footprint is not fixed based on the vehicle technology from a given past model year, but instead can progressively improve in lock step with a grid that is evolving toward a cleaner and lower carbon generating mix. Driven in part by policy, declining prices, and product availability, EV deployments are accelerating, having surpassed 2,000,000 vehicles sold in the U.S. fleet by Dec 2022. Though EVs still account for less than 1% of the domestic vehicle fleet, the growth is definitely accelerating. Projections for continued EV growth through the present “second decade” of mass deployment are varied, and uncertainty is a factor for both capital costs and energy costs. Still, many sources suggest sustained growth approaching double digit shares of the fleet by 2030. EVs are increasingly seen as a win-win solution by many policymakers, in that they can provide benefits to consumers, automakers, and utilities, while also reducing environmental impacts. In spite of substantial progress and aggressive policy support, non-trivial barriers remain. These barriers may simultaneously threaten both broader adoption and certain beneficial outcomes of EV growth. Among one the most critical and poorly understood, is the need to ensure environmental benefits live up to their promise as deployments exceed 10% of the future fleet. This seems to be a kind of threshold of market penetration beyond which grid capacity, resource adequacy, broader electrification, levelized energy costs, and decarbonization may be challenged. While much public attention is focused on light duty vehicle markets , significant opportunities are believed to exist in Medium Duty and certain Heavy Duty applications. For this reason, the EVALUATE research team has conducted a twoyear, two-phase research investigation focused on methodologies and applications across major Light Duty and Medium Duty vehicle classifications. Key contributions of our Phase I included the development of a rigorous methodology involving a high-fidelity system of systems model.

This included a sub-system model for vehicle power trains which provide accurate estimates of energy consumption for representative driving cycles. Additionally, it included a literature review, survey data, observed experimental data, and a protocol to inform EV charging profiles. And finally, it included a series of datasets and procedures developed to understand how electric power is dispatched and delivered at the bulk grid level. More specifically, it generated a high-resolution characterization of the emission rates associated with electric power generation on an hourly, daily, seasonal, and annual basis. While studies have explored each of these sub-systems independently, the research team has been among the first to develop them in an integrated manner to forecast the emissions outputs of a class of vehicles and a range of use cases. The phase I findings were significant and explored light-duty vehicles through typical urban commuters and households that operate LDVs for daily personal use. [See Phase I final report for more on the initial study and its key findings, 1].The over-arching goal of the EVALUATE project has been to ensure that reductions in CO2 and pollutant emissions are more fully understood, and that decision-makers have guidance and tools to help realize them. The research team believes this will be imperative as EV market penetration scales up . To achieve this goal, Phase I of this project developed a system of integrated vehicle, transportation, and electric power system models designed to evaluate hourly marginal CO2 emissions rates for a regional study under various demand scenarios between now and 2030. As noted, the focus was on personal vehicles in the light-duty category. Phase II of this project, presented here, demonstrates the usefulness of these tools in providing policy-relevant information to practitioners and decision-makers. As such, we focus on a series of targeted case studies that extend prior work from LDVs operated by individuals to service oriented vehicles operated by small and medium businesses.To augment the analysis and build upon prior work, additional inquiries were made into the type and capacity of EV charging devices that would be required for these larger vehicles and different use cases. For instance, Phase II has focused more extensively on medium rate and fast charging methods1 . In conjunction, the research team assessed likely charging behavior that would be typical of small businesses in the subject categories. Again, the goal has been to better understand how vehicle use case, charging behavior, and assumptions around the grid, with a particular focus on marginal emissions, may affect the environmental impacts and other relative pros and cons of EVs as a substitute for the incumbent vehicle technology . The selected scenarios and simulated outputs are based upon a series of case studies in the Atlanta, Georgia metropolitan area using local assumptions along with historical and projected grid data for Georgia Power and the Southern Company balancing authority. These case studies evaluate the influence of vehicle classification, usage, best way to dry bud and charging strategies for EVs in both light-duty vehicles and medium-duty trucks .

All case studies explore the relationship between the selected scenarios and the resulting carbon intensity of marginal electrical power generation. This investigation provides an important theoretical contribution to our overall understanding of vehicle electrification for intermediate market penetration rates. Equally important, the study demonstrates the ability of the EVALUATE modeling system to produce practical policy-relevant findings that are valuable to stakeholders that relate to our selected scenarios, the Southeast region, and more broadly. This research is uniquely positioned to address critical gaps and inform strategic decisions that will be economically viable and favorably advance EVs, sustainable transportation solutions, and their concomitant policies. This research identified representative use cases that included Light and Medium Duty return-to-base fleets. Prior to the present study, the research team oversaw a Georgia Tech student-led effort that conducted a preliminary techno-economic investigation into residential service vehicles such as those used by HVAC, exterminator, plumbing, and landscaping personnel, with some high-level economic indicators depicted in Fig 1-1. To this, the current research team added new business-related scenarios including ecommerce, package delivery vehicles, moving trucks, and refuse trucks. In the present study, the team applies the marginal emissions methodology to these expanded use cases, to further demonstrate how the methodology can be applied, and yield some illustrative insights for several discrete vehicle categories and use case scenarios. Finally, the study provides guidance that can inform how decision-makers can optimize effectiveness and cost based on the team’s approach .This research requires the synthesis of three independent sub-system models and data developed or identified by the research team in the areas of vehicle propulsion and auxiliary power and energy need to satisfy prescribed trip/travel demands for a range of vehicle technologies and applications, EV charging profiles that would be considered typical for the service, fleet and medium duty vehicle use cases, and grid generation dispatch with commensurate consideration of emissions intensities for CO2 and major criteria pollutants. The team has extensive experience developing high-fidelity sub-system models and applying them to both generalizable and regional scenarios. As an input to the two phases of the EVALUATE project, the team drew upon more than three years of prior efforts acquiring and conditioning open-source data, alternative vehicle architectures, customized datasets for regional electric power dispatch , and numerous travel route pathways. Under the EVALUATE project, the team deepened its experience by integrating several of these subsystem resources into a holistic picture of emissions by vehicle type and use case. The scope of the second phase of this project has been to update and develop new, more accurate subsystem models and datasets that are granular and of specific relevance to service fleets and medium-duty vehicle operators. The end result of the two phases, therefore, is a set of integrated models built upon high-fidelity data from real-world use cases that generate a range of simulations. Throughout the EVALUATE project, the simulations are generated primarily to draw comparisons, understand the impact of fundamental assumptions around charging behavior and grid emissions, and develop initial guidance around the relative merits of EVs under representative use cases. The use of these tools to guide private sector fleets and medium-duty vehicle operators can be timely since few high-fidelity emissions calculators are available to accompany proprietary economic assessment tools.The team’s methodology was developed in Phase I and expanded in Phase II for the purpose of investigating a broader set of vehicles and charging profiles that typify urban service fleet and medium-duty delivery applications. A brief recap of the major steps in the analysis is presented here. First, physics-based vehicle energy consumption models are developed which facilitate comparisons among vehicle architectures that utilize energy from disparate primary sources . As noted, the Phase II effort extended the modeling from light-duty cars used for personal use, to light-duty pickup trucks and vans used for serviceoriented businesses. Additional models were derived and corroborated against background data to characterize medium-duty delivery trucks and a heavy-duty urban application . A related task involved the identification of driving cycles that approximate typical routes traveled by the associated vehicles.

This publication summarizes the steps involved in establishing hedgerows on farms in California and concludes with a discussion of potential food safety issues associated with hedgerows and attracting wildlife to farms. Farm Plan When considering habitat restoration work on farms it is important for landowners to develop a whole farm plan to integrate their conservation goals and methods with current farming operations. Examples of habitat restoration goals include the use of hedgerows for soil erosion reduction, wildlife enhancement, increasing biodiversity, water and air quality protection, windbreaks, attracting beneficial insects for pest control and bees for pollination in adjacent crops, and weed control. The goals of the restoration work will affect the types of plants selected and where the project should be established on the farm. Consult an aerial map of the farmland to assess the topography, hydrology and drainage, crop production areas, non-crop areas, and buildings before defining the appropriate use of the land for different types of restoration purposes. Also consider potential funding sources, since restoration projects can be expensive. A good source of information for cost-share programs and additional farm plans is your local Natural Resources Conservation Service as well as the Yolo Resource Conservation District , Robins et al. 2001, and CAFF 2004. Site Selection Once a farm plan has been developed, select sites for the proposed work. The most suitable areas for restoration projects include non-cropped areas along roadsides, agricultural drains, fences, canals, marijuana grow system field borders with different elevations, and gullies. Sites should be easily accessible by equipment for project construction and maintenance. Access to water during the growing season is essential for establishment of shrubs and trees for at least the first 3 years or until the plants are well rooted to survive California’s long, dry summers.

Site Analysis After selecting a site for a hedgerow, analyze the area to determine which design and plants would perform best. Some hedgerows fail because the plants used are not well adapted to the local field site conditions. Determine the soil type, assess the area for potential flooding, and identify obstructions such as overhead wires that would limit tree planting. High and low spots that have standing water should also be noted, as well as potential for plant injury from nearby livestock, or competition from established vegetation such as shading from trees, equipment traffic, and herbicide drift. Planning and Design Once the site has been analyzed, make a drawing of the area that shows the size of the restoration project, types of plants to be incorporated in the design, and the planting layout. In general, linear plantings are the easiest to maintain with the large-scale equipment such as mowers, disks, or sprayers commonly used on farms. A single strip of shrubs and/or trees bordered by strips of native perennial grasses, and/or sedges and rushes if riparian, and a forb strip works well as a hedgerow design on field crop farms . Access roads separating the hedgerow from the crop help prevent birds from feeding on newly emerged crop seedlings, which may occur when native grasses and shrubs are planted adjacent to the crop.Figure 1 shows a typical plant spacing for trees, shrubs, and forbs. In mixed perennial plantings one grass type may initially dominate the stand, but over time other species will begin to emerge. In addition to the forbs listed above, the forb strip seed mix can include lupines , clovers , tarweeds , vinegar weed , and California poppy . Additional trees include oaks , California sycamore , and California buckeye . A more complete list of plants and perennial grasses adapted to various regions in California and information on where to purchase them can be obtained from the Resource Guide for Hedgerows in California and the Pictorial Guide to California Native Grasses . For large-scale plantings of shrubs and trees, place orders at least 6 months in advance to ensure plant availability.

Site Preparation and Planting Hedgerow sites should be disked and shaped to prepare the area for planting, providing a good seedbed for the native perennial grasses and forb seed mix. Although the grass seed can be planted into ground that has not been recently worked using a no-till drill, weed control will become more difficult and costly later on. Some hedgerows are planted flat, others on raised 60-inch beds. If the site is flood-irrigated and soils are heavy with a high water-holding capacity, use only plants that tolerate flooding. Space the larger shrubs at a 15-foot spacing and the smaller ones in between the large ones at 7.5 feet . Trees need a 20- to 30-foot spacing, depending on the variety. Fertilize the shrubs and trees with compost or a slow-release fertilizer at recommended rates at time of planting. Plant the native perennial grasses at 12 to 14 pounds per acre and the forb strip at 15 to 20 pounds per acre. Use a no-till drill for the native grasses because the long awns on some varieties tend to get stuck in the drills. Sometimes a carrier such as bran may need to be added to the seed mix to achieve this low planting rate. Perennial grass seed can also be broadcast at 20 to 25 pounds per acre, and forbs at 20 to 30 pounds per acre, then lightly harrowed by dragging a chain across the site to cover the seed. The best time to plant perennials in California is in the fall when cooler and wetter conditions help plants establish before the summer heat sets in. Irrigate every 1 or 2 weeks during the growing season for the first 3 years, or until the plants are well rooted. The duration and frequency of the irrigations will depend on plant evapotranspiration rates and soil type. So, for example, plants in sandier soils on hot days will need more frequent watering than those in heavier clay soils with a high moisture-holding capacity. After 3 years, the hedgerow plants will still benefit from an occasional summer watering. In areas lacking access to water, water tanks can be used with pumps to pressurize and deliver the water through drip lines. Native perennial grasses and many direct-seeded forbs go dormant in the summer and do not need to be irrigated.

Bird herbivory on new forb seedlings can be prevented by the use of bird scare tape on poles and netting. Weed Management Weed control is the most difficult and challenging part of establishing hedgerows of native grasses, trees, and shrubs on farms. For hedgerows of shrubs and trees , the most cost effective and long-term solution for weed control is to use mulches, such as walnut shells or compost, or weed mats. Preemergence herbicides such as Ronstar can be used, but they may not provide enough broad-spectrum weed control. That is, several weeds may be controlled, but others often take their place. Roundup provides effective weed control, but drift to nearby hedgerow plants must be prevented when spraying,especially when the plants are small. Once the hedgerow plants establish, they will help shade out competing weeds. For establishing native perennial grasses and forb strips, let the winter rains bring up the first flush of winter weeds prior to planting; control these by harrowing or spraying with Roundup. A second application of Roundup can be made about a week after planting to help control the faster growing annual weeds before the perennial grasses emerge . Walk the area to make sure the native grasses have not emerged prior to spraying with Roundup or you will lose the stand. These may be difficult to identify so if you are not sure, either skip the Roundup spray or call someone with experience in native grass plantings for help in identifying the seedlings. If drill-seeded, look for rows of seedlings. For broadleaf weed control in native grasses a number of herbicides can be used, including the phenoxys MCPA and 2,4-D, curing and drying weed as well as Buctril , Garlon , Milestone , and Vista . Be sure to check with your local agricultural commissioner for restrictions on the use of these materials, especially with the phenoxys that cannot be used after March 1 in many counties. These herbicides may also injure newly emerging native grass seedlings, so wait until the grasses are at least several inches tall before applying them. Grass weeds in native perennial grass stands are difficult to control. Preemergence herbicides do not give a broad enough spectrum of annual grass weed control, and there are no registered postemergence herbicides that can be used in mixed native grass stands without injuring them. To help the perennial grasses compete, mow annual grasses in the spring before the weeds set seed during the first 2 years of stand establishment. Weed whacking, weed wicking, and spot spraying with herbicides will also help maintain a healthy stand. Burning the native grasses in the fall helps control weeds, but do not burn on a hot day or the native grasses will be injured. Be careful as well not to burn the native shrubs, which can be injured or killed by fire. More information on weed control can be obtained through the University of California Gophers and voles will feed on the roots and crowns of establishing shrubs and trees, sometimes causing extensive plant losses. To prevent vole damage, place plastic tree tubes around plants at the time of planting to keep the rodents from girdling them. In severe vole outbreak years, apply zinc phosphide, diphacinone, or treated grain to control these pests. These rodenticides are available through some county agricultural commissioner offices. Follow the label carefully to avoid poisoning non-target species such as birds. Monitor and trap for gophers when activity is observed. Poison baits are also available for gopher control. Barn owl boxes can also be placed in the hedgerows to attract owls that prey on these rodents.Once established, hedgerows of shrubs, trees, sedges, and native perennial grasses compete fairly well with weeds, but they still require yearly maintenance to keep weeds under control. Grasses should be mowed, grazed, or burned every couple of years to maintain the health of the stand, with the timing and frequency dependent on the weed complex and severity in the stand. In general a good time to mow established grasses is after July, when the bird-nesting season is over. Monitor shrubs and tree plantings yearly for rodents and weeds, as weedy hedgerows tend to attract insect and rodent pests that may cause problems for adjacent crops. Established hedgerows of shrubs and trees may also benefit from an occasional summer watering, especially during drought years.The cost of establishing a hedgerow for the first 3 years is estimated to be $3,847 for a 1,000-foot-long hedgerow with a single row of shrubs and trees bordered by native perennial grasses . This cost includes labor for site analysis and design and field preparation, including disking and shaping the site and preparing a seedbed. The cost also includes plants, seed mixes, fertilizers, and labor for planting. Weed control costs include mulches, herbicides, and hand-hoeing; although high initially, these costs will decline as the native perennial grasses and shrubs mature and outcompete weeds. Irrigation costs include drip tube and emitters as well as labor for installing the system and irrigating the plants for at least 3 years. Vertebrate pest control costs include tree tubes to protect young plants from voles and squirrels, rodenticides, and trapping. Additional costs to manage the hedgerow will be incurred beyond 3 years, but these costs should be minimal, consisting of mowing or spot treatments with herbicides and the occasional watering during the summer or drought years. NRCS cost-share programs are available to help plant hedgerows on farms, covering from 50 to 75 percent of the costs, depending on the program and hedgerow type.Outbreaks of the food pathogens Escherichia coli O157:H7 in leafy green vegetables, as well as outbreaks of serovars of Salmonella enterica in nut crops, have prompted a variety of preharvest food safety concerns about management practices including establishing wildlife habitat and related potential vector attractants to farms. These concerns are largely focused on raw horticultural foods. Although various types of E. coli naturally occur in many animals , research indicates that domestic cattle are the primary reservoir of toxin-forming pathogens such as E. coli O157:H7 associated with foodborne illnesses.