Validating the Psychological Climate Scale in Voluntary Child Welfare

Objective: Organizational climate has emerged as an important factor in understanding and addressing the complexities of providing services in child welfare. This research examines the psychometric properties of each of the dimensions of Parker and colleagues’ Psychological Climate Survey in a sample of voluntary child welfare workers. Methods: Confirmatory factor analysis was utilized to analyze data on 640 child welfare workers providing services directly to children and families. Results: Strong models were developed for each dimension. Each validated model was more parsimonious than in the original instrument but supported the theoretical underpinnings of each. Discussion and Applications to Social Work: Psychological climate in voluntary child welfare agencies can be assessed along each of four dimensions identified by Parker and colleagues: job, role, organization, and supervision. Those wishing to examine psychological climate in voluntary child welfare settings should consider using the models identified in the current research

Workforce Retention Issues in Voluntary Child Welfare

Unlike many studies focused on retention and turnover in public child welfare, this study examined issues of job satisfaction and retention in voluntary child welfare. Although three-fourths of the 1, 624 workers surveyed intended to remain in child welfare, 57.3% had thought about leaving their agencies during the past year. All respondents were dissatisfied with their level of pay, but those thinking of leaving were significantly less satisfied with the contingent rewards they received

Societal Factors Impacting Child Welfare: Validating the Perceptions of Child Welfare Scale

Objective: This research examines the psychometric properties of the Perceptions of Child Welfare Scale (PCWS). This instrument is designed to assess child welfare workers ’ understanding of how society views their role and their work. Methods: Confirmatory factor analysis (CFA) was utilized to analyze data on 538 child welfare workers. Results: The final model consisted of three latent variables with 14 indicators related to stigma, value, and respect (w2 362.33, p .00; root mean square error of approximation [RMSEA] .09; 90 % confidence interval [CI]: [.08,.09]; comparative fit index [CFI] .96; Tucker–Lewis Index [TLI] .95). Discussion: The way in which workers believe others view their work suggests an increasingly complex prototype for understanding workforce issues. Those wishing to examine societal factors related to child welfare workforce issues could use this validated instrument

Design Teams as an Organizational Intervention to Improve Job Satisfaction and Worker Turnover in Public Child Welfare

This study focuses on the field test of a design team intervention in two rural and one urban site experiencing high workforce turnover. Hypothesis 1: Job satisfaction is significantly improved among public child welfare workers participating in the Design Team intervention. Hypothesis 2: Job satisfaction is significantly related to lower turnover in public child welfare workers participating in the Design Team intervention. The Design Team is an externally facilitated intervention in which team members consist of caseworkers and supervisors representing all services provided by the agency. The facilitator uses a formal logic model and team building expertise to guide the team. A pre-post design and structural equation modeling findings indicate a positive impact to overall worker job satisfaction and satisfaction with the nature of their work, and has strong potential to reduce turnover

Expert-augmented machine learning.

Machine learning is proving invaluable across disciplines. However, its success is often limited by the quality and quantity of available data, while its adoption is limited by the level of trust afforded by given models. Human vs. machine performance is commonly compared empirically to decide whether a certain task should be performed by a computer or an expert. In reality, the optimal learning strategy may involve combining the complementary strengths of humans and machines. Here, we present expert-augmented machine learning (EAML), an automated method that guides the extraction of expert knowledge and its integration into machine-learned models. We used a large dataset of intensive-care patient data to derive 126 decision rules that predict hospital mortality. Using an online platform, we asked 15 clinicians to assess the relative risk of the subpopulation defined by each rule compared to the total sample. We compared the clinician-assessed risk to the empirical risk and found that, while clinicians agreed with the data in most cases, there were notable exceptions where they overestimated or underestimated the true risk. Studying the rules with greatest disagreement, we identified problems with the training data, including one miscoded variable and one hidden confounder. Filtering the rules based on the extent of disagreement between clinician-assessed risk and empirical risk, we improved performance on out-of-sample data and were able to train with less data. EAML provides a platform for automated creation of problem-specific priors, which help build robust and dependable machine-learning models in critical applications

WebAL Comes of Age: A review of the first 21 years of Artificial Life on the Web

We present a survey of the first 21 years of web-based artificial life (WebAL) research and applications, broadly construed to include the many different ways in which artificial life and web technologies might intersect. Our survey covers the period from 1994—when the first WebAL work appeared—up to the present day, together with a brief discussion of relevant precursors. We examine recent projects, from 2010–2015, in greater detail in order to highlight the current state of the art. We follow the survey with a discussion of common themes and methodologies that can be observed in recent work and identify a number of likely directions for future work in this exciting area

HOW THE GROWING GAP IN LIFE EXPECTANCY MAY AFFECT RETIREMENT BENEFITS AND REFORMS.

Older Americans have experienced dramatic gains in life expectancy in recent decades, but an emerging literature reveals that these gains are accumulating mostly to those at the top of the income distribution. We explore how growing inequality in life expectancy affects lifetime benefits from Social Security, Medicare, and other programs and how this phenomenon interacts with possible program reforms. We first project that life expectancy at age 50 for males in the two highest income quintiles will rise by 7 to 8 years between the 1930 and 1960 birth cohorts, but that the two lowest income quintiles will experience little to no increase over that time period. This divergence in life expectancy will cause the gap between average lifetime program benefits received by men in the highest and lowest quintiles to widen by $130,000 (in$2009) over this period. Finally we simulate the effect of Social Security reforms such as raising the normal retirement age and changing the benefit formula to see whether they mitigate or enhance the reduced progressivity resulting from the widening gap in life expectancy

The Recent Volcanic History of Axial Seamount: Geophysical Insights into Past Eruption Dynamics with an Eye Toward Enhanced Observations of Future Eruptions

To understand the processes that form oceanic crust as well as the role of submarine volcanoes in exchanging heat and chemicals with the ocean and in supporting chemosynthetic biological communities, it is essential to study underwater eruptions. The world’s most advanced underwater volcano observatory—the Ocean Observatories Initiative Cabled Array at Axial Seamount—builds upon ~30 years of sustained geophysical monitoring at this site with autonomous and remote systems. In April 2015, only months after the Cabled Array’s installation, it recorded an eruption at Axial Seamount, adding to the records of two prior eruptions in 1998 and 2011. Between eruptions, magma recharge is focused beneath the southeast part of the summit caldera, leading to steady inflation and increasing rates of seismicity. During each eruption, the volcano deflates over days to weeks, coincident with high levels of seismicity as a dike is emplaced along one of the volcano’s rifts and lava erupts on the seafloor. Cabled Array seismic data show that motions on an outward-dipping ring fault beneath the caldera accommodate the inflation and deflation. Eruptions appear to occur at a predictable level of inflation; hence, it should be possible to time deployments of additional cabled and autonomous instrumentation to further enhance observations of the next eruption

Tox21Enricher-Shiny: an R Shiny application for toxicity functional annotation analysis

Inference of toxicological and mechanistic properties of untested chemicals through structural or biological similarity is a commonly employed approach for initial chemical characterization and hypothesis generation. We previously developed a web-based application, Tox21Enricher-Grails, on the Grails framework that identifies enriched biological/toxicological properties of chemical sets for the purpose of inferring properties of untested chemicals within the set. It was able to detect significantly overrepresented biological (e.g., receptor binding), toxicological (e.g., carcinogenicity), and chemical (e.g., toxicologically relevant chemical substructures) annotations within sets of chemicals screened in the Tox21 platform. Here, we present an R Shiny application version of Tox21Enricher-Grails, Tox21Enricher-Shiny, with more robust features and updated annotations. Tox21Enricher-Shiny allows users to interact with the web application component (available at http://hurlab.med.und.edu/Tox21Enricher/) through a user-friendly graphical user interface or to directly access the application’s functions through an application programming interface. This version now supports InChI strings as input in addition to CASRN and SMILES identifiers. Input chemicals that contain certain reactive functional groups (nitrile, aldehyde, epoxide, and isocyanate groups) may react with proteins in cell-based Tox21 assays: this could cause Tox21Enricher-Shiny to produce spurious enrichment analysis results. Therefore, this version of the application can now automatically detect and ignore such problematic chemicals in a user’s input. The application also offers new data visualizations, and the architecture has been greatly simplified to allow for simple deployment, version control, and porting. The application may be deployed onto a Posit Connect or Shiny server, and it uses Postgres for database management. As other Tox21-related tools are being migrated to the R Shiny platform, the development of Tox21Enricher-Shiny is a logical transition to use R’s strong data analysis and visualization capacities and to provide aesthetic and developmental consistency with other Tox21 applications developed by the Division of Translational Toxicology (DTT) at the National Institute of Environmental Health Sciences (NIEHS)