Alliance and termination status in couple therapy: A comparison of methods for assessing discrepancies

Much of the empirical data available about therapeutic alliance and its relationship to termination status come from individual psychotherapies. We know less about therapeutic alliance in couple therapy. A unique characteristic of alliance in couple or family therapy is the possibility of discrepancies in alliance between system members. In this study we sought to demonstrate three statistical techniques: standard deviations, the intraclass correlation to assess discrepancies in alliance over time during the initial stage of couple therapy, and the use of these various measures to predict termination status using a sample of 72 couples from a university-based training clinic. Differences in partners’ alliances operationalized either as categorical or continuous variables but when analyzed separately at each time point were not predictive of termination status. When multilevel modeling was used, a difference in the way the discrepancies changed over a period of time was related to termination status. Rashmi Gangamma is currently at Syracuse University

Relational ethics, depressive symptoms, and relationship satisfaction in couples in therapy

The purpose of this study was to examine depressive symptoms and relationship satisfaction as problems related to relational ethics in one\u27s family of origin and current partner relationships in a sample of 68 other-sex couples seeking therapy at a large university clinic. We used the Actor Partner Interdependence Model to analyze dyadic data collected prior to beginning therapy. Specifically, we found significant actor effects between relational ethics in one\u27s family of origin and depressive symptoms, as well as between depressive symptoms and low relationship satisfaction for both male and female partners. We also found significant partner effects for relational ethics in current partner relationship, depressive symptoms, and low relationship satisfaction. Clinical application of contextual therapy theory is discussed

Refugee Gardening: An Opportunity to Improve Economic Conditions, Food Security, and Mental Health

Every year, thousands of refugees enter the United States. Conditions prior to resettlement, such as exposure to conflict, persecution, and loss, as well as conditions after resettlement, such as isolation and adjustment to a new culture, impact refugee mental health, economic security, and food security. Refugee access to land and resources for gardening has been shown to have quality of life benefits, including enhanced food security and mental health outcomes. This research brief summarizes the results of a recent study that examined how community gardening may reduce food insecurity and adverse mental health among refugees living in Central New York. Findings demonstrate that refugee gardening positively impacts mental health, food security, and feelings of connection with refugee communities

A comprehensive survey exploring the application of machine learning algorithms in the detection of land degradation

Early and reliable detection of land degradation helps policymakers to take strict action in more vulnerable areas by making strong rules and regulations in order to achieve sustainable land management and conservation. The detection of land degradation is carried out to identify desertification processes using machine learning techniques in different geographical locations, which are always a challenging issue in the global field. Due to the significance of the detection of land degradation, this article provides an exhaustive review of the detection of land degradation using machine learning algorithms. Initially, the current status of land degradation in India is presented, along with a brief discussion on the overview of widely used factors, evaluation parameters, and algorithms used. Consequently, merits and demerits related to machine learning-based land degradation identification are presented. Additionally, solutions are prescribed in order to reduce existing problems in the detection of land degradation. Since one of the major objectives is to explore the future perspectives of machine learning-based land degradation detection, areas including the application of remote sensing, mapping, optimum features, and algorithms have been broadly discussed. Finally, based on a critical evaluation of existing related studies, the architecture of the machine learning-based desertification process has been proposed. This technology can fulfill the research challenges in the detection of land degradation and computation difficulties in the development of models for the detection of land degradation

Evaluation of therapist and client language in Motivational Interviewing (MI) sessions: A secondary analysis of data from the Southern Methodist Alcohol Research Trial (SMART) study

In the United States, “binge” drinking among college students is an emerging public health concern due to the significant physical and psychological effects on young adults. The focus is on identifying interventions that can help decrease high-risk drinking behavior among this group of drinkers. One such intervention is Motivational interviewing (MI), a client-centered therapy that aims at resolving client ambivalence by developing discrepancy and engaging the client in change talk. Of late, there is a growing interest in determining the active ingredients that influence the alliance between the therapist and the client. This study is a secondary analysis of the data obtained from the Southern Methodist Alcohol Research Trial (SMART) project, a dismantling trial of MI and feedback among heavy drinking college students. The present project examines the relationship between therapist and client language in MI sessions on a sample of “binge” drinking college students. Of the 126 SMART tapes, 30 tapes (‘MI with feedback’ group = 15, ‘MI only’ group = 15) were randomly selected for this study. MISC 2.1, a mutually exclusive and exhaustive coding system, was used to code the audio/videotaped MI sessions. Therapist and client language were analyzed for communication characteristics. Overall, therapists adopted a MI consistent style and clients were found to engage in change talk. Counselor acceptance, empathy, spirit, and complex reflections were all significantly related to client change talk (p-values ranged from 0.001 to 0.047). Additionally, therapist ‘advice without permission’ and MI Inconsistent therapist behaviors were strongly correlated with client sustain talk (p-values ranged from 0.006 to 0.048). Simple linear regression models showed a significant correlation between MI consistent (MICO) therapist language (independent variable) and change talk (dependent variable) and MI inconsistent (MIIN) therapist language (independent variable) and sustain talk (dependent variable). The study has several limitations such as small sample size, self-selection bias, poor inter-rater reliability for the global scales and the lack of a temporal measure of therapist and client language. Future studies might consider a larger sample size to obtain more statistical power. In addition the correlation between therapist language, client language and drinking outcome needs to be explored

Characterization and Proinflammatory Response of Airborne Biological Particles from Wastewater Treatment Plants

Wastewater contains a variety of microorganisms, and unit operations in the plants could release these biological components into the air environment. These airborne biological particles could have adverse health effects on plant workers and the downwind population. This study provides a first report on the concentration and characterization of the airborne biological particles in six wastewater treatment plants in Mumbai, India. The study indicates that 49% and 27% of the samples exceed, respectively, the exposure limit for airborne endotoxin and bacteria in occupational settings. Endotoxin was identified as the single most important component of the particulate matter responsible for induction of proinflammatory indicator (tumor necrosis factor-alpha) in in vitro assay. Identification of several clinically important bacterial species in the samples suggests that the workers at the treatment plant are exposed to opportunistic and infectious bacteria. Principal component analysis was used to identify the groups among the bacterial species which serves as the signature for transport study. Analysis also shows that the component related to spore-forming bacteria is present in all samples