Psychometric Evaluation of an Adapted Short-Form Spirituality Scale in a Sample of Predominantly White Adults in an Inpatient Substance Use Disorder Treatment Program.

Spirituality is an important aspect of treatment and recovery for substance use disorders (SUDs), but ambiguities in measurement can make it difficult to incorporate as part of routine care. We evaluated the psychometric properties of an adapted short-form version of the Spirituality Scale (the Spirituality Scale-Short-Form; SS-SF) for use in SUD treatment settings. Participants were adult patients (N = 1,388; Mage = 41.23 years, SDage = 11.55; 68% male; 86% White) who entered a large, clinically mixed inpatient SUD treatment program. Factor analysis supported the two-dimensional structure, with factors representing Self-Discovery and Transcendent Connection. Tests of measurement invariance demonstrated that the scale was invariant across age and gender subgroups. The SS-SF exhibited convergent and concurrent validity via associations with participation in spiritual activities, hopefulness, life satisfaction, 12-step participation, and depressive symptoms. Finally, scores on the SS-SF were significantly higher at discharge compared to admission, demonstrating short-term sensitivity to change. These findings support use of the SS-SF as a concise, psychometrically sound measure of spirituality in the context of substance use treatment.

Separating Behaviours and Adverse Consequences in the Problem Gambling Severity Index (PGSI): A Confirmatory Factor Analysis and Rasch Analysis.

The Problem Gambling Severity Index (PGSI) is considered the "gold standard" for measuring problem gambling. The PGSI provides a single score summed across nine items. The nine items of the PGSI comprise two subdomains: problematic behaviours and adverse consequences. The aim of the present study was to compare evidence of a one-factor structure to evidence of a two-factor structure representing the two subdomains. With a sample of 1,251 bettors, we conducted confirmatory factor analyses and Rasch analyses to assess evidence supporting the one-factor and the two-factor structures. In addition, stochastic search variable selection was conducted with the total PGSI score, PGSI behaviour score, and PGSI adverse consequences score as separate outcomes to examine whether information is lost when collapsing the two subdomains into a single factor. Overall, there was stronger support of a two-factor structure than a one-factor structure. However, the two-factors were highly correlated with one another and shared most predictors except for one. We recommend continued use of the one-factor structure of the PGSI unless one aims to better understand the relationship between problematic behaviours and adverse consequences.

Characterizing Clinical Heterogeneity in a Large Inpatient Addiction Treatment Sample: Confirmatory Latent Profile Analysis and Differential Levels of Craving and Impulsivity.

Background: Individuals with substance use disorders (SUDs) have highly heterogeneous presentations and identifying more homogeneous subgroups may foster more personalized treatment. This study used SUD and other psychiatric indicators to characterize latent subgroups of patients in a large inpatient addiction treatment program. The resulting subgroups were then analyzed with respect to differences on clinically informative motivational mechanisms. Methods: Patients (n = 803) were assessed for severity of SUD (ie, alcohol use disorder, drug use disorder), post-traumatic stress disorder, anxiety disorders, and major depressive disorder. Confirmatory latent profile analysis (CLPA) was used to identify latent subgroups, hypothesizing 4 subgroups. Subgroups were then characterized with respect to multiple indicators of impulsivity (ie, delay discounting and impulsive personality traits via the UPPS-P) and craving. Results: The CLPA confirmed the hypothesized 4-profile solution according to all indicators (eg, entropy = 0.90, all posterior probabilities ⩾.92). Profile 1 (n = 229 [32.2%], 24.9% female, median age in range of 45-49) reflected individuals with high alcohol severity and low psychiatric severity (HAlc/LPsy). Profile 2 (n = 193 [27.1%], 29.3% female, median age in range of 35-39) reflected individuals with high drug and psychiatric severity (HDrug/HPsy). Profile 3 (n = 160 [22.5%], 37.6% female, median age in range of 45-49) reflected individuals with high alcohol severity and psychiatric severity (HAlc/HPsy). Profile 4 (n = 130 [18.3%], 19.4% female, median age in range of 35-39) reflected individuals with high drug severity and low psychiatric severity (HDrug/LPsy). Both high comorbid psychiatric severity subgroups exhibited significantly higher craving and facets of impulsivity. Conclusions: The results provide further evidence of 4 latent subgroups among inpatients receiving addiction treatment, varying by alcohol versus other drugs and low versus high psychiatric comorbidity. Furthermore, they reveal the highest craving and impulsivity in the high psychiatric comorbidity groups, suggesting targets for more intensive clinical intervention in these patients.

Predictors of Problem Gambling for Sports and Non-sports Gamblers: A Stochastic Search Variable Selection Analysis.

Differences in the psychological characteristics and gambling behaviors of sports bettors and non-sports bettors were examined with a view to identifying predictors of problem gambling severity. A survey was completed by 1,280 participants, 596 of whom had placed bets on a sporting event in the last year. We found that sports bettors are at greater risk of problem gambling due to differences in attitudes towards gambling, personality traits, thinking styles, erroneous cognitions, and gambling motivations. Moreover, our findings suggest that the difference between individuals who bet on sports and those who do not is more quantitative than qualitative. A stratified stochastic search variable selection analysis by type of bettor revealed similar important predictors of problem gambling for both sports bettors and non-sports bettors; however, the association between the predictors and problem gambling was stronger for sports bettors. Overall, the findings of this study suggest that preventative methods and interventions for problem gambling should be targeted as a function of whether individuals bet on sports.

Predicting the risk and timing of major mood disorder in offspring of bipolar parents: exploring the utility of a neural network approach.

BACKGROUND: Bipolar disorder onset peaks over early adulthood and confirmed family history is a robust risk factor. However, penetrance within families varies and most children of bipolar parents will not develop the illness. Individualized risk prediction would be helpful for identifying those young people most at risk and to inform targeted intervention. Using prospectively collected data from the Canadian Flourish High-risk Offspring cohort study available in routine practice, we explored the use of a neural network, known as the Partial Logistic Artificial Neural Network (PLANN) to predict the time to diagnosis of major mood disorders in 1, 3 and 5-year intervals. RESULTS: Overall, for predictive performance, PLANN outperformed the more traditional discrete survival model for 3-year and 5-year predictions. PLANN was better able to discriminate or rank individuals based on their risk of developing a major mood disorder, better able to predict the probability of developing a major mood disorder and better able to identify individuals who would be diagnosed in future time intervals. The average AUC achieved by PLANN for 5-year prediction was 0.74, which indicates good discrimination. CONCLUSIONS: This evaluation of PLANN is a useful step in the investigation of using neural networks as tools in the prediction of mood disorders in at-risk individuals and the potential that neural networks have in this field. Future research is needed to replicate these findings in a separate high-risk offspring sample.