Aligning Perspectives of Subjective Well-Being: Comparing Spouse and Colleague Perceptions of Social Worker Happiness

Social workers experience higher rates of burnout and attrition when compared to other health related occupational groups. Previous research on the well being of social workers has tended to focus on the social workers themselves. But the development of well-being is dynamic and is fostered through relationships and interactions with others. In the case of social workers, these relationships include workplace, professional, and personal life interactions. This research sought to better understand the level of congruence between a social worker’s perspective of well-being and perspectives held by significant people in their workplace and at home. Utilizing qualitative methods we interviewed colleagues and spouses (n=10) of social workers that were found to have high levels of work-related subjective well-being. The findings support previous conclusions on the positive subjective well-being (SWB) of practicing social workers, but also indicate a lack of a deeper understanding of the nuances that contribute to social worker SWB. These findings are particularly useful for social workers trying to enhance their SWB, and have direct applicability in education and professional development settings that seek to enhance social worker self-care

Large-scale sequencing identifies multiple genes and rare variants associated with Crohn’s disease susceptibility

peer reviewe

Hitting the Ground Running: Neo-Conservatism and First-Year Canadian Social Workers

This paper explores how the popular use of the expression “hitting the ground running” in reference to beginning social work practice draws upon military imagery and reflects neo-conservative expectations of first-year social workers.  Discussion of the international and Canadian definitions of social work, key social work values, the neo-conservative paradigm, and the role of language in understanding human experiences provides context to this analysis.  Ultimately, it is argued that it is in the best interests of the social work profession for the phrase hitting the ground running to be abandoned (or used critically) when making reference to first-year social workers, and a new metaphor is suggested that could take its place in the social work lexicon

Land cover affects microclimate and temperature suitability for arbovirus transmission in an urban landscape.

The emergence of mosquito-transmitted viruses poses a global threat to human health. Combining mechanistic epidemiological models based on temperature-trait relationships with climatological data is a powerful technique for environmental risk assessment. However, a limitation of this approach is that the local microclimates experienced by mosquitoes can differ substantially from macroclimate measurements, particularly in heterogeneous urban environments. To address this scaling mismatch, we modeled spatial variation in microclimate temperatures and the thermal potential for dengue transmission by Aedes albopictus across an urban-to-rural gradient in Athens-Clarke County GA. Microclimate data were collected across gradients of tree cover and impervious surface cover. We developed statistical models to predict daily minimum and maximum microclimate temperatures using coarse-resolution gridded macroclimate data (4000 m) and high-resolution land cover data (30 m). The resulting high-resolution microclimate maps were integrated with temperature-dependent mosquito abundance and vectorial capacity models to generate monthly predictions for the summer and early fall of 2018. The highest vectorial capacities were predicted for patches of trees in urban areas with high cover of impervious surfaces. Vectorial capacity was most sensitive to tree cover during the summer and became more sensitive to impervious surfaces in the early fall. Predictions from the same models using temperature data from a local meteorological station consistently over-predicted vectorial capacity compared to the microclimate-based estimates. This work demonstrates that it is feasible to model variation in mosquito microenvironments across an urban-to-rural gradient using satellite Earth observations. Epidemiological models applied to the microclimate maps revealed localized patterns of temperature suitability for disease transmission that would not be detectable using macroclimate data. Incorporating microclimate data into disease transmission models has the potential to yield more spatially precise and ecologically interpretable metrics of mosquito-borne disease transmission risk in urban landscapes

Disruptions in neural connectivity associated with reduced susceptibility to a depth inversion illusion in youth at ultra high risk for psychosis

Patients with psychosis exhibit a reduced susceptibility to depth inversion illusions (DII) in which a physically concave surface is perceived as convex (e.g., the hollow mask illusion). Here, we examined the extent to which lessened susceptibility to DII characterized youth at ultra high risk (UHR) for psychosis. In this study, 44 UHR participants and 29 healthy controls judged the apparent convexity of face-like human masks, two of which were concave and the other convex. One of the concave masks was painted with realistic texture to enhance the illusion; the other was shown without such texture. Networks involved with top-down and bottom-up processing were evaluated with resting state functional connectivity magnetic resonance imaging (fcMRI). We examined regions associated with the fronto-parietal network and the visual system and their relations with susceptibility to DII. Consistent with prior studies, the UHR group was less susceptible to DII (i.e., they were characterized by more veridical perception of the stimuli) than the healthy control group. Veridical responses were related to weaker connectivity within the fronto-parietal network, and this relationship was stronger in the UHR group, suggesting possible abnormalities of top-down modulation of sensory signals. This could serve as a vulnerability marker and a further clue to the pathogenesis of psychosis

Comprehensive estimation of spatial and temporal migratory connectivity across the annual cycle to direct conservation efforts

Migratory connectivity is the degree to which populations are linked in space and time across the annual cycle. Low connectivity indicates mixing of populations while high connectivity indicates population separation in space or time. High migratory connectivity makes individual populations susceptible to local environmental conditions; therefore, evaluating migratory connectivity continuously across a species range is important for understanding differential population trends and revealing places and times contributing to these differences. The common nighthawk Chordeiles minor is a widespread, declining, long‐distance migratory bird. Variable population trends across the nighthawk breeding range suggest that knowledge of migratory connectivity is needed to direct conservation. We used GPS tags to track 52 individuals from 12 breeding populations. We estimated migratory connectivity as 0.29 (Mantel coefficient: 0 = no connectivity, 1 = full connectivity) between the breeding and wintering grounds. We then estimated migratory connectivity at every latitude (spatial connectivity) or day (temporal connectivity) of migration and smoothed those migratory connectivity estimates to produce continuous migratory connectivity ‘profiles'. Spatial and temporal connectivity were highest during migration through North America (around 0.3–0.6), with values generally around 0 in Central and South America due to mixing of populations along a common migratory route and similar migration timing across populations. We found local peaks in spatial and temporal connectivity during migration associated with crossing the Gulf of Mexico. We used simulations to estimate the probability that our method missed peaks (spatial: 0.12, temporal: 0.18) or detected false peaks (spatial: 0.11, temporal: 0.37) due to data gaps and showed that our approach remains useful even for sparse and/or sporadic location data. Our study presents a generalizable approach to evaluating migratory connectivity across the full annual cycle that can be used to focus migratory bird conservation towards places and times of the annual cycle where populations are more likely to be limited

SARS-CoV-2 RNAemia predicts clinical deterioration and extrapulmonary complications from COVID-19

BackgroundThe determinants of coronavirus disease 2019 (COVID-19) disease severity and extrapulmonary complications (EPCs) are poorly understood. We characterized relationships between severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) RNAemia and disease severity, clinical deterioration, and specific EPCs.MethodsWe used quantitative and digital polymerase chain reaction (qPCR and dPCR) to quantify SARS-CoV-2 RNA from plasma in 191 patients presenting to the emergency department with COVID-19. We recorded patient symptoms, laboratory markers, and clinical outcomes, with a focus on oxygen requirements over time. We collected longitudinal plasma samples from a subset of patients. We characterized the role of RNAemia in predicting clinical severity and EPCs using elastic net regression.ResultsOf SARS-CoV-2-positive patients, 23.0% (44 of 191) had viral RNA detected in plasma by dPCR, compared with 1.4% (2 of 147) by qPCR. Most patients with serial measurements had undetectable RNAemia within 10 days of symptom onset, reached maximum clinical severity within 16 days, and symptom resolution within 33 days. Initially RNAemic patients were more likely to manifest severe disease (odds ratio, 6.72 [95% confidence interval, 2.45-19.79]), worsening of disease severity (2.43 [1.07-5.38]), and EPCs (2.81 [1.26-6.36]). RNA loads were correlated with maximum severity (r = 0.47 [95% confidence interval, .20-.67]).ConclusionsdPCR is more sensitive than qPCR for the detection of SARS-CoV-2 RNAemia, which is a robust predictor of eventual COVID-19 severity and oxygen requirements, as well as EPCs. Because many COVID-19 therapies are initiated on the basis of oxygen requirements, RNAemia on presentation might serve to direct early initiation of appropriate therapies for the patients most likely to deteriorate

Using a 29-mRNA Host Response Classifier To Detect Bacterial Coinfections and Predict Outcomes in COVID-19 Patients Presenting to the Emergency Department

Clinicians in the emergency department (ED) face challenges in concurrently assessing patients with suspected COVID-19 infection, detecting bacterial coinfection, and determining illness severity since current practices require separate workflows. Here, we explore the accuracy of the IMX-BVN-3/IMX-SEV-3 29 mRNA host response classifiers in simultaneously detecting severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection and bacterial coinfections and predicting clinical severity of COVID-19. A total of 161 patients with PCR-confirmed COVID-19 (52.2% female; median age, 50.0 years; 51% hospitalized; 5.6% deaths) were enrolled at the Stanford Hospital ED. RNA was extracted (2.5 mL whole blood in PAXgene blood RNA), and 29 host mRNAs in response to the infection were quantified using Nanostring nCounter. The IMX-BVN-3 classifier identified SARS-CoV-2 infection in 151 patients with a sensitivity of 93.8%. Six of 10 patients undetected by the classifier had positive COVID tests more than 9 days prior to enrollment, and the remaining patients oscillated between positive and negative results in subsequent tests. The classifier also predicted that 6 (3.7%) patients had a bacterial coinfection. Clinical adjudication confirmed that 5/6 (83.3%) of the patients had bacterial infections, i.e., Clostridioides difficile colitis (n = 1), urinary tract infection (n = 1), and clinically diagnosed bacterial infections (n = 3), for a specificity of 99.4%. Two of 101 (2.8%) patients in the IMX-SEV-3 "Low" severity classification and 7/60 (11.7%) in the "Moderate" severity classification died within 30 days of enrollment. IMX-BVN-3/IMX-SEV-3 classifiers accurately identified patients with COVID-19 and bacterial coinfections and predicted patients' risk of death. A point-of-care version of these classifiers, under development, could improve ED patient management, including more accurate treatment decisions and optimized resource utilization. IMPORTANCE We assay the utility of the single-test IMX-BVN-3/IMX-SEV-3 classifiers that require just 2.5 mL of patient blood in concurrently detecting viral and bacterial infections as well as predicting the severity and 30-day outcome from the infection. A point-of-care device, in development, will circumvent the need for blood culturing and drastically reduce the time needed to detect an infection. This will negate the need for empirical use of broad-spectrum antibiotics and allow for antibiotic use stewardship. Additionally, accurate classification of the severity of infection and the prediction of 30-day severe outcomes will allow for appropriate allocation of hospital resources