Posttreatment motivation and alcohol treatment outcome 9 months later: findings from structural equation modeling.
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ABSTRACT:
SUBMITTER: Cook S
PROVIDER: S-EPMC4352962 | biostudies-other | 2015 Feb
REPOSITORIES: biostudies-other
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Project description:PurposeTo establish a model of Japanese pharmacy students' learning motivation profile and investigate the effects of pharmaceutical practical training programs on their learning motivation.MethodsThe Science Motivation Questionnaire II was administered to pharmacy students in their 4th (before practical training), 5th (before practical training at clinical sites), and 6th (after all practical training) years of study at Josai International University in April, 2016. Factor analysis and multiple-group structural equation modeling were conducted for data analysis.ResultsA total of 165 students participated. The learning motivation profile was modeled with 4 factors (intrinsic, career, self-determination, and grade motivation), and the most effective learning motivation was grade motivation. In the multiple-group analysis, the fit of the model with the data was acceptable, and the estimated mean value of the factor of 'self-determination' in the learning motivation profile increased after the practical training programs (P= 0.048, Cohen's d= 0.43).ConclusionPractical training programs in a 6-year course were effective for increasing learning motivation, based on 'self-determination' among Japanese pharmacy students. The results suggest that practical training programs are meaningful not only for providing clinical experience but also for raising learning motivation.
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Project description:Flavored cigar restrictions have the potential to benefit public health. Flavor availability facilitates cigarillo use, but it is unknown if flavor impacts patterns of co-use of cigarillos and cannabis, an increasingly prevalent behavior among young adults. Data were collected (2020-2021) in a cross-sectional online survey administered to a convenience sample of young adults who smoked cigarillos from 15 areas with high cigar use prevalence. We assessed the relationship between flavored cigarillo use and motivation to quit cannabis and cigarillo use among past 30-day co-users (N = 218), as well as several covariates (e.g., cigarillo price and flavor/cannabis policy). Flavored cigarillo perceived appeal and harm were hypothesized parallel mediators. Most co-users reported usually using flavored cigarillos (79.5%), which was not significantly associated with motivation to quit cigarillos or cannabis. Perceived cigarillo harm (β = 0.17, 95% CI = 0.00, 0.33), advertising exposure (β = 0.12, 95% CI = 0.00, 0.24), and income (among racial/ethnic minorities; β = -0.13, 95% CI = -0.25, -0.02) were significant predictors of motivation to quit cigarillos. There were no significant predictors of motivation to quit cannabis. Cigarillo flavor was not associated with motivation to quit, so findings could suggest that banning flavors in cigars may have a neutral impact on co-use with cannabis among young adults.
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Project description:Body dysmorphic disorder (BDD), together with its subtype muscle dysmorphia (MD), has been relocated from the Somatoform Disorders category in the DSM-IV to the newly created Obsessive-Compulsive and Related Disorders category in the DSM-5. Both categorizations have been criticized, and an empirically derived classification of BDD is lacking. A community sample of N = 736 participants completed an online survey assessing different psychopathologies. Using a structural equation modeling approach, six theoretically derived models, which differed in their allocation of BDD symptoms to various factors (i.e. general psychopathology, somatoform, obsessive-compulsive and related disorders, affective, body image, and BDD model) were tested in the full sample and in a restricted sample (n = 465) which indicated primary concerns other than shape and weight. Furthermore, measurement invariance across gender was examined. Of the six models, only the body image model showed a good fit (CFI = 0.972, RMSEA = 0.049, SRMR = 0.027, TLI = 0.959), and yielded better AIC and BIC indices than the competing models. Analyses in the restricted sample replicated these findings. Analyses of measurement invariance of the body image model showed partial metric invariance across gender. The findings suggest that a body image model provides the best fit for the classification of BDD and MD. This is in line with previous studies showing strong similarities between eating disorders and BDD, including MD. Measurement invariance across gender indicates a comparable presentation and comorbid structure of BDD in males and females, which also corresponds to the equal prevalence rates of BDD across gender.
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Project description:Gene expression is controlled by multiple regulators and their interactions. Data from genome-wide gene expression assays can be used to estimate molecular activities of regulators within a model organism and extrapolate them to biological processes in humans. This approach is valuable in studies to better understand complex human biological systems which may be involved in diseases and hence, have potential clinical relevance. In order to achieve this, it is necessary to infer gene interactions that are not directly observed (i.e. latent or hidden) by way of structural equation modeling (SEM) on the expression levels or activities of the downstream targets of regulator genes. Here we developed an R Shiny application, termed "Structural Equation Modeling of In silico Perturbations (SEMIPs)" to compute a two-sided t-statistic (T-score) from analysis of gene expression data, as a surrogate to gene activity in a given human specimen. SEMIPs can be used in either correlational studies between outcome variables of interest or subsequent model fitting on multiple variables. This application implements a 3-node SEM model that consists of two upstream regulators as input variables and one downstream reporter as an outcome variable to examine the significance of interactions among these variables. SEMIPs enables scientists to investigate gene interactions among three variables through computational and mathematical modeling (i.e. in silico). In a case study using SEMIPs, we have shown that putative direct downstream genes of the GATA Binding Protein 2 (GATA2) transcription factor are sufficient to infer its activities in silico for the conserved progesterone receptor (PGR)-GATA2-SRY-box transcription factor 17 (SOX17) genetic network in the human uterine endometrium.
| S-EPMC8652139 | biostudies-literature
Project description:The competition in higher education has increased, while lecturers are involved in multiple assignments that include teaching, research and publication, consultancy, and community services. The demanding nature of academia leads to excessive work load and stress among academicians in higher education. Notably, offering the right motivational mix could lead to job satisfaction and performance. The current study aims to demonstrate the effects of extrinsic and intrinsic motivational factors influencing job satisfaction and job performance among academicians working in Malaysian private higher educational institutions (PHEIs). Cross-sectional data were collected from the Malaysian PHEIs and the randomly selected 343 samples. The data analysis was performed with the dual analysis of partial least square structural equation modeling (PLS-SEM) and artificial neural network (ANN) analysis. As a result, it was found that financial rewards, promotion, performance appraisal, classroom environment, and code of conduct significantly predicted job satisfaction. The code of conduct, autonomy, and self-efficacy strongly influenced job performance. The relationship between job satisfaction and job performance was highly moderated by self-efficacy. It was suggested from the ANN analysis that the three prominent factors influencing job satisfaction are financial rewards, performance appraisal, and code of conduct. The analysis supported three significant factors influencing job performance: self-efficacy, performance appraisal, and code of conduct. The management of PHEIs should build the correct policies to transform job satisfaction into job performance. Self-efficacy plays an essential role in activating job performance. Other significant motivating factors that promote job satisfaction and performance, such as emotional intelligence, mindfulness, and other personal traits, should be included in future studies. In addition, future research could use a mixed-method or multi-respondent approach to investigate the important variables and their impact on lecturers' job satisfaction and performance.
| S-EPMC9251502 | biostudies-literature
Project description:Pharmacy students in Japan have to maintain strong motivation to learn for six years during their education. The authors explored the students’ learning structure. All pharmacy students in their 4th through to 6th year at Josai International University participated in the survey. The revised two factor study process questionnaire and science motivation questionnaire II were used to assess their learning process and learning motivation profiles, respectively. Structural equation modeling (SEM) was used to examine a causal relationship between the latent variables in the learning process and those in the learning motivation profile. The learning structure was modeled on the idea that the learning process affects the learning motivation profile of respondents. In the multi-group SEM, the estimated mean of the deep learning to learning motivation profile increased just after their clinical clerkship for 6th year students. This indicated that the clinical experience benefited students’ deep learning, which is probably because the experience of meeting with real patients encourages meaningful learning in pharmacy studies.
| S-EPMC6024982 | biostudies-literature
Project description:At least 282 Food Policy Councils (FPCs) are currently working to improve access to healthy foods in their communities by connecting food system sectors, gathering community input, and advising food policy. Empirical research on FPCs is limited. This study empirically evaluates FPCs to better understand the relationships between Organizational Capacity, Social Capital, and Council Effectiveness by testing a FPC Framework adapted from Allen and colleagues (2012). Members of all FPCs in the U.S., Canada, and Native American Tribes and First Nations were invited to complete the Food Policy Council Self-Assessment Tool (FPC-SAT). Structural equation modeling was used to test the FPC Framework. Three hundred and fifty-four FPC members from 95 councils completed the FPC-SAT. After slight modification, a revised FPC Framework was a good fit with the data (?2 = 40.085, df = 24, p-value = .021, comparative fit index = 0.988, Tucker Lewis index = 0.982, root mean squared error of approximation = 0.044, p-close = .650). A moderation analysis revealed that community context influences the relationship between Social Capital and Council Effectiveness within the FPC Framework. The FPC Framework can guide capacity building interventions and FPC evaluations. The empirically tested framework can help FPCs efficiently work toward achieving their missions and improving their local food system.
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Project description:ObjectivesWhen examining the relationship between smoking intensity and toxicant exposure biomarkers in an effort to understand the potential risk for smoking-related disease, individual biomarkers may not be strongly associated with smoking intensity because of the inherent variability in biomarkers. Structural equation modeling (SEM) offers a powerful solution by modeling the relationship between smoking intensity and multiple biomarkers through a latent variable.MethodsBaseline data from a randomized trial (N = 1250) were used to estimate the relationship between smoking intensity and a latent toxicant exposure variable summarizing five volatile organic compound biomarkers. Two variables of smoking intensity were analyzed: the self-report cigarettes smoked per day and total nicotine equivalents in urine. SEM was compared with linear regression with each biomarker analyzed individually or with the sum score of the five biomarkers.ResultsSEM models showed strong relationships between smoking intensity and the latent toxicant exposure variable, and the relationship was stronger than its counterparts in linear regression with each biomarker analyzed separately or with the sum score.ConclusionsSEM is a powerful multivariate statistical method for studying multiple biomarkers assessing the same class of harmful constituents. This method could be used to evaluate exposure from different combusted tobacco products.
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Project description:Alcohol use in the U.S. continues to be a prevalent behavior with the potential for far-reaching personal and public health consequences. Risk factors for problematic drinking include negative affect and impulsive decision-making. Research suggests exposure to nature reduces negative affect, increases positive affect, and reduces impulsive choice. The purpose of the current study was to explore the relationships between exposure to nature (actively going out to nature and the level of greenness around the participant's daily life), affect, impulsive decision-making, and alcohol use, using structural equation modeling. Cross-sectional data (N = 340) collected online on Amazon MTurk were used to test the hypothesized relationships separately for alcohol consumption and alcohol-related problems. Actively spending time in nature was associated with lower negative affect and higher positive affect, while passive exposure to nature was only associated with higher positive affect. In turn, negative affect was positively related to both alcohol measures, while positive affect was related to increased alcohol consumption, but not alcohol-related problems. Impulsive decision-making was not related to nature or alcohol measures. Findings suggest that intentionally spending time in nature may protect against problematic alcohol use by reducing negative affect. These results warrant further research on nature as an adjunct treatment for reducing alcohol and substance-related harms and carry implications for public education and increasing accessibility to natural spaces.
| S-EPMC9603149 | biostudies-literature
Project description:PurposeAdolescents who report having mental health problems, including depression and anxiety, are at greater risk of alcohol use. We examined the longitudinal association between mental health and alcohol use six months later in a diverse adolescent sample attending a primary care appointment. The primary care setting provides a unique opportunity to reach this younger age group and address risk factors, including mental health problems and substance use.MethodsAdolescents aged 12-18 (n = 668) recruited from waiting rooms at four primary care clinics in Los Angeles, California and Pittsburgh, Pennsylvania completed a baseline web-based survey (April 2013 to November 2015) and another survey six months later. Bivariate analysis and multi-variable regression assessed associations between baseline mental health and 6-month alcohol use outcomes (any use, heavy use, and maximum quantity).ResultsAdolescents were stratified by mental health scores using the Mental Health Inventory-5 (MHI-5) cut off at baseline. In unadjusted analyses of alcohol outcomes at six months, adolescents with more mental health problems reported higher alcohol use across all three measures (p < .01 for heavy use; p < .05 for any use and maximum quantity used). Adolescents' reports of better mental health at baseline were associated with fewer heavy drinking episodes (p < .05) and lower maximum number of drinks (p < .05) at six months, after adjusting for baseline alcohol use, intervention group, site, and demographic characteristics.ConclusionsAddressing mental health in primary care may be important for decreasing alcohol problems in adolescents. Strategies for facilitating screening and intervention with adolescents at-risk of alcohol use are warranted.
| S-EPMC6611548 | biostudies-literature