A narrative review of the well-being and burnout of U.S. community pharmacists.

OBJECTIVES: The purpose of this narrative review is to summarize the literature on well-being and burnout among community pharmacists in the U.S. and provide recommendations for future research. METHODS: Relevant literature was identified by searching PubMed for combinations of keywords such as "burnout" and "well-being" combined with "pharmacists." Titles and abstracts were reviewed for relevancy, and full text articles were reviewed when applicable. RESULTS: While burnout is defined by its 3 core symptoms of emotional exhaustion, depersonalization, and low personal accomplishment, well-being is more challenging to define and measure, which has led to it being less studied. Community pharmacists faced high rates of burnout, low quality of life (QOL), and extreme fatigue prior to the COVID-19 pandemic, a situation that has likely only worsened. Factors such as workload, the type of community pharmacy, the level of education or training of the pharmacist, and stress may be some of the contributors to high rates of burnout. Clinician burnout may be related to high rates of mental health disorders seen in pharmacists, may impact patient safety and satisfaction, and may affect productivity and costs to employers and the healthcare system overall. There has been no research into interventions or strategies to support well-being and reduce burnout among community pharmacists, but having a workplace that is perceived as supporting well-being may have some impact. Recommendations for future research include the following: (1) define well-being, (2) explore why various factors support well-being or contribute to burnout, (3) determine the impact of community pharmacists experiencing well-being or burnout, and (4) develop strategies to support well-being and reduce burnout that are specific to community pharmacy. CONCLUSION: There is a sparsity of evidence regarding community pharmacist well-being and burnout. Further research is needed to generate the evidence needed to support interventions that are specific to the unique work setting of community pharmacists.

Flow with an intelligent tutor: A latent variable modeling approach to tracking flow during artificial tutoring.

Increasing use of intelligent tutoring systems in education calls for analytic methods that can unravel students' learning behaviors. In this study, we explore a latent variable modeling approach for tracking learning flow during computer-interactive artificial tutoring. The study considers three models that give discrete profiles of a latent process: the (i) latent class model, (ii) latent transition model, and (iii) hidden Markov model. We illustrate application of each model using example log data from Cognitive Tutor Algebra I and suggest analytic procedures of drawing learning flow. Through experimental application, we show that the models can reveal substantive information about students' learning behaviors and have potential utility for describing the learning flow. The models differed in the assumptions and data constraints but yielded consistent findings on the flow states and interaction modalities. Based on our experiential analyses, we discuss strengths and limitations of the models and illuminate areas of future development.

The Comparison of Latent Variable Propensity Score Models to Traditional Propensity Score Models under Conditions of Covariate Unreliability.

Propensity score (PS) methods are implemented by researchers to balance the differences between participants in control and treatment groups that exist in observational studies using a set of baseline covariates. Propensity scores are most commonly calculated using baseline covariates in a logistic regression model to predict the binary grouping variable (control versus treatment). Low reliability associated with the covariates can adversely impact the calculation of treatment effects in propensity score models. The incorporation of latent variables when calculating propensity scores has been suggested to offset the negative impact of covariate unreliability. Simulation studies were conducted to compare the performance of latent variable methods with traditional propensity score methods when estimating the treatment effect under conditions of covariate unreliability. The results indicated that using factor scores or composite variables to compute propensity scores resulted in biased estimates and inflated Type I error rates as compared to using latent factors to compute propensity scores in certain conditions. This was largely dependent upon the number of infallible covariates also included in the PS model and the outcome analysis model analyzed. Implications of the findings are discussed.

The Performance of Multivariate Methods for Two-Group Comparisons with Small Samples and Incomplete Data.

In intervention studies having multiple outcomes, researchers often use a series of univariate tests (e.g., ANOVAs) to assess group mean differences. Previous research found that this approach properly controls Type I error and generally provides greater power compared to MANOVA, especially under realistic effect size and correlation combinations. However, when group differences are assessed for a specific outcome, these procedures are strictly univariate and do not consider the outcome correlations, which may be problematic with missing outcome data. Linear mixed or multivariate multilevel models (MVMMs), implemented with maximum likelihood estimation, present an alternative analysis option where outcome correlations are taken into account when specific group mean differences are estimated. In this study, we use simulation methods to compare the performance of separate independent samples t tests estimated with ordinary least squares and analogous t tests from MVMMs to assess two-group mean differences with multiple outcomes under small sample and missingness conditions. Study results indicated that a MVMM implemented with restricted maximum likelihood estimation combined with the Kenward-Roger correction had the best performance. Therefore, for intervention studies with small N and normally distributed multivariate outcomes, the Kenward-Roger procedure is recommended over traditional methods and conventional MVMM analyses, particularly with incomplete data.

Preliminary Findings of the Links Between Gender Role Attitudes and Sexual Behaviors in Mexican American Early Adolescent Boys.

This study explored the relations between Latino gender role attitudes (traditional machismo attitudes and caballerismo attitudes) and sexual behaviors among 242 Mexican American early adolescent boys in the southwest United States. Specifically, a multiple mediator model estimated the association between gender role attitudes and sexual activity through a mediational pathway connecting substance use, sexual motives, and peer influence. Results from analyzing this structural equation model indicated that traditional machismo attitudes were not associated with sexual behaviors. Caballerismo gender role attitudes, however, were indirectly linked with reduced sexual motives and substance via peer influence. This study underscores the importance of social context, behavior, and psychological motivations in explaining differences in sexual motives and substance use among young Mexican American adolescent boys who hold different kinds of beliefs about their gender.

Familial ethnic socialization, gender role attitudes, and ethnic identity development in Mexican-origin early adolescents.

OBJECTIVES: This study examined the relations between familial ethnic socialization and ethnic identity development in 438 Mexican-origin (n = 242 boys and n = 196 girls) preadolescents. In addition, machismo and marianismo gender role attitudes were examined as potential mediators in this link. METHOD: Confirmatory factor analyses (CFA) of the Familial Ethnic Socialization Scale (FES), Machismo Measure (MM), Marianismo Beliefs Scale (MBS), and the Ethnic Identity Brief Scale (EISB) were conducted to test the factor structure with a preadolescent Mexican-origin sample. Separate path analyses of analytic models were then performed on boys and girls. RESULTS: Results of the CFAs for survey measures revealed that for the FES, a 1-factor version indicated acceptable fit; for the MM, the original 2-factor structure indicated acceptable model fit; for the MBS, a revised 3-factor version indicated acceptable model fit; and, for the EISB, the affirmation and resolution dimensions showed acceptable fit. Among boys, FES was significantly and positively linked to caballerismo, and EISB affirmation and resolution; furthermore, the links between FES and EISB affirmation and resolution were indirectly connected by caballerismo. In addition, traditional machismo was negatively linked to EISB affirmation, and caballerismo was positively linked to EISB affirmation and resolution. Among girls, FES was significantly and positively related to the MBS-virtuous/chaste pillar, and EISB affirmation and resolution. The MBS-subordinate to others pillar was negatively linked to EISB affirmation. CONCLUSIONS: This study underscores the importance of FES and positive gender role attitudes in the link to ethnic identity development among Mexican-origin preadolescents. (PsycINFO Database Record

Examining the Factor Structure of the Self-Compassion Scale in Four Distinct Populations: Is the Use of a Total Scale Score Justified?

This study examined the factor structure of the Self-Compassion Scale (SCS) using a bifactor model, a higher order model, a 6-factor correlated model, a 2-factor correlated model, and a 1-factor model in 4 distinct populations: college undergraduates (N = 222), community adults (N = 1,394), individuals practicing Buddhist meditation (N = 215), and a clinical sample of individuals with a history of recurrent depression (N = 390). The 6-factor correlated model demonstrated the best fit across samples, whereas the 1- and 2-factor models had poor fit. The higher order model also showed relatively poor fit across samples, suggesting it is not representative of the relationship between subscale factors and a general self-compassion factor. The bifactor model, however, had acceptable fit in the student, community, and meditator samples. Although fit was suboptimal in the clinical sample, results suggested an overall self-compassion factor could still be interpreted with some confidence. Moreover, estimates suggested a general self-compassion factor accounted for at least 90% of the reliable variance in SCS scores across samples, and item factor loadings and intercepts were equivalent across samples. Results suggest that a total SCS score can be used as an overall mesure of self-compassion.

Perceived discrimination and sexual precursor behaviors in Mexican American preadolescent girls: The role of psychological distress, sexual attitudes, and marianismo beliefs.

OBJECTIVES: This study explored the relation between perceived discrimination and sexual precursor behaviors among 205 Mexican American preadolescent middle school girls. In addition, this study examined whether psychological distress and sexual attitudes mediated and whether marianismo beliefs moderated this relation. METHOD: A categorical confirmatory factor analysis (CCFA) of the Marianismo Beliefs Scale (MBS) was conducted to test the factor structure with a preadolescent Mexican American population (ages 11-14). A path analysis of analytic models was then performed to examine the hypothesized relations between perceived discrimination, psychological distress, sexual attitudes, marianismo beliefs, and sexual precursor behaviors. RESULTS: Results of the CCFA did not support the original 5-factor structure of the MBS for preadolescent Latina girls. However, a revised version of the MBS indicated an acceptable model fit, and findings from the path analysis indicated that perceived discrimination was both directly and indirectly linked to sexual precursor behaviors via psychological distress. Marianismo was not found to moderate the relation between perceived discrimination and sexual risk behaviors, however certain marianismo pillars were significantly negatively linked with sexual attitudes and precursor behaviors. CONCLUSIONS: This study underscores the importance of psychological distress in the perceived discrimination and sexual precursor link as well as the compensatory aspects of marianismo against sexual precursor behaviors in Mexican American preadolescent girls. (PsycINFO Database Record

Perceived Discrimination, Peer Influence and Sexual Behaviors in Mexican American Preadolescents.

Both discrimination and sexual health disparities have significant negative health implications for Latina/o preadolescent youth, including negative mental health outcomes, STIs/HIV, unintended pregnancy, and ongoing poverty. Studying these links within this population, therefore, has significant public health relevance, both in terms of promoting sexual health in general as well as serving the specific needs of Latina/o youth. This study explored the relationship between perceived discrimination, peer influence and sexual behaviors among 438 Mexican American preadolescents in the Southwest United States (55.3 % male). Additionally, this study examined whether psychological distress, substance use, and sexual motives mediated and whether gender moderated these relations. A multiple-group path analysis of the analytical model was performed to examine the hypothesized relations between perceived discrimination, peer influence, psychological distress, substance use, sexual motives and sexual behaviors. The findings indicated that perceived discrimination was directly linked to sexual behaviors among participants and indirectly linked via substance use. The findings also indicated that peer influence was indirectly linked to sexual behaviors via substance use among participants and via sexual motives among boys. This study underscores the importance of substance use in the perceived discrimination, peer influence and sexual behavior link in Mexican American preadolescents. Additionally, it highlights the importance of sexual motives in the link between peer influence and sexual behaviors of Mexican American boys.

The Role of Student Beliefs in Dual-Enrollment Courses.

Access to dual-enrollment courses, which allow high school students to earn college credit, is stratified by race/ethnicity, class, and geography. States and colleges have begun using multiple measures of readiness, including non-cognitive measures of student preparedness, in lieu of strict reliance on test scores in an attempt to expand and equalize access. This practice was accelerated by COVID-19 due to disruptions in standardized testing. However, limited research has examined how non-cognitive beliefs shape students' experiences and outcomes in dual-enrollment courses. We study a large dual-enrollment program created by a university in the Southwest to examine these patterns. We find that mathematics self-efficacy and educational expectations predict performance in dual-enrollment courses, even when controlling for students' academic preparedness, while factors such as high school belonging, college belonging, and self-efficacy in other academic domains are unrelated to academic performance. However, we find that students of color and first-generation students have lower self-efficacy and educational expectations before enrolling in dual-enrollment courses, in addition to having lower levels of academic preparation. These findings suggest that using non-cognitive measures to determine student eligibility for dual-enrollment courses could exacerbate, rather than ameliorate, inequitable patterns of participation. Students from historically marginalized populations may benefit from social-psychological as well as academic supports in order to receive maximum benefits from early postsecondary opportunities such as dual-enrollment. Our findings have implications for how states and dual-enrollment programs determine eligibility for dual-enrollment as well as how dual-enrollment programs should be designed and delivered in order to promote equity in college preparedness. Supplementary Information: The online version contains supplementary material available at 10.1007/s11162-023-09740-z.

Distinct racial and ethnic metabolic syndrome characteristics: A comparative assessment in low-income children 7-10 years of age.

BACKGROUND: Pediatric MetS prevalence varies due to lack of consensus on evaluative criteria and associated thresholds, with most not recommending a diagnosis <10 years. However, MetS risk components are becoming evident earlier in life and affect races and ethnicities disproportionately. OBJECTIVES: To compare the prevalence of MetS based on existing definitions and elucidate racial- and ethnic-specific characteristics associated with MetS prevalence. METHODS: The baseline and follow-up samples included 900 and 557 children 7-10 years, respectively. Waist circumference, BMI percentile, blood pressure, fasting plasma glucose (FPG), insulin, triglycerides, and high-density lipoprotein cholesterol (HDL-C) were measured. Agreement between MetS definitions was quantified via kappa statistics. MetS and risk factor prevalence and the predictability of metabolic parameters on MetS eight months later was evaluated via logistic regression. McFadden pseudo-R2 was reported as a measure of predictive ability, and the Akaike information criterion evaluated fit of each model. RESULTS: The baseline sample was 55.0% male and 71.6% Hispanic, followed by non-Hispanic White (NHW) (17.3%) and non-Hispanic Black (NHB) (11.1%), with an average age of 9.2 years. MetS prevalence ranged from 7.6% to 21.4%, highest in Hispanic (9.0%-24.0%) and lowest in NHB children (4.0%-14.0%). Highest agreement was between Ford et al. and Cook et al. definitions (K = 0.88) and lowest agreements were consistently with the International Diabetes Federation criteria (K ≤ 0.57). Compared to NHW children, Hispanic children had higher odds for MetS (OR: 1.7; p = 0.03) and waist circumference, HDL-C, and FPG risk factors (p < 0.05), while NHB children had higher odds for the FPG risk factor (p ≤ 0.007) and lower odds for the plasma triglycerides risk factor (p = 0.002), across multiple MetS definitions. In longitudinal analyses, HDL-C was the strongest independent predictor of MetS in Hispanic and NHW children (p < 0.001 and p < 0.01, respectively), while plasma triglycerides was the strongest independent predictor of MetS in NHB children (p < 0.05). CONCLUSIONS: MetS prevalence was high in children ≤10 years, and proposed criteria are susceptible to racial and ethnic bias, diagnosing some populations more than other populations with high cardiovascular risk. Earlier preventative measures should be imposed in clinical settings, accounting for racial and ethnic differences, to mitigate disease onset.

A Monte Carlo study of the impact of missing data and differential item functioning on theta estimates from two polytomous Rasch family models.

This paper examines the impact of differential item functioning (DIF), missing item values, and different methods for handling missing item values on theta estimates with data simulated from the partial credit model and Andrich's rating scale model. Both Rasch family models are commonly used when obtaining an estimate of a respondent's attitude. The degree of missing data, DIF magnitude, and the percentage of DIF items were varied in MCAR data conditions in which the focal group was 10% of the total population. Four methods for handling missing data were compared: complete-case analysis, mean substitution, hot-decking, and multiple imputation. Bias, RMSE, means, and standard errors of the theta estimates for the focal group were adversely affected by the amount and magnitude of DIF items. RMSE and fidelity coefficients for both the reference and focal group were adversely impacted by the amount of missing data. While all methods of handling missing data performed fairly similarly, multiple imputation and hot-decking showed slightly better performance.

Sexual health risks and protective resources in gay, lesbian, bisexual, and heterosexual homeless youth.

ISSUES AND PURPOSE: To compare sexual health risks and protective resources of homeless adolescents self-identified as gay (G), lesbian (L), or bisexual (B), with those self-identified as heterosexual, and to determine the differences between these two groups and the differences within the GLB group. DESIGN AND METHODS: A secondary analysis of survey data collected from a nonprobability sample of 425 homeless adolescents between 16 and 20 years of age. RESULTS: Sexual health risks and protective resources differed between those self-identified as GLB and those self-identified as heterosexual. More G/L youth reported a history of sexual abuse and being tested and treated for HIV, and more scored lower on the assertive communication measure than did bisexual or heterosexual youth. Moreover, there were gender differences within the GLB group; more males than females self-identified as homosexual and more females than males self-identified as bisexual. PRACTICE IMPLICATIONS: Sexual health interventions for this population should be both gender- and sexual orientation-specific.

Dyadic Curve-of-Factors Model: An Introduction and Illustration of a Model for Longitudinal Non-Exchangeable Dyadic Data.

The analysis of longitudinal data collected from non-exchangeable dyads presents a challenge for applied researchers for various reasons. This paper introduces the Dyadic Curve-of-Factors Model (D-COFM) which extends the Curve-of-Factors Model (COFM) proposed by McArdle (1988) for use with non-exchangeable dyadic data. The D-COFM overcomes problems with modeling composite scores across time and instead permits examination of the growth in latent constructs over time. The D-COFM also appropriately models the interdependency among non-exchangeable dyads. Different parameterizations of the D-COFM are illustrated and discussed using a real dataset to aid applied researchers when analyzing dyadic longitudinal data.

Latent growth modeling with domain-specific outcomes comprised of mixed response types in intervention studies.

OBJECTIVE: When several continuous outcome measures of interest are collected across time in experimental studies, the use of standard statistical procedures, such as multivariate analysis of variance or growth curve modeling, can be properly used to assess treatment effects. However, when data consist of mixed responses (e.g., continuous and ordered categorical [ordinal] responses), traditional modeling approaches are no longer appropriate. The purpose of this article is to illustrate the use of a more suitable modeling procedure when mixed responses are collected in longitudinal intervention studies. METHOD: Problems with traditional analyses of such data are discussed, as are potential advantages provided by the proposed modeling approach. The application of the multiple-domain latent growth modeling approach with mixed responses is illustrated for experimental designs with data from the SeniorWISE study (McDougall et al., 2010). This multisite randomized trial assessed memory functioning of 265 elderly adults across a 26-month period after receiving either a memory or health promotion training program. RESULTS: The latent growth models illustrated allow one to examine treatment effects on the growth of multiple mixed outcomes while incorporating associations among multiple responses, which allows for better missing data treatment, greater power, and more accurate control of Type I error. The interpretation of parameters of interest and treatment effects is discussed using the SeniorWISE data. CONCLUSIONS: Multiple-domain latent growth modeling with mixed responses is a flexible statistical modeling tool that can have substantial benefits for applied researchers. As such, the use of this modeling approach is expected to increase.

Interaction of duration of homelessness and gender on adolescent sexual health indicators.

PURPOSE: The purpose of this analysis was to determine the effects of duration of homelessness and gender on personal and social resources, cognitive-perceptual factors, and sexual health behaviors among homeless youth. DESIGN: Cross-sectional analysis of data collected at baseline from 461 homeless adolescents who participated in a sexual health intervention study was done. METHOD: Data were collected via laptop computers from homeless adolescents (mean age=19.52+1.91 years) in both comparison and intervention groups before the initiation of the intervention. FINDINGS: Significant interaction effects were found for personal and social resources F (4, 426)=2.83, p<.05. Male participants who had been homeless<6 months had significantly higher scores on social connectedness than did male participants who were homeless>1 year. Univariate analysis of variance (ANOVA) indicated that both boys and girls who had been homeless>1 year had greater AIDS knowledge, F (1, 441)=7.91, p<.01, reported significantly more sexual risk-taking behaviors, F (1, 396)=9.93, p<.05, and engaged in fewer safe-sex behaviors, F (1, 396)=12.05, p<.05, than did those who had been homeless<6 months. Univariate ANOVA indicated that female participants had significantly lower levels of perceived health status, F (1, 429)=12.08, p<.01, significantly greater sexual self-care behaviors, F (1, 396)=16.29, p<.01, and significantly higher levels of assertive communication F (1, 396)=4.03, p<.05 than did male participants, regardless of duration of homelessness. CONCLUSIONS: The duration of homelessness and gender has both direct and interaction effects on cognitive-perceptual and behavioral outcomes associated with sexual health. CLINICAL RELEVANCE: Nurses and other healthcare providers working with homeless youth recognize the need to develop brief interventions that address health-risk behaviors. Findings from this study indicate that gender-specific interventions should be provided to youth soon after they become homeless.

The Performance of Cross-Validation Indices Used to Select Among Competing Covariance Structure Models Under Multivariate Nonnormality Conditions.

Cudeck and Browne (1983) proposed using cross-validation as a model selection technique in structural equation modeling. The purpose of this study is to examine the performance of eight cross-validation indices under conditions not yet examined in the relevant literature, such as nonnormality and cross-validation design. The performance of each cross-validation index was measured in terms of true model selection rate as well as consistency of model selection. The performance of the cross-validation indices tended to improve as factor loading and sample size increased but performed less well as nonnormality increased. The double cross-validated indices outperformed their simple cross-validated counterparts in certain conditions. Recommendations are provided as to which cross-validation methods would optimally perform in a given condition.

A Comparison of Methods to Test for Mediation in Multisite Experiments.

A Monte Carlo study extended the research of MacKinnon, Lockwood, Hoffman, West, and Sheets (2002) for single-level designs by examining the statistical performance of four methods to test for mediation in a multilevel experimental design. The design studied was a two-group experiment that was replicated across several sites, included a single intervening variable and outcome, and assumed that the effects of the treatment and mediator were constant across sites. The findings provide new evidence of the benefits of and further support for using the asymmetric confidence limits approach to test for mediation. In addition, the authors provide further support for using confidence intervals to assess if treatment effects are completely mediated, as using traditional hypothesis testing may lead to erroneous conclusions.