Screening children with a history of maltreatment for post-traumatic stress disorder in frontline social care organizations: A process evaluation

The use of standardized screening tools to identify trauma exposure and associated symptoms is commonly recommended as a key component in the development of trauma informed services and is seen as integral to facilitating access to evidence-based therapies. However, there is limited evidence in the UK about the factors influencing the adoption of such tools into routine practice in children's social care. This paper presents the findings from a process evaluation of how practitioners implemented a screening tool for post-traumatic stress disorder, anxiety and depression into their day-to-say practice and what worked or did not work during the process. The findings from this study highlight the potential benefits for both young people and practitioners, alongside some of the challenges involved. In particular, practitioners need to see the direct benefit for young people of being assessed. Additionally, practitioners value regular mentoring as they become more proficient in using standardized screening tools.     

Effectiveness of the Coral Tree Family Service family inpatient unit: a longitudinal study exploring change in family functioning

Child mental health disorders represent a major burden to public health in Australia due to high prevalence rates, the widespread impact across domains, and the potential for difficulties to persist into adolescence and adulthood. Extensive evidence exists for the use of parent management training and cognitive behavioural therapy to treat difficulties experienced by children; however, a proportion of children do not benefit as expected from these treatments. The use of complementary therapeutic approaches and variations to the mode and intensity of existing intervention is warranted; family inpatient units represent a unique example of this. The aim of this study is to evaluate the effectiveness of a well-established Australian inpatient unit that admits the whole family, for children aged 12 years and younger referred with mental health, behavioural, or emotional difficulties. This study's longitudinal within-subject study design utilised routinely collected outcome data from the systemic clinical outcome and routine evaluation, 15 item (SCORE-15)—a valid and reliable measure of family functioning—from a sample of 980 participants attending the Family Residential Program. This study reported significant improvement in family functioning across all outcome variables between baseline and post-intervention, with no variables returning to pre-intervention levels at follow-up. Clinically significant changes in overall family functioning showed that 37% of participant scores moved from the clinical range at baseline to the nonclinical range post-intervention. This study represents the first empirical evaluation of the Family Residential Program, and the reported results provide compelling evidence for the program to effect improvement in family functioning for families with long-standing and severe difficulties. Poor rates of questionnaire completion following intervention frequent the literature base of family inpatient units, with low post-intervention (56%) and follow-up (12%) rates evident in this study. This study uniquely contributes to the growing evidence base of family inpatient units using rigorous evaluation methods.     

Walk-in clinic counselling for emotional regulation with low-needs youth on the autism spectrum

A single-session consultation model for low-needs youth on the autism spectrum is presented with the mental health concern of emotional self-regulation, a common issue in family psychotherapy. This research is contextualised within the growing field of short-term therapy as it intersects the growing demand to therapeutically address clients on the autism spectrum. The treatment intervention is delineated through five sequential phases within the walk-in clinic setting for the practitioner clinician. A rationale, overview, and instruction for the practitioner clinician utilising this novel model is provided through a case study format. By using a more structured and integrated approach to treatment, organised through an externalised metaphor, it is suggested that the current model will be more effective for this specific clinical population. Additionally, a case illustration is provided that scaffolds the treatment model, which includes table formats and pictures. The case study illustrates the self-regulation map metaphor visually and interactively linking client challenges, strategies, and motivations simultaneously on multiple electronic devices in session. Relevant works were selected to explore the effectiveness of single-session consultation models for those presenting on the autism spectrum. Treatment interventions for specific clinical populations are recommended, especially within walk-in clinic therapy. This clinical research introduces a paradigm shift towards therapeutically addressing emotional self-regulation with low-needs youth on the spectrum through virtual, interactive technology.     

Stability analysis using mixed models: A critique of tolerance interval methods and a probabilistic solution

Recently, tolerance interval approaches to the calculation of a shelf life of a drug product have been proposed in the literature. These address the belief that shelf life should be related to control of a certain proportion of batches being out of specification. We question the appropriateness of the tolerance interval approach. Our concerns relate to the computational challenges and practical interpretations of the method. We provide an alternative Bayesian approach, which directly controls the desired proportion of batches falling out of specification assuming a controlled manufacturing process. The approach has an intuitive interpretation and posterior distributions are straightforward to compute. If prior information on the fixed and random parameters is available, a Bayesian approach can provide additional benefits both to the company and the consumer. It also avoids many of the computational challenges with the tolerance interval methodology.     

Revisiting the Prediction of Financial Vulnerability

Whether or not a nonprofit organization is vulnerable to financial problems is a concern of all stakeholders of the organization. Recently, Greenlee and Trussel (2000) and Trussel and Greenlee (2001) expanded Tuckman and Chang's work (1991) to predict which organizations are financially vulnerable. This article extends the work of these authors by developing a model of financial vulnerability that includes four financial indicators, controls for the sector to which an organization belongs, and is based on a sample of over ninety‐four thousand organizations. The model is useful as a screening, monitoring, and attention‐getting device.     

Borrowing Information across Populations in Estimating Positive and Negative Predictive Values

A marker's capacity to predict risk of a disease depends on disease prevalence in the target population and its classification accuracy, i.e. its ability to discriminate diseased subjects from non-diseased subjects. The latter is often considered an intrinsic property of the marker; it is independent of disease prevalence and hence more likely to be similar across populations than risk prediction measures. In this paper, we are interested in evaluating the population-specific performance of a risk prediction marker in terms of positive predictive value (PPV) and negative predictive value (NPV) at given thresholds, when samples are available from the target population as well as from another population. A default strategy is to estimate PPV and NPV using samples from the target population only. However, when the marker's classification accuracy as characterized by a specific point on the receiver operating characteristics (ROC) curve is similar across populations, borrowing information across populations allows increased efficiency in estimating PPV and NPV. We develop estimators that optimally combine information across populations. We apply this methodology to a cross-sectional study where we evaluate PCA3 as a risk prediction marker for prostate cancer among subjects with or without previous negative biopsy.     

Testing for qualitative interaction of multiple sources of informative dropout in longitudinal data

Longitudinal studies suffer from patient dropout. The dropout process may be informative if there exists an association between dropout patterns and the rate of change in the response over time. Multiple patterns are plausible in that different causes of dropout might contribute to different patterns. These multiple patterns can be dichotomized into two groups: quantitative and qualitative interaction. Quantitative interaction indicates that each of the multiple sources is biasing the estimate of the rate of change in the same direction, although with differing magnitudes. Alternatively, qualitative interaction results in the multiple sources biasing the estimate of the rate of change in opposing directions. Qualitative interaction is of special concern, since it is less likely to be detected by conventional methods and can lead to highly misleading slope estimates. We explore a test for qualitative interaction based on simultaneous confidence intervals. The test accommodates the realistic situation where reasons for dropout are not fully understood, or even entirely unknown. It allows for an additional level of clustering among participating subjects. We apply these methods to a study exploring tumor growth rates in mice as well as a longitudinal study exploring rates of change in cognitive functioning for Alzheimer's patients.     

Longitudinal dynamic analyses of cognition in the health and retirement study panel

The purpose of this paper is to highlight some classic issues in the measurement of change and to show how contemporary solutions can be used to deal with some of these issues. Five classic issues will be raised here: (1) Separating individual changes from group differences; (2) options for incomplete longitudinal data over time, (3) options for nonlinear changes over time; (4) measurement invariance in studies of changes over time; and (5) new opportunities for modeling dynamic changes. For each issue we will describe the problem, and then review some contemporary solutions to these problems base on Structural Equation Models (SEM). We will fit these SEM to using existing panel data from the Health & Retirement Study (HRS) cognitive variables. This is not intended as an overly technical treatment, so only a few basic equations are presented, examples will be displayed graphically, and more complete references to the contemporary solutions will be given throughout.     

Vendor Selection with Bundling: A Comment*

In a recent article by Rosenthal, Zydiak, and Chaudhry (1995), a mixed integer linear programming model was introduced to solve the vendor selection problem for the case in which the vendor can sell items individually or as part of a bundle. Each vendor offered only one type of bundle, and the buyer could purchase at most one bundle per vendor. The model employed n(m+ 1) binary variables, where n is the number of vendors and m is the number of products they sell. The existing model can lead to a purchasing paradox: it may force the buyer to pay more to receive less. We suggest a reformulation of the same problem that (i) eliminates this paradox and reveals a more cost-effective purchasing strategy; (ii) uses only n integer variables and significantly reduces the computational workload; and (iii) permits the buyer to purchase more than one bundle per vendor.