Barriers and Facilitators of Responding to Problem Gambling: Perspectives from Australian Mental Health Services

Despite high rates of comorbidity between problem gambling and mental health disorders, few studies have examined barriers or facilitators to the implementation of screening for problem gambling in mental health services. This exploratory qualitative study identified key themes associated with screening in mental health services. Semi-structured interviews were undertaken with 30 clinicians and managers from 11 mental health services in Victoria, Australia. Major themes and subthemes were identified using qualitative content analysis. Six themes emerged including competing priorities, importance of routine screening, access to appropriate screening tools, resources, patient responsiveness and workforce development. Barriers to screening included a focus on immediate risk as well as gambling being often considered as a longer-term concern. Clinicians perceived problem gambling as a relatively rare condition, but did acknowledge the need for brief screening. Facilitators to screening were changes to system processes, such as identification of an appropriate brief screening instrument, mandating its use as part of routine screening, as well as funded workforce development activities in the identification and management of problem gambling.     

Understanding engagement in the Program of Assertive Community Treatment (PACT) from the perspectives of individuals receiving treatment

ABSTRACT

Engagement of individuals with serious mental illnesses in community mental health services is a significant challenge. The Program of Assertive Community Treatment (PACT) is an individual-centered and self-contained mental health program that provides psychiatric treatment, rehabilitation, and support services to persons with serious mental illness who have a history of or likelihood of disengagement with services. Understanding what helps and hinders consumers’ involvement in PACT services may provide information on how to tailor engagement strategies to individuals based on their treatment needs and preferences. The current study builds on existing studies by exploring factors that help and hinder engagement in PACT services from the perspectives of individuals receiving treatment. We conducted open-ended, semi-structured interviews with 17 individuals receiving PACT services. Nine themes were identified through thematic analysis: Desirable qualities of PACT, Focusing on positive outcomes, PACT as a safety net, Recovery orientation, Practical barriers, Conflictual relationships, and Medication side-effects. We conclude that the development of a trusting, therapeutic relationship that is collaborative and person-centered and that is facilitated by both practical and emotional support is critical to engaging individuals in treatment and maximizing positive outcomes.     

<Emphasis Type="BoldItalic">MAD-STEC</Emphasis>: a method for multiple automatic detection of space-time emerging clusters

Crime or disease surveillance commonly rely in space-time clustering methods to identify emerging patterns. The goal is to detect spatial-temporal clusters as soon as possible after its occurrence and to control the rate of false alarms. With this in mind, a spatio-temporal multiple cluster detection method was developed as an extension of a previous proposal based on a spatial version of the Shiryaev–Roberts statistic. Besides the capability of multiple cluster detection, the method have less input parameter than the previous proposal making its use more intuitive to practitioners. To evaluate the new methodology a simulation study is performed in several scenarios and enlighten many advantages of the proposed method. Finally, we present a case study to a crime data-set in Belo Horizonte, Brazil.     

Testing for bioequivalence of highly variable drugs from TR‐RT crossover designs with heterogeneous residual variances

Traditional bioavailability studies assess average bioequivalence (ABE) between the test (T) and reference (R) products under the crossover design with TR and RT sequences. With highly variable (HV) drugs whose intrasubject coefficient of variation in pharmacokinetic measures is 30% or greater, assertion of ABE becomes difficult due to the large sample sizes needed to achieve adequate power. In 2011, the FDA adopted a more relaxed, yet complex, ABE criterion and supplied a procedure to assess this criterion exclusively under TRR‐RTR‐RRT and TRTR‐RTRT designs. However, designs with more than 2 periods are not always feasible. This present work investigates how to evaluate HV drugs under TR‐RT designs. A mixed model with heterogeneous residual variances is used to fit data from TR‐RT designs. Under the assumption of zero subject‐by‐formulation interaction, this basic model is comparable to the FDA‐recommended model for TRR‐RTR‐RRT and TRTR‐RTRT designs, suggesting the conceptual plausibility of our approach. To overcome the distributional dependency among summary statistics of model parameters, we develop statistical tests via the generalized pivotal quantity (GPQ). A real‐world data example is given to illustrate the utility of the resulting procedures. Our simulation study identifies a GPQ‐based testing procedure that evaluates HV drugs under practical TR‐RT designs with desirable type I error rate and reasonable power. In comparison to the FDA's approach, this GPQ‐based procedure gives similar performance when the product's intersubject standard deviation is low (≤0.4) and is most useful when practical considerations restrict the crossover design to 2 periods.     

Staff Development as a School Climate Intervention to Support Transgender and Gender Nonconforming Students: An Integrated Research Partnership Model for School Counselors and Counselor Educators

ABSTRACT

This article posits that an integrative research partnership model can assist school counselors in addressing the marginalization of transgender students in schools through staff development. A methodological integration of action research and program evaluation through quantitative and qualitative data collection is explained, and a collaborative process is highlighted. A case example is provided to demonstrate the use of the design.     

Flexible objective Bayesian linear regression with applications in survival analysis

We study objective Bayesian inference for linear regression models with residual errors distributed according to the class of two-piece scale mixtures of normal distributions. These models allow for capturing departures from the usual assumption of normality of the errors in terms of heavy tails, asymmetry, and certain types of heteroscedasticity. We propose a general non-informative, scale-invariant, prior structure and provide sufficient conditions for the propriety of the posterior distribution of the model parameters, which cover cases when the response variables are censored. These results allow us to apply the proposed models in the context of survival analysis. This paper represents an extension to the Bayesian framework of the models proposed in [16]. We present a simulation study that shows good frequentist properties of the posterior credible intervals as well as point estimators associated to the proposed priors. We illustrate the performance of these models with real data in the context of survival analysis of cancer patients.     

Model selection criteria in beta regression with varying dispersion

We address the issue of model selection in beta regressions with varying dispersion. The model consists of two submodels, namely: for the mean and for the dispersion. Our focus is on the selection of the covariates for each submodel. Our Monte Carlo evidence reveals that the joint selection of covariates for the two submodels is not accurate in finite samples. We introduce two new model selection criteria that explicitly account for varying dispersion and propose a fast two step model selection scheme which is considerably more accurate and is computationally less costly than usual joint model selection. Monte Carlo evidence is presented and discussed. We also present the results of an empirical application.     

The bivariate alpha-skew-normal distribution

In this paper, we propose a new bivariate distribution, namely bivariate alpha-skew-normal distribution. The proposed distribution is very flexible and capable of generalizing the univariate alpha-skew-normal distribution as its marginal component distributions; it features a probability density function with up to two modes and has the bivariate normal distribution as a special case. The joint moment generating function as well as the main moments are provided. Inference is based on a usual maximum-likelihood estimation approach. The asymptotic properties of the maximum-likelihood estimates are verified in light of a simulation study. The usefulness of the new model is illustrated in a real benchmark data.     

A general long-term aging model with different underlying activation mechanisms: Modeling,Bayesian estimation,and case influence diagnostics

In this paper we propose a general cure rate aging model. Our approach enables different underlying activation mechanisms which lead to the event of interest. The number of competing causes of the event of interest is assumed to follow a logarithmic distribution. The model is parameterized in terms of the cured fraction which is then linked to covariates. We explore the use of Markov chain Monte Carlo methods to develop a Bayesian analysis for the proposed model. Moreover, some discussions on the model selection to compare the fitted models are given, as well as case deletion influence diagnostics are developed for the joint posterior distribution based on the ψ-divergence, which has several divergence measures as particular cases, such as the Kullback–Leibler (K-L), J-distance, L₁ norm, and χ²-square divergence measures. Simulation studies are performed and experimental results are illustrated based on a real malignant melanoma data.     

INDUCING OVERCONFIDENCE

In this study, I present a theory which explains how the influence of others may generate overconfidence. The argument is built on the idea that the more help an agent receives when performing a task, the less informative the score on that task will be relative to the agent's ability to perform it. If an agent is confident, he tends to benefit from more cooperation opportunities simply because he is likely to be perceived as being more skilled. As a result, he remains confident because the future signals he will observe will contain very little information regarding his ability. On the contrary, if the agent is not confident, he will receive more informative scores, which will help him learn his true ability faster. (JEL D81, D83, D84)