The prevalence and characteristics of relational depth events in psychotherapy

We introduce two complementary measures of relational depth, defined as a state of profound contact and engagement between client and therapist. Using an internet-based survey of client and therapist accounts (n = 342), judges rated relational depth as present in over a third of significant therapy event descriptions. Participants also completed the Relational Depth Inventory (RDI), for which we report reliability, validity and factor structure. Relational depth events were more likely to occur in the presence of strong therapeutic alliance, and with female participants, but client or therapist role and therapy duration were not related to relational depth content or RDI. RDI items for connectedness, love, respect and intimacy were most strongly associated with relational depth content

Adozione di RDA alla Library of Congress

The process of implementation of RDA by Library of Congress, National Agricultural Library, and National Library of Medicine is presented. Each phase of development, test, decision, preparation for implementation of RDA and training about RDA is fully and accurately described and discussed. Benefits from implementation of RDA for the Library of Congress are identified and highlighted: more flexibility in cataloguing decisions, easier international sharing of cataloguing data, clearer linking among related works; closer cooperation with other libraries in the North American community, production of an online learning platform in order to deliver RDA training on a large scale in real time to catalogers

Stepping back from crisis points : the provision and acknowledgement of support in an online suicide discussion forum

Suicide is a global health concern, though little is known about the social practices that might support those who are contemplating suicide. Online forums provide a unique insight into the anonymous discussion of suicide, including sociocultural norms about suicide and the delicate management of online interaction. This article examines the provision and acknowledgement of support in an online discussion forum about suicide, using discursive psychology to analyse the textual interaction. The analysis illustrates how forum threads function as case studies, and enable members to gain support on numerous occasions. In this way, members can gain help at crisis points as and when these occur, while still maintaining authenticity as a valid forum member. The analysis also provides additional evidence for models of suicide which highlight the fluid nature of suicidality and contributes to the preventative work on suicide by demonstrating how support can be provided at crisis points. 2 Suicidal behaviours represent a global public health concern and substantial research effort has gone into identifying specific risk and protective factors (e.g., O'Connor & Nock, 2014) . The rise of technology provides a unique opportunity to examine suicidal issues in online spaces (Mishara & Kerkhof, 2013) , yet research to date has focused mainly on the motivations and individual characteristics of those using the internet for this purpose, rather than on the social practices in the online spaces themselves. Following Horne and Wiggins (2009) and Paulus and Varga (2015), this article focuses on the provision and acknowledgement of support in an o nline suicide di scussion forum. Specifically, the aim of this article is to explicate the interactional features of support in the suicide forums, and of the potential for members to be supported by others by being metaphorically talked back from the ‘edge’; i.e., representing a shift from being at a crisis point to a stance that might still be troubled but is not immediately suicidal. As such, the article provides additional empirical support for discursive accounts of managing health identities online as well as demonstrating the potential of qualitative analyses to contribute to preventative work on suicid

Predicting Poor Outcomes Among Individuals Seeking Care for Major Depressive Disorder

OBJECTIVE: To develop and validate algorithms to identify individuals with major depressive disorder (MDD) at elevated risk for suicidality or for an acute care event. METHODS: We conducted a retrospective cohort analysis among adults with MDD diagnosed between January 1, 2018 and February 28, 2019. Generalized estimating equation models were developed to predict emergency department (ED) visit, inpatient hospitalization, acute care visit (ED or inpatient), partial-day hospitalization, and suicidality in the year following diagnosis. Outcomes (per 1000 patients per month, PkPPM) were categorized as all-cause, psychiatric, or MDD-specific and combined into composite measures. Predictors included demographics, medical and pharmacy utilization, social determinants of health, and comorbid diagnoses as well as features indicative of clinically relevant changes in psychiatric health. Models were trained on data from 1.7M individuals, with sensitivity, positive predictive value, and area-under-the-curve (AUC) derived from a validation dataset of 0.7M. RESULTS: Event rates were 124.0 PkPPM (any outcome), 21.2 PkPPM (psychiatric utilization), and 7.6 PkPPM (suicidality). Among the composite models, the model predicting suicidality had the highest AUC (0.916) followed by any psychiatric acute care visit (0.891) and all-cause ED visit (0.790). Event-specific models all achieved an AUC \u3e0.87, with the highest AUC noted for partial-day hospitalization (AUC = 0.938). Select predictors of all three outcomes included younger age, Medicaid insurance, past psychiatric ED visits, past suicidal ideation, and alcohol use disorder diagnoses, among others. CONCLUSIONS: Analytical models derived from clinically-relevant features identify individuals with MDD at risk for poor outcomes and can be a practical tool for health care organizations to divert high-risk populations into comprehensive care models

The Community Health Worker (CHW) Common Indicators Project: Engaging CHWs in Measurement to Sustain the Profession

Despite progress in documenting the outcomes of Community Health Worker interventions, the lack of standardized measures to assess CHW practice has made it difficult for programs to conduct reliable evaluations, and impossible to aggregate data across programs and regions, impeding commitment to sustainable, long-term financing of CHW programs. In addition, while CHWs have sometimes been involved as data collectors, they have seldom been engaged as full partners in all stages of evaluation and research. This manuscript details the current work being done by the CI Project, demonstrating how CHWs are able to contribute to the integrity, sustainability, and viability of CHW programs through the collaborative development and adoption of a set of common process and outcome constructs and indicators for CHW practice and CHW program implementation

Homogeneous Subgroups of Young Children with Autism Improve Phenotypic Characterization in the Study to Explore Early Development

The objective of this study was to identify homogenous classes of young children with autism spectrum disorder (ASD) to improve phenotypic characterization. Children were enrolled in the Study to Explore Early Development between 2 and 5 years of age. 707 children were classified with ASD after a comprehensive evaluation with strict diagnostic algorithms. Four classes of children with ASD were identified from latent class analysis: mild language delay with cognitive rigidity, mild language and motor delay with dysregulation, general developmental delay, and significant developmental delay with repetitive motor behaviors. We conclude that a four-class phenotypic model of children with ASD best describes our data and improves phenotypic characterization of young children with ASD. Implications for screening, diagnosis, and research are discussed

Drug-gene interactions of antihypertensive medications and risk of incident cardiovascular disease: a pharmacogenomics study from the CHARGE consortium

Background Hypertension is a major risk factor for a spectrum of cardiovascular diseases (CVD), including myocardial infarction, sudden death, and stroke. In the US, over 65 million people have high blood pressure and a large proportion of these individuals are prescribed antihypertensive medications. Although large long-term clinical trials conducted in the last several decades have identified a number of effective antihypertensive treatments that reduce the risk of future clinical complications, responses to therapy and protection from cardiovascular events vary among individuals. Methods Using a genome-wide association study among 21,267 participants with pharmaceutically treated hypertension, we explored the hypothesis that genetic variants might influence or modify the effectiveness of common antihypertensive therapies on the risk of major cardiovascular outcomes. The classes of drug treatments included angiotensin-converting enzyme inhibitors, beta-blockers, calcium channel blockers, and diuretics. In the setting of the Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) consortium, each study performed array-based genome-wide genotyping, imputed to HapMap Phase II reference panels, and used additive genetic models in proportional hazards or logistic regression models to evaluate drug-gene interactions for each of four therapeutic drug classes. We used meta-analysis to combine study-specific interaction estimates for approximately 2 million single nucleotide polymorphisms (SNPs) in a discovery analysis among 15,375 European Ancestry participants (3,527 CVD cases) with targeted follow-up in a case-only study of 1,751 European Ancestry GenHAT participants as well as among 4,141 African-Americans (1,267 CVD cases). Results Although drug-SNP interactions were biologically plausible, exposures and outcomes were well measured, and power was sufficient to detect modest interactions, we did not identify any statistically significant interactions from the four antihypertensive therapy meta-analyses (Pinteraction > 5.0×10−8). Similarly, findings were null for meta-analyses restricted to 66 SNPs with significant main effects on coronary artery disease or blood pressure from large published genome-wide association studies (Pinteraction ≥ 0.01). Our results suggest that there are no major pharmacogenetic influences of common SNPs on the relationship between blood pressure medications and the risk of incident CVD

Myocardial infarction and stroke associated with diuretic based two drug antihypertensive regimens: population based case-control study

Objective To examine the association of myocardial infarction and stroke incidence with several commonly used two drug antihypertensive treatment regimens

Burned area and carbon emissions across northwestern boreal North America from 2001-2019

Fire is the dominant disturbance agent in Alaskan and Canadian boreal ecosystems and releases large amounts of carbon into the atmosphere. Burned area and carbon emissions have been increasing with climate change, which have the potential to alter the carbon balance and shift the region from a historic sink to a source. It is therefore critically important to track the spatiotemporal changes in burned area and fire carbon emissions over time. Here we developed a new burned-area detection algorithm between 2001-2019 across Alaska and Canada at 500 m (meters) resolution that utilizes finer-scale 30 m Landsat imagery to account for land cover unsuitable for burning. This method strictly balances omission and commission errors at 500 m to derive accurate landscape- and regional-scale burned-area estimates. Using this new burned-area product, we developed statistical models to predict burn depth and carbon combustion for the same period within the NASA Arctic-Boreal Vulnerability Experiment (ABoVE) core and extended domain. Statistical models were constrained using a database of field observations across the domain and were related to a variety of response variables including remotely sensed indicators of fire severity, fire weather indices, local climate, soils, and topographic indicators. The burn depth and aboveground combustion models performed best, with poorer performance for belowground combustion. We estimate 2.37×106 ha (2.37 Mha) burned annually between 2001-2019 over the ABoVE domain (2.87 Mha across all of Alaska and Canada), emitting 79.3 ± 27.96 Tg (±1 standard deviation) of carbon (C) per year, with a mean combustion rate of 3.13 ± 1.17 kg C m-2. Mean combustion and burn depth displayed a general gradient of higher severity in the northwestern portion of the domain to lower severity in the south and east. We also found larger-fire years and later-season burning were generally associated with greater mean combustion. Our estimates are generally consistent with previous efforts to quantify burned area, fire carbon emissions, and their drivers in regions within boreal North America; however, we generally estimate higher burned area and carbon emissions due to our use of Landsat imagery, greater availability of field observations, and improvements in modeling. The burned area and combustion datasets described here (the ABoVE Fire Emissions Database, or ABoVE-FED) can be used for local- to continental-scale applications of boreal fire science