Strengthening Clinical Research in Marriage and Family Therapy: Challenges and Multilevel Solutions

There is a critical need for high‐quality and accessible treatments to improve mental health. Yet, there are indications that the research being conducted by contemporary marriage and family therapy (MFT) scholars focuses less on advancing and disseminating clinical interventions than in previous decades. In this article, we describe challenges to increasing rigorous clinical research in MFT. We use systems mapping and the intervention‐level framework to identify strategic goals designed to drive innovation in clinical research in the field. It is our hope this article encourages dialog and action among MFT stakeholder groups to support clinical science that will improve the health and functioning of families.     

Integrating self-care into clinical practice with trauma clients

ABSTRACT

Self-care is recognized as an important aspect of social work practice. Beginning in educational settings, developing social workers are encouraged to learn about and practice self-care. However, self-care is typically promoted through strategies and approaches outside of the practice context. Social workers are oftentimes encouraged to use individualized self-care plans, which often feature a variety of relaxation and secondary techniques; focus on self-awareness and self-reflection; and have proper use of supervision. Although these strategies are invaluable, they do little to directly benefit the social worker during direct practice or while in session with a client which is a time period when clinicians are particularly vulnerable to distress or burnout. The importance of real-time self-care strategies is particularly critical for clinicians, especially those who engage in trauma work, given the sensitivity of topics that clients may have endured or benefit from assistance in processing. Further, little information has been published on the use of self-care techniques during a session with a client or in a clinical context. The purpose of the present paper is to discuss self-care strategies that can be useful both outside of and within a clinical setting and present a model of such strategies that focus on integrating self-care in clinical practice particularly for those engaging in trauma work. The paper will conclude by discussing the relevancy of the developed model in practice.     

Mothers' interoceptive knowledge predicts children's emotion regulation and social skills in middle childhood

Interoception, often defined as the perception of internal physiological changes, is implicated in many adult social affective processes, but its effects remain understudied in the context of parental socialization of children's emotions. We hypothesized that what parents know about the interoceptive concomitants of emotions, or interoceptive knowledge (e.g., “my heart races when excited”), may be especially relevant in emotion socialization and in supporting children's working models of emotions and the social world. We developed a measure of mothers' interoceptive knowledge about their own emotions and examined its relation to children's social affective outcomes relative to other socialization factors, including self‐reported parental behaviors, emotion beliefs, and knowledge of emotion‐relevant situations and non‐verbal expressions. To assess these, mothers (N = 201) completed structured interviews and questionnaires. A few months later, third‐grade teachers rated children's social skills and emotion regulation observed in the classroom. Results indicated that mothers' interoceptive knowledge about their own emotions was associated with children's social affective skills (emotion regulation, social initiative, cooperation, self‐control), even after controlling for child gender and ethnicity, family income, maternal stress, and the above maternal socialization factors. Overall, findings suggest that mothers' interoceptive knowledge may provide an additional, unique pathway by which children acquire social affective competence.     

Estimation for seasonal fractional ARIMA with stable innovations via the empirical characteristic function method

Seasonal fractional ARIMA (ARFISMA) model with infinite variance innovations is used in the analysis of seasonal long-memory time series with large fluctuations (heavy-tailed distributions). Two methods, which are the empirical characteristic function (ECF) procedure developed by Knight and Yu [The empirical characteristic function in time series estimation. Econometric Theory. 2002;18:691–721] and the Two-Step method (TSM) are proposed to estimate the parameters of stable ARFISMA model. The ECF method estimates simultaneously all the parameters, while the TSM considers in the first step the Markov Chains Monte Carlo–Whittle approach introduced by Ndongo et al. [Estimation of long-memory parameters for seasonal fractional ARIMA with stable innovations. Stat Methodol. 2010;7:141–151], combined with the maximum likelihood estimation method developed by Alvarez and Olivares [Méthodes d'estimation pour des lois stables avec des applications en finance. Journal de la Société Française de Statistique. 2005;1(4):23–54] in the second step. Monte Carlo simulations are also used to evaluate the finite sample performance of these estimation techniques.     

Bayesian estimation of reliability using time-to-failure distribution of parametric degradation models

This paper focusses on computing the Bayesian reliability of components whose performance characteristics (degradation – fatigue and cracks) are observed during a specified period of time. Depending upon the nature of degradation data collected, we fit a monotone increasing or decreasing function for the data. Since the components are supposed to have different lifetimes, the rate of degradation is assumed to be a random variable. At a critical level of degradation, the time to failure distribution is obtained. The exponential and power degradation models are studied and exponential density function is assumed for the random variable representing the rate of degradation. The maximum likelihood estimator and Bayesian estimator of the parameter of exponential density function, predictive distribution, hierarchical Bayes approach and robustness of the posterior mean are presented. The Gibbs sampling algorithm is used to obtain the Bayesian estimates of the parameter. Illustrations are provided for the train wheel degradation data.     

Measuring stakeholder identification with nonprofit causes: the development and validation of the identification with social causes scale

Nonprofit organizations (NPOs) achieve desired outcomes by fostering organizational identification. However, little research has explored how identification with the cause or the recipients of support relates to identification with the NPO. This study developed the Identification with Social Causes Scale as a mechanism for distinguishing organizational identification from identification with a social group or cause. Data were collected using two groups: the homeless (n = 318) and HIV positive (n = 314) individuals. Exploratory factor analysis and multigroup confirmatory factor analysis yielded a 9-item scale measuring two dimensions: attachment and consubstantiality. Initial construct validity of the scale was established.     

The beta generalized linear exponential distribution

In this paper, a new five-parameter lifetime distribution called beta generalized linear exponential distribution (BGLED) is introduced. It includes at least 17 popular sub-models as special cases such as the beta linear exponential, the beta generalized exponential, and the exponentiated generalized linear distributions. Mathematical and statistical properties of the proposed distribution are discussed in details. In particular, explicit expression for the density function, moments, asymptotics distributions for sample extreme statistics, and other statistical measures are obtained. The estimation of the parameters by the method of maximum-likelihood is discussed and the finite sample properties of the maximum-likelihood estimators (MLEs) are investigated numerically. A real data set is used to demonstrate its superior performance fit over several existing popular lifetime models.