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. Author manuscript; available in PMC: 2024 May 22.
Published in final edited form as: Subst Use Misuse. 2023 May 22;58(9):1121–1131. doi: 10.1080/10826084.2023.2212282

Associations among Enacted Stigma, Perceived Chances for Success, Life Satisfaction, and Substance Use among Sexual Minority and Heterosexual Youth

Tim Janssen 1, Kristi E Gamarel 2, Ethan H Mereish 1,3, Suzanne M Colby 1, Michelle Haikalis 1, Kristina M Jackson 1
PMCID: PMC10388366  NIHMSID: NIHMS1917916  PMID: 37216278

Abstract

Objective:

Sexual minority youth report high rates of substance use compared to heterosexual youth. Stigma can diminish perceptions of future success and life satisfaction and contribute to elevated substance use. This study examined whether experiences of enacted stigma (i.e., discrimination) and substance use among sexual minority and heterosexual youth were indirectly associated through perceived chances for success and life satisfaction.

Method:

In a sample of 487 adolescents who indicated their sexual identity (58% female, M age = 16.0, 20% sexual minority), we assessed substance use status and factors that might explain sexual minority disparities in substance use. Using structural equation modeling, we examined indirect associations between sexual minority status and substance use status through these factors.

Results:

Compared to heterosexual youth, sexual minority youth reported greater stigma, which was associated with both lower perceived chances for success and life satisfaction, which were in turn associated with greater likelihood of substance use.

Conclusions:

Findings highlight the importance of attending to stigma, perceived chances for success, and general life satisfaction to understand and intervene to prevent substance use among sexual minority youth.

Keywords: sexual minority, adolescents, substance use, stigma, life satisfaction

Inequities in substance use have been consistently observed between sexual minority and heterosexual youth (Goldbach et al., 2014). As compared to their heterosexual peers, sexual minority youth report higher use of alcohol, cannabis, and cigarettes (Johns et al., 2018; Schuler et al., 2018). Further, sexual minority youth more often initiate substance use early and demonstrate more rapid increases in substance use over time compared to heterosexual youth (Marshal et al., 2009; Schuler & Collins, 2019). This is concerning, as early substance use initiation increases the chances of addiction later in life (Jackson et al., 2021) and has been associated with a range of negative outcomes, including poor school performance (Cox et al., 2007) and risky sexual behaviors (Elkington et al., 2010; Tapert et al., 2001).

The predominant conceptual framework for understanding adverse health outcomes among sexual minority populations is the minority stress model (Brooks, 1981; Meyer, 2003). The model posits that beyond general life stressors, sexual minority individuals—as a function of their minority status in a stigmatized group—are subject to excess stress due to prejudice, stigma, and social rejection (Brooks, 1981; Meyer, 2003). These experiences are posited to contribute to internalized stigma and result in mental health and substance use problems. Numerous studies have illustrated associations between enacted stigma (i.e., discrimination) and substance use among sexual minority youth (Poon et al., 2012; Saewyc et al., 2014; Swann et al., 2019). Further, prior studies have demonstrated that stigma and victimization can serve as indirect pathways in the link between sexual orientation and substance use (Dermody et al., 2016; Phillips et al., 2017).

Despite documented associations between enacted stigma and substance use among sexual minority youth, limited research has focused on the factors that might explain how enacted stigma may account for inequities in substance use between sexual minority and heterosexual youth. Repeated experiences of stigma, and other sources of adversity, such as general life stressors, can negatively affect perceptions of the likelihood of meeting one’s future goals and aspirations (Bauremeister, 2014) and can reduce life satisfaction (Toomey, 2010). By investigating whether enacted stigma relates to substance use via these intervening constructs, above and beyond the effect of general life stressors, this study seeks to build on past literature documenting substance use inequities to examine why they exist so that interventions may more effectively eliminate them.

Perceived Chances for Success

Life stressors and stigma have the potential to undercut individuals’ confidence in achieving personal goals. This is concerning because the pursuit of goals and goal attainment are consistently and positively related to health and well-being (Messersmith & Schulenberg, 2010; Oyserman et al., 2015) and is largely a function of one’s social context (Nurmi et al., 1994; Wang et al., 2019). A number of studies have demonstrated that adolescents and emerging adults living in adverse social conditions may develop a sense of hopelessness about their chances to succeed in life and may perceive that it is unlikely that they will achieve a variety of major life goals (Bolland, 2003; Griffin et al., 2004; Brumley et al., 2017). For example, social and environmental stressors such as poverty or neighborhood deprivation may inhibit perceived life chances (Jessor, 2016).

Sexual minority people are a stigmatized group that experiences added minority stressors (Brooks, 1981; Meyer, 2003). Experiences of enacted stigma (e.g., prejudice and discrimination) in one’s environment are one form of minority stress and may reduce perceptions of chances for success (Jessor & Jessor, 1977; Shaw et al., 2019). That is, youth who experience more enacted stigma may have greater difficulty envisioning opportunities for success in the future.

Substance use may follow from a lack of perceived chances for success in life (Jessor & Jessor, 1977; Varghese et al., 2015). Whether one’s perceptions of the likelihood of success in achieving personal goals and aspirations are shaped by broad life stressors such as poverty or neighborhood deprivation, or minority stress via experiences of enacted stigma, individuals are likely to be at increased risk for substance use as a function of greater stress (Hatzenbuehler et al., 2015). This points to perceptions of chances for success in life as a potential pathway through which youth sexual minority status confers disparities in the incidence of substance use. We speculate that this pathway statistically mediates the effect of enacted stigma on this incidence.

Perceptions of Life Satisfaction

Similar to perceived chances for success, an individual’s social environment has a powerful influence on perceptions of life satisfaction (Homel & Burns, 1989; Sastre & Ferrière, 2000; Povedano-Diaz et al., 2020). Perceptions of life satisfaction is a construct distinct from indices of mental health (Huebner, 1991) and refers to the cognitive evaluation of one’s life (Pavot & Diener, 2008). These perceptions are ongoing evaluations sensitive to changes in an individual’s life circumstances, which differ from their perceptions of their future lives (Pavot & Diener, 2008). Research has consistently found negative associations between stressful life events, including being bullied or feeling unsafe at school, and life satisfaction (Funk et al., 2006; Moksnes et al., 2016). Experiences of enacted stigma also have the potential to detract from stigmatized persons’ life satisfaction; for example, sexual identity-targeted school bullying before the age of 18 is shown to be associated with subsequent adulthood life satisfaction (van der Star et al., 2021).

Importantly, perceptions of life satisfaction can influence whether an individual engages in a particular emotional or coping response in the face of adverse events (Pavot & Diener, 2008). For example, youth may respond to low perceptions of life satisfaction by participating in risk-taking behaviors, such as substance use (Zullig et al., 2001; Esposito et al., 2020). To explore this theoretical process, we were interested in testing whether low perceptions of life satisfaction may also serve as a pathway through which sexual orientation confers disparities in the incidence of substance use. We speculate that this pathway statistically mediates the effect of enacted stigma on substance use.

The Current Study

The goal of the present study was to examine factors that might help account for sexual orientation disparities in substance use among youth. Specifically, we examined whether differences in the likelihood of substance use between sexual minority youth and their heterosexual counterparts may be accounted for by higher levels of reported enacted stigma, which in turn relate to reduced perceived chances for success and reduced life satisfaction. First, we hypothesized that sexual minority youth would report greater levels of enacted stigma compared to their heterosexual counterparts. Second, we hypothesized that experiences of enacted stigma would be related to fewer perceived chances for success and lower life satisfaction. Third, we hypothesized that fewer perceived chances for success and lower life satisfaction would relate to a higher likelihood of substance use. Fourth, we hypothesized that sexual minority status would be indirectly associated with substance initiation through enacted stigma and greater perceived chances for success or life satisfaction. Our first three hypotheses are based on evidence from the literature to date; the fourth hypothesis is theoretically grounded and is the first investigation of the factors through which enacted stigma exerts an influence on substance use behavior.

We hypothesized that these associations would hold over and above associations explained by sociodemographic differences and experiences of general stressors such as life stress, high school stress, and perceived stress. Although sexual minority youth have consistently been shown to report greater tobacco use compared to their heterosexual counterparts, results have been mixed regarding cannabis and alcohol use (Marshal et al., 2008). As such, we focus on four key substance use behaviors: cannabis use, cigarette smoking, alcohol consumption, and heavy drinking. We also focus on the very early stages of use, specifically whether the participant initiated a given substance use behavior, to understand whether earlier findings supporting an association between enacted stigma and substance use replicated at lower levels of use.

Methods

Participants and Procedure

We used data from a sample recruited for a larger study on adolescent substance use and health behaviors. Participants were recruited from one urban, three suburban, and two rural schools in Rhode Island, across five cohorts following an accelerated cohort design during 2009 – 2011. 24% of the sample 24% racial minority (5% Black, 3% Asian, 2% American Indian, 8% mixed race, 6% other), 12% of Hispanic/Latinx ethnicity, and 52% identified as female at enrollment (534 out of N = 1,023). The enrollment sample had a mean age of 12.5 years (range: 10–15 years old, SD = 0.95). The study consisted of 30- to 45-minute web-based surveys administered at six time points over an initial three years, followed by re-enrollment into a quarterly follow-up study that spanned an additional two to four years, or until participants graduated from high school. Of the original sample, 91% (N = 932) agreed to participate in the follow-up study. Further detail on this study is available in previously published work (Jackson et al., 2014, 2015; Janssen et al., 2018). Parents were mailed a series of annual paper-and-pencil surveys to complete, including at baseline and the start of the quarterly follow-up study.

Assessment Timing.

The sexual identity measure was added at the onset of the quarterly follow-up study and then administered annually, with a number of waves varying across participants as a function of date and age at enrollment. Although the parent study contains multiple waves, we focused on three time points in the present study to maximize consistent spacing and temporal ordering to the extent possible. Measures were pulled from three preselected time points (Time 1 – Time 3; each half a year apart) based on the timing of when each assessment was administered. To optimize temporal ordering between putative mediators and outcomes, all putative mediators were assessed at the survey time point most proximal to, or coinciding with, the first sexual identity assessment (Time 1). The measure of enacted stigma was taken from the time point when it was added to the study and assessed the experience of discrimination over the prior year (collected one year following the sexual identity measure assessment; Time 3). Measures of life stress, perceived stress, perceived chances for success, life satisfaction, and substance use were assessed at the time that the sexual identity assessment was first added (Time 1), with the exception of high school stressors which were assessed a half year later (Time 2). Substance use outcomes were also assessed at Time 3. Baseline substance use (Time 1) was a covariate in analyses to allow for examination of the unique impact of study variables on post-baseline substance use.

Analytical Sample.

The analytical sample was defined as those participants who had completed at least one assessment of the sexual identity measure (n = 487; M age = 16.0 years, SD = 0.75). Since some participants (in the earliest study cohorts; n = 305) had graduated from high school by this point and did not receive the measure, this missingness was primarily a consequence of the study design. Those participants who completed the sexual identity measure did not differ from the remainder on race, ethnicity, or socioeconomic status. However, as compared to those not completing a measure of sexual identity, those who completed the measure were significantly more likely to be female (n = 284 of 487 girls, versus n = 203 of 487 boys included, χ2(1) = 13.94, p < .001), and significantly younger (F(901) = 23.17, p < .001, the latter by design). All study procedures were approved by the University’s Institutional Review Board.

Measures

Sexual Identity (Time 1 – Time 3).

Based on the Williams Institute guidelines (Badgett, 2009), participants were asked to report their sexual identity by indicating whether they considered themselves to be ‘Heterosexual or Straight,’ ‘Gay or Lesbian,’ or ‘Bisexual.’ Approximately 7% of participants self-reported a sexual identity other than heterosexual at any time point, with the most common change over time identifying first as straight, then bisexual (n =11), and another group who endorsed multiple changes (n =12). Given the small number of participants who identified as Gay or Lesbian (n = 18) or Bisexual (n = 46) or self-reported a different sexual identity across the time points (n = 32), we dichotomized this variable into sexual minority youth (‘Gay or Lesbian’ or “Bisexual’) versus heterosexual youth in indirect models. Participants who indicated a sexual minority identity at any survey wave were considered a sexual minority person for this dichotomization.

Substance Use (Time 1 – Time 3).

We asked whether or not participants had ever used cannabis in any form, had ever smoked a cigarette (at least a puff), had ever had a standard full drink other than at a religious occasion, and had ever engaged in heavy drinking (defined as consuming 3 or more standard drinks on a single occasion; Donovan, 2009). Each of these indications was coded 0 (No) or 1 (Yes). The model included two sets of substance use variables: baseline substance use as a covariate and Time 3 substance use as the outcome.

Enacted Stigma (Time 3).

The widely used 9-item Everyday Discrimination Scale was used to assess everyday experiences of enacted stigma (e.g., “You are treated with less respect than other people are”) based on different social identities (e.g., sexual identity, race, gender) (Williams et al., 1997). Participants were asked to rate the frequency of each experience in their “day-to-day life” on a scale: (0) ‘Never’; (1) ‘Less than once a year;’ (2) ‘A few times a year;’ (3) ‘A few times a month;’ (4) ‘At least once a week;’ (5) ‘Almost every day.’ Consistent with more recent intersectional discrimination quantitative research (Bauer & Scheim, 2019), we created a mean score based on all nine items to assess the overall experience of enacted stigma given that attributions of discrimination are subjective and can vary based on events, situations, and characteristics of the individual. Internal consistency was high (α = .96).

Putative Mediators (Time 1).

Perceived Chances for Success was assessed with a set of items assessing beliefs about how participants perceive they will do later in life (Elliott et al., 2012). The scale consists of seven items (e.g., having a good job or career; earn a good living), for which participants first rated their aspirations, that is, how important an event is (“How important is it to you…”), ranging from (1) ‘Not at all important’ to (5) ‘Very important.’ Participants were then asked about their perception of their ability to achieve this aspiration (“What do you think your chances are…”) on a scale ranging from (1) ‘Poor’ to (5) ‘Excellent.’ The present study uses a mean score for the seven expectations items such that higher scores indicate greater optimism concerning future success. Internal consistency was high (α = .92).

We assessed Perceived Life Satisfaction using five items from the Satisfaction with Life Scale (Diener et al., 1985), with participants rating agreement with statements (e.g., “So far I have gotten the important things I want in life”) on a scale from (1) ‘Strongly agree’ to (7) ‘Strongly disagree.’ We created a mean score based on all five items. Internal consistency was high (α = .93).

Covariates (Time 1 – Time 2).

Participants reported their gender identity, race, and ethnicity at baseline (in years 2009–2011). Gender identity was assessed with an item that allowed for two response options, “male” and female.” We calculated age to reflect the participant’s age at the time of the first sexual identity survey, based on baseline reports on date of birth and survey completion date. Parent reports indicating whether their child was eligible for free or reduced-price lunch were used as a measure of socioeconomic status (SES). To assess general stressors, participants reported on three measures: Life Stress, High School Stressors, and Perceived Stress. For the first of these three measures, we used a 24-item Life Stress Scale (Bobo et al., 1986), which included different domains of life (e.g., “My schedule / having too many responsibilities”), on which participants were asked to rate how much stress in their life was introduced by each domain, on a range from (0) ‘None’ to (3) ‘Large/major stress.’ We created a mean score based on all 24 items. Internal consistency was high (α = .92). Next, we used 30 items from the 35-item High School Stressor Scale (Burnett & Fanshawe, 1997). We created a mean score based on the item means across four subscales (9 items assessing Demands, 9 for Responsibility, 5 for Environment, and 7 for Independence) since internal consistency among all 30 items was high (α = .96). For each item, participants were asked to rate: “To what extent is each of the following a problem for you?”, with items listing examples of stressors (e.g., “Noisy classrooms”), to be rated on a scale ranging from (1) ‘No problem at all’ to (7) ‘A very big problem.’ Finally, we used the Perceived Stress Survey (Box, 2017). For 10 items, participants were asked to rate: “In the past month, please describe how often have you…”, with scenarios (e.g., “Felt difficulties were piling up so high that you could not overcome them?”) to be rated from (1) ‘Never’ to (5) ‘Very often.’ We created a mean score based on all 10 items as an indicator of Perceived Stress. Internal consistency was high (α = .87).

Analysis Strategy

After exploring descriptive statistics (e.g., means, frequencies), we examined the unconditional fit of a single latent factor model of General Stress, using mean scores of Life Stress, High School Stressors, and Perceived Stress as indicators. We investigated measurement equivalence among sexual minority youth compared to heterosexual youth by examining differential item functioning (DIF) for item intercepts, factor loadings, and factor variance. We found adequate fit for a single latent factor model of general stress (Comparative Fit Index (CFI): .973, Standardized Root Mean Square Residual (SRMR): .037, Root Mean Square Error of Approximation (RMSEA): .093; 90% RMSEA CI: .043 - .153) (Hu & Bentler, 1999), and we found no significant DIF based on sexual identity, although sexual minority youth scored higher on the general stress factor than heterosexual youth in the DIF model. Thus, we used factor scores from these models as statistical mediators in subsequent models.

We investigated indirect associations in two sets of structural equation models (see Figures 1 and 2 for diagrams). As shown in these Figures, whereas Enacted Stigma was assessed at Time 3, it is used to predict Perceived Chances for Success and Life Satisfaction assessed at T1. Sexual Identity indicates whether participants endorsed non-straight sexual identity at any time point (see Assessment Timing, above). To test whether sexual minority status would be indirectly associated with substance initiation through enacted stigma and greater levels of perceived chances for success or life satisfaction (fourth hypothesis), we included two sets of sequential pathways. We ran two separate models due to the highly intercorrelated nature of life satisfaction and perceptions of chances of success.

Figure 1.

Figure 1.

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Estimates from model investigating mediation by perceived chances for success of the effects of sexual identity on likelihood of substance use (n = 487).

Note. Estimates for arrows leading to mediators are on a linear scale. Estimates above the division line are adjusted only for baseline substance use; those below are adjusted for race, age, ethnicity, gender, and SES. T: Time point.

Figure 2.

Figure 2.

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Estimates from model investigating mediation by life satisfaction of the effects of sexual identity on likelihood of substance use (n = 487).

Note. Estimates for arrows leading to mediators are on a linear scale. Estimates above the division line are adjusted only for baseline substance use; those below are adjusted for race, age, ethnicity, gender, and SES. Black lines indicate significant estimates; grey lines indicate non-significant estimates. T: Time point.

During initial analysis, we became concerned that the high multicollinearity (r = .43) between these two would lead to suppression of their associations with substance use outcomes. For this reason, their indirect associations are presented separately. The supplemental materials include additional analyses in which life satisfaction and perceptions of chances of success are included in a single model. Both sets included Enacted Stigma, as a mediator, predicted by sexual identity. Second, the models featured either Perceived Chances for Success or Perceived Life Satisfaction (Models 1 and 2, respectively, predicted by sexual identity and Enacted Stigma, controlling for General Stress). Finally, four dichotomous outcomes represented the use of cannabis, cigarette puffing, full drink, and heavy drinking. Outcomes were predicted by sexual identity, General Stress, and Enacted Stigma, plus either Perceived Chances for Success or Perceived Life Satisfaction. This allowed us to test sequential indirect models, specifically whether enacted stigma statistically mediated the indirect association of sexual identity with perceived chances for success or life satisfaction and whether perceived chances for success or life satisfaction, in turn, statistically mediated the indirect association of enacted stigma with substance use (see Figure 1a). Both unadjusted (controlling only for general stressors and baseline substance use levels) and adjusted models (further controlling for gender, race, ethnicity, socioeconomic status, and age) were estimated, with 5000 bias-corrected bootstrapped estimates of indirect associations as the products of individual association effects (Preacher & Hayes, 2008). All analyses were performed in Mplus 8.0 (Muthén & Muthén, 2012).

In order to estimate the size and significance of indirect associations, we examined estimates for all individual combinations of paths through mediators (i.e., testing both single and sequential indirect effects). Missing data were handled using the Full Information Maximum Likelihood estimator with missing data assumed MAR (Enders & Bandalos, 2009). Data are not publicly available but may be made privately available upon reasonable request to the study PI (KMJ). This study was not preregistered.

Results

Descriptive information and bivariate relations

Table 1 details descriptive information for study variables and correlations between predictors. Table 2 contains further information on sexual identity patterns (descriptions of frequencies with which participants indicated sexual identity and how much this varied over time).

Table 1.

Descriptive characteristics of the sample and bivariate correlations (n = 487).

Prevalence
LGB status Minority subgroups Correlations
M or % SD Range (min max) Straight (n = 391) Non-Straight (n = 96) Bisexual (n = 46) Gay or Lesbian (n = 18) In Flux (n = 32) 2 3 4 5 6
Gender (Male) 42% 45%ab 29%a 20%bc 50%** 31%c
Ethnicity (Hispanic) 14% 13% 19% 17% 17% 22%
Race (White) 81% 82% 76% 76% 78% 75%
Age 15.96 0.75 (14.50 18.50) 15.95 16.02 16.10 15.97 15.92
SES (% using lunch subsidy) 32% 29%de 42%d 38% 35% 53%e
1. Enacted stigma 1.16 1.42 (0.00 5.00) 1.08f 1.54f 1.47 1.72 1.56 .23** .21** .16** −.23** −.28**
2. Life Stress 0.82 0.59 (0.00 2.88) 0.78gh 1.01g 1.13hi 1.10 0.81i .36** .64** −.30** −.09*
3. High School Stressors 2.98 1.20 (1.00 6.67) 2.95 3.13 3.33 3.17 2.86 .24** −.21** −.11**
4. Perceived Stress 2.63 0.88 (1.00 5.00) 2.58jk 2.85j 3.01kl 3.07 2.51l −.11** −.02
5. Life Satisfaction 4.82 1.51 (1.00 7.00) 4.94mn 4.31m 4.19n 4.32 4.47 .43**
6. Perceived Chances for Success 4.47 0.58 (1.00 5.00) 4.52op 4.27o 4.24p 4.23 4.34 --
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Note. Significance tests between sexual identity and sociodemographic differences are based on chi-square tests of independence. Pairs of superscript letters indicate significant mean differences between pairs (e.g. h indicates that means for participants indicating “Straight” were significantly different from those indicating “Bisexual” on the life stress scale), at p < .05. “In Flux” refers to participants who indicate changes in sexual identity between time points (see Table 2).

Table 2.

Sexual identity and associated substance use initiation risk (n = 487).

Full Drink Heavy Drinking Cigarette Puffing Cannabis Use
Orientation Trajectories N % % % %
Overall 487 34.8 21.47 16.35 25.52
Straight 391 32.03 20.1 12.5 23.32
Bisexual 46 45.45 23.26 38.64 37.21
Gay or Lesbian 18 35.29 23.53 11.76 17.65
In Flux 32 53.13 34.38 34.38 40.63
Substance Use Comparisons X2 p-value
 Straight vs. Bisexual .585 .197 .045 .709
 Straight vs. Gay or Lesbian .241 .424 .210 .149
 Straight vs. In Flux .702 .844 .234 .470
 Bisexual vs. Gay or Lesbian .472 .982 .042 .142
 Bisexual vs. In Flux .509 .289 .704 .764
 Gay or Lesbian vs. In Flux .234 .433 .088 .103
 Overall .041 .295 <.001 .037
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Note. “In Flux” refers to having indicated a minority sexual identity at some time point in the study, but not consistently. Specifically, 11 participants indicated being straight, then bisexual, 7 indicated bisexual, then straight, 1 indicated bisexual, then gay/lesbian, 1 indicated gay/lesbian, then bisexual, and 12 indicated multiple changes. Numbers in bold indicate significant group differences in substance use comparisons.

Direct Associations

As shown in Table 2, sexual identity was significantly associated with the likelihood of having had a full drink of alcohol, having puffed a cigarette, and having used cannabis (ps = .041, <.001, and .037, respectively), such that heterosexual adolescents were less likely to have endorsed using these substances than sexual minority youth. Sexual identity was not related to likelihood of having ever engaged in heavy drinking (p = .295). Further, a greater proportion of bisexual youth had puffed a cigarette compared to both heterosexual or lesbian and gay youth.

Figures 1 and 2 present estimates for direct associations in our models, featuring life satisfaction and perceived changes of success, respectively. Hypotheses specific to the adjusted associations were examined, accounting for all other variables in the model. Consistent with Hypothesis 1, sexual minority youth reported greater enacted stigma than heterosexual youth (B = .31, p = .022). Greater enacted stigma was associated with having reported fewer perceived chances of success (B= −0.27, p <.001) and lower life satisfaction (B= −0.18, p <.001; Hypothesis 2). In partial support of Hypothesis 3, lower levels of perceived chances of success were related to a greater likelihood of cigarette use (B = −.031; p = .018), and lower life satisfaction was related to a greater likelihood of all but cigarette use (Bs ranged from −.268 to −.289, ps <.05).

Indirect Associations

Associations through Perceived Chances for Success.

Figure 1 shows results from the models featuring perceived chances for success. Table 3 shows the covariate-adjusted estimates of bootstrapped indirect associations for these analyses. For alcohol use, the indirect association of sexual identity with substance use, via enacted stigma, was also significant (Bind= .106). Finally, for cigarette initiation, the indirect association of sexual identity with substance use, via enacted stigma, was significant (Bind= .108). We conducted post-hoc analyses to investigate whether depressive symptoms explained the link between enacted stigma, life satisfaction, perceived chances for success, and substance use outcomes. In two models, we controlled for the effect of number of parent-reported child internalizing problems on the Child Behavior Checklist (Achenbach, 1999) on each outcome. This covariate did not meaningfully change the strength, direction, or significance of focal effects of sexual identity, stigma, life satisfaction, or perceived chances for success in either of the models (For Model 1, Wald X2(4, N=487)=2.347, p = .672; for Model 2, Wald X2(4, N=487)= 2.303, p = .680; none of the effects of depression on substance use outcomes were significant). Exploratively, in partial support of the fourth hypothesis, the sequential indirect association of sexual identity on substance use through enacted stigma and perceived chances for success was significant for the outcomes of having a full drink of alcohol, cigarette, and cannabis use but not heavy drinking (Bind= .021, .032, and .021, respectively).

Table 3.

Estimated bootstrapped indirect associations from models predicting substance initiation from sexual identity, for perceived chances for success (n = 487).

BOOTSTRAPPED CONFIDENCE INTERVAL
Outcome Mediators 99% LL 95% LL mean 95% UL 99% UL
Separate Mediators
Any Full Drink Enacted Stigma −.014 .013 .106 .257 .330
Perceived Chances for Success −.021 −.002 .073 .228 .295
Heavy Drinking Enacted Stigma −.092 −.031 .068 .219 .283
Perceived Chances for Success −.072 −.035 .045 .167 .229
Cigarette Initiation Enacted Stigma −.121 −.057 .051 .219 .294
Perceived Chances for Success −.009 .011 .108 .270 .347
Cannabis Initiation Enacted Stigma −.120 −.073 .028 .160 .200
Perceived Chances for Success −.032 −.003 .073 .207 .266
Exploratory Sequential Mediators
Any Full Drink Enacted Stigma → Perceived Chances for Success −.004 .001 .021 .068 .092
Heavy Drinking Enacted Stigma → Perceived Chances for Success −.020 −.009 .013 .052 .067
Cigarette Initiation Enacted Stigma → Perceived Chances for Success >.000 .006 .032 .091 .113
Cannabis Initiation Enacted Stigma → Perceived Chances for Success −.007 .001 .021 .065 .084
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Note: Reported effects are mean and confidence intervals of bias-corrected bootstrapped mediation effects. Effects are those from adjusted models in which baseline use, gender, age, race, ethnicity, and SES were included as covariates. LL: Lower Limit. UL: Upper Limit. Numbers in bold indicate indirect associations with bootstrapped confidence intervals not containing zero, indicating significance.

Associations through Life Satisfaction.

Figure 2 shows results from the models featuring life satisfaction as a mediator. Table 4 shows the adjusted estimates of bootstrapped indirect associations for these analyses. For having a full drink of alcohol use, the indirect association of sexual identity with substance use, via enacted stigma, was also significant (Bind= .108). Exploratively and in partial support of the fourth hypothesis, there was a significant sequential indirect association of sexual identity with substance use through both enacted stigma and life satisfaction. This sequential indirect association was significant for having a full drink of alcohol, heavy drinking, cannabis use (Bind= .017, .019, .019, respectively), and for cigarette use (Bind= .017, 95% CI = >.000, .059).

Table 4.

Estimated bootstrapped indirect associations from models predicting substance initiation from sexual identity, for life satisfaction (n = 487).

BOOTSTRAPPED CONFIDENCE INTERVAL
Outcome Mediators 99% LL 95% LL mean 95% UL 99% UL
Separate Mediators
Any Full Drink Enacted Stigma −.009 .014 .108 .257 .332
Life Satisfaction −.024 −.004 .059 .192 .252
Heavy Drinking Enacted Stigma −.075 −.024 .068 .206 .264
Life Satisfaction −.027 −.005 .064 .210 .273
Cigarette Initiation Enacted Stigma −.084 −.029 .070 .237 .304
Life Satisfaction −.029 −.009 .059 .229 .294
Cannabis Initiation Enacted Stigma −.095 −.053 .035 .158 .201
Life Satisfaction −.024 −.003 .064 .210 .264
Exploratory Sequential Mediators
Any Full Drink Enacted Stigma → Life Satisfaction −.002 .002 .017 .054 .069
Heavy Drinking Enacted Stigma → Life Satisfaction −.003 .002 .019 .062 .079
Cigarette Initiation Enacted Stigma → Life Satisfaction −.007 >.000 .017 .059 .080
Cannabis Initiation Enacted Stigma → Life Satisfaction <.000 .003 .019 .060 .077
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Note: Reported effects are mean and confidence intervals of bias-corrected bootstrapped mediation effects. Effects are those from adjusted models in which baseline use, gender, age, race, ethnicity, and SES were included as covariates. LL: Lower Limit. UL: Upper Limit. Numbers in bold indicate indirect associations with bootstrapped confidence intervals not containing zero, indicating significance.

Additional Exploratory Analyses

Post-hoc investigation was conducted, exploring whether sequential indirect associations revealed by previous analyses were moderated by gender identity (male or female). To examine moderation, Model 1 and Model 2 were each separately re-run, allowing moderation in the relation between sexual identity and enacted stigma, between enacted stigma and perceived chances for success, and between perceived chances for success and the substance use outcomes, as well as residual direct effects of sexual identity and perceived chances for success, sexual identity, and substance use outcomes, and enacted stigma and substance use outcomes. We examined moderation in two manners: one assuming moderators had equal effects on all different substance use outcomes (6 total added parameters) and one assuming moderators had independent effects (15 total added parameters).

Models featuring six added parameters were uniformly preferred in terms of omnibus goodness-of-fit criteria as compared to models featuring 15 added parameters. Comparing models without moderation to those with six added parameters, there was inconsistent evidence for moderation by gender based on relative goodness-of-fit statistics. In both models, statistics were inconsistent in that the Chi-Square-based Wald test supported the inclusion of moderator effects, whereas the Bayesian Information Criterion (BIC) did not. In the moderation model featuring life Satisfaction, moderation estimates were such that 1) sexual identity was associated with lower life satisfaction much more strongly among women than men; 2) enacted stigma was associated with a greater likelihood of substance use more strongly among women than men. In the moderation model featuring perceived chances for success, sexual identity was associated with lower life satisfaction much more strongly among women than men.

Discussion

The current study examined whether youth sexual identity was indirectly associated with greater likelihood of substance use via enacted stigma, perceived chances for success, and life satisfaction. Substance use inequities persist (Goldbach et al., 2014; Marshal et al., 2008), and this trend may continue in the future due to greater stigmatization of sexual minority communities in the U.S. in recent years. Prior studies have shown that adolescents and emerging adults who can envision a successful future and are satisfied with their lives are less likely to engage in risk behaviors (Bauremeister, 2014; Zullig et al., 2001), including reduced tobacco use (Gamarel et al., 2020). Further, prior studies have shown that victimization and stigma can serve as indirect pathways through which sexual minority status can confer risk for substance use disparities (Dermody et al., 2016; Phillips et al., 2017). Our results link and extend these findings by showing associations with even the earliest stages of substance use and lend support to a theoretical process by which enacted stigma—more commonly experienced by sexual minority youth—may create a barrier to envisioning opportunities for success in the future and may reduce one’s satisfaction with life, which may, in turn, increase the likelihood of alcohol, cigarette, and cannabis use. The major caveat to these findings is that although we offer evidence of indirect associations, the data were not consistently temporally ordered (see Figures). As such, we caution against interpreting these findings as evidence of causality. Such evidence would require observing changes in putative mediators subsequent to endorsing a sexual minority identity, which was not possible with current data. Nevertheless, we assert that our data demonstrate evidence of associations along a credible risk pathway for substance use among sexual minority youth. Further, exploratory analyses suggest that such associations may occur more strongly among women than men. However, this explorative finding was inconsistent and should be examined in more detail in future studies.

The present findings are consistent with the general processes theorized to account for substance use and other mental health inequities among sexual minority youth. Specifically, sexual minority youth experience greater minority stress in the form of stigma, which contributes to psychopathology via internalization and anticipation of those experiences in the form of emotion dysregulation, interpersonal difficulties, and cognitive processes (Hatzenbuehler, 2009). In this work, we focused on putative mediating factors specific to cognition in the form of perceptions of likelihood of success and evaluations of one’s life satisfaction. It is plausible that youth who internalize or anticipate negative societal messages may be at greatest risk for lower life satisfaction and perceptions of future success. Further, depression is one of the most common disorders in adolescence (Ghandour et al., 2019) and is a likely contributing factor to victimization and stigma experiences. Although post-hoc analyses in this study controlled for parent-reported internalizing behaviors, future research should be sensitive to the role of depression, anxiety, and post-traumatic stress symptoms and their impact on perceptions of future and quality of life to guide future substance use interventions designed with sexual minority youth.

Importantly, as noted in seminal work describing intersectionality in oppression among Black women, individuals possess intersecting social identities based on dynamics rooted in power and privilege (Crenshaw, 1989). Systemic racism interlocks with other forms of oppression (e.g., heterosexism, cissexism, sexism, ableism) and is mutually constitutive and inextricably intertwined for many individuals (Bowleg, 2008). The present work statistically controlled for the effects of gender, race, ethnicity, socioeconomic status, and age due to sample size constraints. In addition, we explored the potential moderating role of gender, suggesting that it is plausible that heterosexism and sexism could potentially interact to further substance use inequities. However, future research should more fully incorporate an intersectional lens to understand how structural racism intersects with other forms of structural stigma (e.g., heterosexism) and may contribute to substance use inequities among sexual minority youth (English, Boone, Carter, et al., 2022).

Given documented sexual orientation inequities in substance use and our findings demonstrating that sexual minority youth reported higher enacted stigma and stress than their heterosexual peers, continued efforts are urgently needed to offset negative societal messages surrounding sexual minority communities. For example, our findings support the value of promoting programming such as Genders & Sexualities Alliances (GSAs) as well as social media campaigns, which inspire both sexual minority youth and their heterosexual peers with messages of social inclusion and justice, as well as positive role models and change societal norms. In the face of continued experiences of stigma, some youth—those who more highly value and identify with their minority group—report greater stigma resistance (Yip & Chan, 2021). In turn, stigma resistance is related to less internalization of experienced stigma and a greater sense of self-empowerment (Yip & Chan, 2021), which may also serve as a buffer against substance use.

Strengths and Limitations

The current study benefited from multiple strengths, including a sample of youth that was relatively large compared to other studies examining mediation reported by Fritz and MacKinnon (2007). The study used well-validated measures and adjusted for covariates and general stress as well as baseline substance use. Capitalizing on these strengths, we provided a test of sequential indirect associations that represent a plausible theoretical direction of the mechanisms underlying substance use inequities among sexual minority youth.

However, certain limitations should be noted. First, there was a partial mismatch between our hypothesized model and the temporal ordering of variables as assessed in the parent study. Sexual identity was assessed throughout the study, and enacted stigma was assessed before perceived chances of success and life satisfaction, which was due to the parent study not being designed with the current research in mind. However, this is assuaged by the fact that the Wave 3 assessment of enacted stigma required that participants recall over the past year, making it more commensurate with the variables collected at Wave 1. Hence, the results should not be taken as indicative of causality. Nonetheless, the current work represents an innovative first important step in this area, setting the stage and offering an approach for replication using fully temporally-ordered data. Second, due to the low number of participants who identified as sexual minorities, we were only powered to examine whether identifying as a sexual minority, compared to heterosexual, was broadly associated with increased risk for substance use and whether this risk was mediated. Future work should further elucidate these relations by examining which sexual minority group is at greatest risk and could further delve into changes in sexual identity, including those indicating they are unsure of their sexual identity. Additionally, we did not include comprehensive measurement to assess gender identity, such that we are not able to determine whether a young person identified as transgender or nonbinary. Future research is warranted that uses better gender identity measurement to understand potential gender differences in the association between sexual orientation identity and substance use inequities. Finally, our measures of substance use were limited to lower-risk stages of early substance use; future studies would benefit from examining this subject in a sample of regular substance users to further elucidate dose-response relationships.

Conclusions and Implications

We conclude that enacted stigma, perceived chances for future success, and life satisfaction may serve as mechanisms that underlie sexual minority young people’s greater risk for substance use initiation compared to their heterosexual peers. Increased exposure to enacted stigma, compared to heterosexual youth, may undermine sexual minority youth’s perceived chances for future success and life satisfaction, thereby diminishing the protective effects that hopeful cognitive appraisals of life have on health and well-being.

Continued policy and advocacy efforts are needed to challenge stigma to ensure equity for sexual minority youth and alleviate sexual orientation inequities in substance use. There is a need for advocacy to raise awareness of structural societal factors, including laws and social attitudes, which are important determinants of sexual orientation inequities. Specifically, anti-stigma policies are protective against substance use in sexual minority youth (Hatzenbuehler et al., 2015) and may also increase perceived chances for success in life and life satisfaction.

In terms of clinical practice, clinicians may benefit from attending to sexual minority youth’s cognitive appraisals of their life and helping them overcome barriers to envisioning and achieving their future goals in order to intervene in or prevent substance use. Further resources clinicians can utilize may include programming such as GSAs –more commonly known as gender-sexuality alliances using messages of social inclusion and justice. Awareness of and encouragement of stigma resistance may empower sexual minority youth to overcome such barriers to their future goals.

Supplementary Material

Supp 1

Public Significance Statement.

Findings point to the importance of stigma and its relation to life satisfaction and perceived chances of success, which may constitute pathways through which sexual minority youth inequities in substance use arise and persist. Such pathways underscore the need for interventions promoting social inclusion, positive role models, and changes to societal norms.

Acknowledgments:

This work was supported by the by the National Institute on Alcohol Abuse and Alcoholism under R01 AA016838 (PI: Jackson); K01 AA026335 (PI: Janssen); under K08 AA025011 (PI: Mereish); under K02 AA13938 (PI: Jackson) and under T32 AA007459 (PI: Monti). The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. Data are not publicly available. This study was not preregistered. The authors report there are no competing interests to declare.

References

  1. Badgett MV (2009). Best practices for asking questions about sexual orientation on surveys. https://escholarship.org/uc/item/706057d5
  2. Bauer GR, & Scheim AI (2019). Advancing quantitative intersectionality research methods: Intracategorical and intercategorical approaches to shared and differential constructs. Social Science & Medicine, 226, 260–262. 10.1016/j.socscimed.2019.03.018 [DOI] [PubMed] [Google Scholar]
  3. Bauremeister JA (2014). How statewide LGB policies go from “under our skin” to “into our hearts”: Fatherhood aspirations and psychological well-being among emerging adult sexual minority men. Journal of Youth and Adolescence, 43(8), 1295–1305. 10.1007/s10964-013-0059-6 [DOI] [PMC free article] [PubMed] [Google Scholar]
  4. Bobo JK, Gilchrist LD, Elmer JF, Snow WH, & Schinke SP (1986). Hassles, role strain, and peer relations in young adolescents. The Journal of Early Adolescence, 6(4), 339–352. 10.1177/0272431686064005 [DOI] [Google Scholar]
  5. Bolland JM (2003). Hopelessness and risk behaviour among adolescents living in high-poverty inner-city neighbourhoods. Journal of Adolescence, 26(2), 145–158. 10.1016/S0140-1971(02)00136-7 [DOI] [PubMed] [Google Scholar]
  6. Bowleg L (2008). When Black+ lesbian+ woman? Black lesbian woman: The methodological challenges of qualitative and quantitative intersectionality research. Sex roles, 59(5), 312–325. 10.1007/s11199-008-9400-z [DOI] [Google Scholar]
  7. Box, N. I. H. T. (2017). NIH Toolbox Perceived Stress Survey Technical Manual.
  8. Brooks VR (1981). Minority stress and lesbian women. Lexington Books. [Google Scholar]
  9. Brumley LD, Jaffee SR, & Brumley BP (2017). Pathways from childhood adversity to problem behaviors in young adulthood: The mediating role of adolescents’ future expectations. Journal of youth and adolescence, 46(1), 1–14. 10.1007/s10964-016-0597-9 [DOI] [PubMed] [Google Scholar]
  10. Burnett PC, & Fanshawe JP (1997). Measuring school-related stressors in adolescents. Journal of Youth and Adolescence, 26(4), 415–428. 10.1023/A:1024529321194 [DOI] [Google Scholar]
  11. Cox RG, Zhang L, Johnson WD, & Bender DR (2007). Academic performance and substance use: Findings from a state survey of public high school students. Journal of School Health, 77(3), 109–115. 10.1111/j.1746-1561.2007.00179.x [DOI] [PubMed] [Google Scholar]
  12. Crenshaw K (1991). Mapping the margins: intersectionality, identity politics, and violence against women of color. In Fineman MA & Mykitiuk R (Eds.), The Public Nature of Violence: the Discovery of Domestic Abused (pp. 93–117). Routledge. [Google Scholar]
  13. Dermody SS, Marshal MP, Burton CM, & Chisolm DJ (2016). Risk of heavy drinking among sexual minority adolescents: Indirect pathways through sexual orientation-related victimization and affiliation with substance-using peers. Addiction, 111(9), 1599–1606. 10.1111/add.13409 [DOI] [PMC free article] [PubMed] [Google Scholar]
  14. Diener ED, Emmons RA, Larsen RJ, & Griffin S (1985). The satisfaction with life scale. Journal of Personality Assessment, 49(1), 71–75. 10.1207/s15327752jpa4901_13 [DOI] [PubMed] [Google Scholar]
  15. Donovan JE (2009). Estimated blood alcohol concentrations for child and adolescent drinking and their implications for screening instruments. Pediatrics, 123(6), e975–e981. 10.1542/peds.2008-0027 [DOI] [PMC free article] [PubMed] [Google Scholar]
  16. Elkington KS, Bauermeister JA, & Zimmerman MA (2010). Psychological distress, substance use, and HIV/STI risk behaviors among youth. Journal of Youth and Adolescence, 39(5), 514–527. 10.1007/s10964-010-9524-7 [DOI] [PMC free article] [PubMed] [Google Scholar]
  17. Elliott DS, Huizinga D, & Menard S (2012). Multiple problem youth: Delinquency, substance use, and mental health problems. Springer Science & Business Media. [Google Scholar]
  18. Enders CK, & Bandalos DL (2009). The Relative Performance of Full Information Maximum Likelihood Estimation for Missing Data in Structural Equation Models. Structural Equation Modeling, 8(3), 430–457. 10.1207/S15328007SEM0803_5 [DOI] [Google Scholar]
  19. English D, Boone CA, Carter JA, Talan AJ, Busby DR, Moody RL, … & Rendina HJ (2022). Intersecting structural oppression and suicidality among black sexual minority male adolescents and emerging adults. Journal of research on adolescence, 32(1), 226–243. 10.1111/jora.12726 [DOI] [PMC free article] [PubMed] [Google Scholar]
  20. Esposito M, Ferrara M, Panzaru C, & De Vito E (2020). The Relationship between Life Satisfaction and Risk Behaviors: A Cross-Cultural Analysis of Youth. Advances in Applied Sociology, 10(9), 356–368. 10.4236/aasoci.2020.109022 [DOI] [Google Scholar]
  21. Fritz MS, & MacKinnon DP (2007). Required sample size to detect the mediated effect. Psychological Science, 18(3), 233–239. 10.1111/j.1467-9280.2007.01882.x [DOI] [PMC free article] [PubMed] [Google Scholar]
  22. Funk BA, Huebner ES, & Valois RF (2006). Reliability and validity of a brief life satisfaction scale with a high school sample. Journal of Happiness Studies, 7(1), 41–54. 10.1007/s10902-005-0869-7 [DOI] [Google Scholar]
  23. Gamarel KE, Finer Z, Resnicow K, Green-Jones M, Kelley E, Jadwin-Cakmak L, & Outlaw A (2020). Associations between internalized HIV stigma and tobacco smoking among adolescents and young adults living with HIV: The moderating role of future orientations. AIDS and Behavior, 24(1), 165–172. 10.1007/s10461-019-02567-9 [DOI] [PubMed] [Google Scholar]
  24. Ghandour RM, Sherman LJ, Vladutiu CJ, Ali MM, Lynch SE, Bitsko RH, & Blumberg SJ (2019). Prevalence and treatment of depression, anxiety, and conduct problems in US children. The Journal of pediatrics, 206, 256–267. 10.1016/j.jpeds.2018.09.021 [DOI] [PMC free article] [PubMed] [Google Scholar]
  25. Goldbach JT, Tanner-Smith EE, Bagwell M, & Dunlap S (2014). Minority stress and substance use in sexual minority adolescents: A meta-analysis. Prevention Science, 15(3), 350–363. 10.1007/s11121-013-0393-7 [DOI] [PubMed] [Google Scholar]
  26. Griffin KW, Botvin GJ, Nichols TR, & Scheier LM (2004). Low perceived chances for success in life and binge drinking among inner-city minority youth. Journal of Adolescent Health, 34(6), 501–507. 10.1016/j.jadohealth.2003.07.026 [DOI] [PubMed] [Google Scholar]
  27. Hatzenbuehler ML, Jun H-J, Corliss HL, & Austin SB (2015). Structural stigma and sexual orientation disparities in adolescent drug use. Addictive Behaviors, 46, 14–18. 10.1016/j.addbeh.2015.02.017 [DOI] [PMC free article] [PubMed] [Google Scholar]
  28. Homel R, & Burns A (1989). Environmental quality and the well-being of children. Social Indicators Research, 21(2), 133–158. 10.1007/BF00300500 [DOI] [Google Scholar]
  29. Hu L, & Bentler PM (1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling: A Multidisciplinary Journal, 6(1), 1–55. 10.1080/10705519909540118 [DOI] [Google Scholar]
  30. Huebner ES (1991). Further validation of the Students’ Life Satisfaction Scale: The independence of satisfaction and affect ratings. Journal of Psychoeducational Assessment, 9(4), 363–368. 10.1177/073428299100900408 [DOI] [Google Scholar]
  31. Jackson KM, Colby SM, Barnett NP, & Abar CC (2015). Prevalence and correlates of sipping alcohol in a prospective middle school sample. Psychology of Addictive Behaviors, 29(3), 766. 10.1037/adb0000072 [DOI] [PMC free article] [PubMed] [Google Scholar]
  32. Jackson KM, Roberts ME, Colby SM, Barnett NP, Abar CC, & Merrill JE (2014). Willingness to drink as a function of peer offers and peer norms in early adolescence. Journal of Studies on Alcohol and Drugs, 75(3), 404–414. 10.15288/jsad.2014.75.404 [DOI] [PMC free article] [PubMed] [Google Scholar]
  33. Jackson KM, Marceau K, Colby SM, Barnett NP, Rogers ML, & Hayes KL (2021). Trajectories of early alcohol use milestones: Interrelations among initiation and progression. Alcoholism: Clinical and Experimental Research, 45(11), 2294–2308. 10.1111/acer.14723 [DOI] [PMC free article] [PubMed] [Google Scholar]
  34. Janssen T, Cox MJ, Stoolmiller M, Barnett NP, & Jackson KM (2018). The role of sensation seeking and R-rated movie watching in early substance use initiation. Journal of Youth and Adolescence, 47(5), 991–1006. 10.1007/s10964-017-0742-0 [DOI] [PMC free article] [PubMed] [Google Scholar]
  35. Jessor R (2016). Problem behavior theory and adolescent risk behavior: A re-formulation. In The origins and development of problem behavior theory (pp. 117–130). Springer. [Google Scholar]
  36. Jessor R, & Jessor SL (1977). Problem behavior and psychosocial development: A longitudinal study of youth. Academic Pr. [Google Scholar]
  37. Johns MM, Lowry R, Rasberry CN, Dunville R, Robin L, Pampati S, … & Kollar LMM (2018). Violence victimization, substance use, and suicide risk among sexual minority high school students—United States, 2015-2017. Morbidity and Mortality Weekly Report, 67(43), 1211. 10.15585/mmwr.mm6743a4 [DOI] [PMC free article] [PubMed] [Google Scholar]
  38. Marshal MP, Friedman MS, Stall R, King KM, Miles J, Gold MA, Bukstein OG, & Morse JQ (2008). Sexual orientation and adolescent substance use: A meta-analysis and methodological review. Addiction, 103(4), 546–556. 10.1111/j.1360-0443.2008.02149.x [DOI] [PMC free article] [PubMed] [Google Scholar]
  39. Marshal MP, Friedman MS, Stall R, & Thompson AL (2009). Individual trajectories of substance use in lesbian, gay and bisexual youth and heterosexual youth. Addiction, 104(6), 974–981. 10.1111/j.1360-0443.2009.02531.x [DOI] [PMC free article] [PubMed] [Google Scholar]
  40. Messersmith EE, & Schulenberg JE (2010). Goal attainment, goal striving, and well-being during the transition to adulthood: A ten-year US national longitudinal study. New Directions for Child and Adolescent Development, 2010(130), 27–40. 10.1002/cd.279 [DOI] [PMC free article] [PubMed] [Google Scholar]
  41. Meyer IH (2003). Prejudice, social stress, and mental health in lesbian, gay, and bisexual populations: Conceptual issues and research evidence. Psychological Bulletin, 129(5), 674. 10.1037/0033-2909.129.5.674 [DOI] [PMC free article] [PubMed] [Google Scholar]
  42. Moksnes UK, Løhre A, Lillefjell M, Byrne DG, & Haugan G (2016). The association between school stress, life satisfaction and depressive symptoms in adolescents: Life satisfaction as a potential mediator. Social Indicators Research, 125(1), 339–357. 10.1007/s11205-014-0842-0 [DOI] [Google Scholar]
  43. Muthén B, & Muthén L (2012). Mplus User’s Guide (Seventh Edition). Muthén & Muthén. [Google Scholar]
  44. Nurmi J-E, Poole ME, & Kalakoski V (1994). Age differences in adolscent future-oriented goals, concerns, and related temporal extension in different sociocultural contexts. Journal of Youth and Adolescence, 23(4), 471–487. 10.1007/BF01538040 [DOI] [Google Scholar]
  45. Oyserman D, Destin M, & Novin S (2015). The context-sensitive future self: Possible selves motivate in context, not otherwise. Self and Identity, 14(2), 173–188. 10.1080/15298868.2014.965733 [DOI] [Google Scholar]
  46. Pavot W, & Diener E (2008). The satisfaction with life scale and the emerging construct of life sastisfaction. The Journal of Positive Psychology, 3(2), 137–152. 10.1080/17439760701756946 [DOI] [Google Scholar]
  47. Phillips II G, Turner B, Salamanca P, Birkett M, Hatzenbuehler ML, Newcomb ME, … & Mustanski B (2017). Victimization as a mediator of alcohol use disparities between sexual minority subgroups and sexual majority youth using the 2015 National Youth Risk Behavior Survey. Drug and alcohol dependence, 178, 355–362. 10.1016/j.drugalcdep.2017.05.040 [DOI] [PMC free article] [PubMed] [Google Scholar]
  48. Poon C, Saewyc E, & Chen W (2012). Enacted stigma, problem substance use, and protective factors among Asian sexual minority youth in British Columbia. Canadian Journal of Community Mental Health, 30(2), 47–64. 10.7870/cjcmh-2011-0016 [DOI] [Google Scholar]
  49. Povedano-Diaz A, Muñiz-Rivas M, & Vera-Perea M (2020). Adolescents’ life satisfaction: The role of classroom, family, self-concept and gender. International Journal of Environmental Research and Public Health, 17(1), 19. 10.3390/ijerph17010019 [DOI] [PMC free article] [PubMed] [Google Scholar]
  50. Preacher KJ, & Hayes AF (2008). Asymptotic and resampling strategies for assessing and comparing indirect effects in multiple mediator models. Behavior Research Methods, 40(3), 879–891. 10.3758/BRM.40.3.879 [DOI] [PubMed] [Google Scholar]
  51. Saewyc E, Clark T, Barney L, Brunanski D, & Homma Y (2014). Enacted stigma and HIV risk behaviours among sexual minority indigenous youth in Canada, New Zealand, and the United States. Pimatisiwin, 11(3), 411. 10.111/jpc.12397 [DOI] [PMC free article] [PubMed] [Google Scholar]
  52. Sastre MTM, & Ferrière G (2000). Family’Decline’and the Subjective Well-being of Adolescents. Social Indicators Research, 49(1), 69–82. 10.1023/A:1006935129243 [DOI] [Google Scholar]
  53. Schuler MS, & Collins RL (2019). Early alcohol and smoking initiation: A contributor to sexual minority disparities in adult use. American Journal of Preventive Medicine, 57(6), 808–817. 10.1016/j.amepre.2019.07.020 [DOI] [PMC free article] [PubMed] [Google Scholar]
  54. Schuler MS, Rice CE, Evans-Polce RJ, & Collins RL (2018). Disparities in substance use behaviors and disorders among adult sexual minorities by age, gender, and sexual identity. Drug and alcohol dependence, 189, 139–146. 10.1016/j.drugalcdep.2018.05.008 [DOI] [PMC free article] [PubMed] [Google Scholar]
  55. Shaw ST, Spink K, & Chin-Newman C (2019). “Do I really belong here?”: The stigma of being a community college transfer student at a four-year university. Community College Journal of Research and Practice, 43(9), 657–660. 10.1080/10668926.2018.1528907 [DOI] [Google Scholar]
  56. Swann G, Stephens J, Newcomb ME, & Whitton SW (2019). Effects of sexual/gender minority-and race-based enacted stigma on mental health and substance use in female assigned at birth sexual minority youth. Cultural Diversity and Ethnic Minority Psychology. 10.1037/cdp0000292 [DOI] [PMC free article] [PubMed] [Google Scholar]
  57. Tapert SF, Aarons GA, Sedlar GR, & Brown SA (2001). Adolescent substance use and sexual risk-taking behavior. Journal of Adolescent Health, 28(3), 181–189. 10.1016/S1054-139X(00)00169-5 [DOI] [PubMed] [Google Scholar]
  58. Toomey RB (2010). Gender-nonconforming lesbian, gay, bisexual, and transgender youth: School victimization and young adult psychosocial adjustment. Developmental Psychology, 46(6), 1580–1589. 10.1037/2329-0382.1.S.71 [DOI] [PubMed] [Google Scholar]
  59. van der Star A, Pachankis JE, & Bränström R (2021). Country-level structural stigma, school-based and adulthood victimization, and life satisfaction among sexual minority adults: A life course approach. Journal of Youth and Adolescence, 50(1), 189–201. 10.1007/s10964-020-01340-9 [DOI] [PMC free article] [PubMed] [Google Scholar]
  60. Varghese R, Norman TS, & Thavaraj S (2015). Perceived stress and self efficacy among college students: A global review. International Journal of Human Resource Management and Research, 5(3), 15–24. http://dx.doi.org/10.2139/ssrn.2703908 [Google Scholar]
  61. Wang M-T, Degol JL, & Henry DA (2019). An integrative development-in-sociocultural-context model for children’s engagement in learning. American Psychologist, 74(9), 1086–1102. 10.1037/amp0000522 [DOI] [PubMed] [Google Scholar]
  62. Williams DR, Yu Y, Jackson JS, & Anderson NB (1997). Racial differences in physical and mental health: Socio-economic status, stress and discrimination. Journal of Health Psychology, 2(3), 335–351. 10.1177/135910539700200305 [DOI] [PubMed] [Google Scholar]
  63. Yip CCH, & Chan KKS (2021). Stigma Resistance Among Sexual Minorities. Sexuality Research and Social Policy, 1–9. 10.1016/j.jad.2021.02.057 [DOI] [PMC free article] [PubMed] [Google Scholar]
  64. Zullig KJ, Valois RF, Huebner ES, Oeltmann JE, & Drane JW (2001). Relationship between perceived life satisfaction and adolescents’ substance abuse. Journal of Adolescent Health, 29(4), 279–288. 10.1016/S1054-139X(01)00269-5 [DOI] [PubMed] [Google Scholar]

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