A test of the Maslach Burnout Inventory in three samples of healthcare professionals

Structural equation modelling with LISREL was used to investigate the factor structure of the Maslach Burnout Inventory (MBI). Emotional exhaustion was the most robust of the MBI's three factors, followed by depersonalization, while the personal accomplishment factor performed weakly. A new measurement model was developed in a sample of 197 nurses consisting of the emotional exhaustion and depersonalization dimensions, which were measured with five and two empirical MBI indicators, respectively. A test of invariance of the two-factor model across three samples (i.e. one calibration sample of nurses, and two validation samples consisting of hospital laboratory technicians and hospital managers with an effective sample size of 445) produced a good fit for the proposed two-factor model. Assessment of psychometric properties of the two-factor model produced (1) internal consistencies comparable to those reported in the literature for the MBI's originally specified emotional exhaustion and depersonalization scales, and (2) correlations with criterion variables that were all in the expected direction and magnitude, comparable to those produced by the originally specified scales. Theoretical implications for the use of the two-factor model in burnout research are discussed.     

A Bayesian Approach to Integrated Ecological and Human Health Risk Assessment for the South River,Virginia Mercury‐Contaminated Site

We conducted a regional‐scale integrated ecological and human health risk assessment by applying the relative risk model with Bayesian networks (BN‐RRM) to a case study of the South River, Virginia mercury‐contaminated site. Risk to four ecological services of the South River (human health, water quality, recreation, and the recreational fishery) was evaluated using a multiple stressor–multiple endpoint approach. These four ecological services were selected as endpoints based on stakeholder feedback and prioritized management goals for the river. The BN‐RRM approach allowed for the calculation of relative risk to 14 biotic, human health, recreation, and water quality endpoints from chemical and ecological stressors in five risk regions of the South River. Results indicated that water quality and the recreational fishery were the ecological services at highest risk in the South River. Human health risk for users of the South River was low relative to the risk to other endpoints. Risk to recreation in the South River was moderate with little spatial variability among the five risk regions. Sensitivity and uncertainty analysis identified stressors and other parameters that influence risk for each endpoint in each risk region. This research demonstrates a probabilistic approach to integrated ecological and human health risk assessment that considers the effects of chemical and ecological stressors across the landscape.     

Bayesian Hierarchical Structure for Quantifying Population Variability to Inform Probabilistic Health Risk Assessments

Human variability is a very important factor considered in human health risk assessment for protecting sensitive populations from chemical exposure. Traditionally, to account for this variability, an interhuman uncertainty factor is applied to lower the exposure limit. However, using a fixed uncertainty factor rather than probabilistically accounting for human variability can hardly support probabilistic risk assessment advocated by a number of researchers; new methods are needed to probabilistically quantify human population variability. We propose a Bayesian hierarchical model to quantify variability among different populations. This approach jointly characterizes the distribution of risk at background exposure and the sensitivity of response to exposure, which are commonly represented by model parameters. We demonstrate, through both an application to real data and a simulation study, that using the proposed hierarchical structure adequately characterizes variability across different populations.     

Cardiac Sensitization Thresholds of Halon Replacement Chemicals Predicted in Humans by Physiologically-Based Pharmacokinetic Modeling

Human exposure to halons and halon replacement chemicals is often regulated on the basis of cardiac sensitization potential. The dose-response data obtained from animal testing are used to determine the no observable adverse effect level (NOAEL) and lowest observable adverse effect level (LOAEL) values. This approach alone does not provide the information necessary to evaluate the cardiac sensitization potential for the chemical of interest under a variety of exposure concentrations and durations. In order to provide a tool for decision-makers and regulators tasked with setting exposure guidelines for halon replacement chemicals, a quantitative approach was established which allowed exposures to be assessed in terms of the chemical concentrations in blood during the exposure. A physiologically-based pharmacokinetic (PBPK) model was used to simulate blood concentrations of Halon 1301 (bromotrifluoromethane, CF₃Br), HFC-125 (pentafluoroethane, CHF₂CF₃), HFC-227ea (heptafluoropropane, CF₃CHFCF₃), HCFC-123 (dichlorotrifluoroethane, CHCl₂CF₃), and CF₃I (trifluoroiodomethane) during inhalation exposures. This work demonstrates a quantitative approach for use in linking chemical inhalation exposures to the levels of chemical in blood achieved during the exposure.     

Is the Gene-Environment Interaction Paradigm Relevant to Genome-Wide Studies? The Case of Education and Body Mass Index

This study uses data from the Framingham Heart Study to examine the relevance of the gene-environment interaction paradigm for genome-wide association studies (GWAS). We use completed college education as our environmental measure and estimate the interactive effect of genotype and education on body mass index (BMI) using 260,402 single-nucleotide polymorphisms (SNPs). Our results highlight the sensitivity of parameter estimates obtained from GWAS models and the difficulty of framing genome-wide results using the existing gene-environment interaction typology. We argue that SNP-environment interactions across the human genome are not likely to provide consistent evidence regarding genetic influences on health that differ by environment. Nevertheless, genome-wide data contain rich information about individual respondents, and we demonstrate the utility of this type of data. We highlight the fact that GWAS is just one use of genome-wide data, and we encourage demographers to develop methods that incorporate this vast amount of information from respondents into their analyses.     

Being Who I Am: Effective Teaching,Learning, Student Support,and Societal Change Through LGBQ Faculty Freedom

Framed within the context of a heterosexist society in which not all sexual minority faculty feel professionally and/or personally safe stepping out of the classroom closet, this qualitative study examines the phenomenon of non-heterosexual faculty disclosing their sexual orientation to students in their classrooms. Findings demonstrate the depth and magnitude of impact the disclosure has on teaching, learning, student support, and societal change. Overarching is that faculty do not necessarily view the experience as “coming out in the classroom,” but rather as “being who I am” in terms of freedom to present an authentic self, if so desired, to students.     

Black,Queer, and Looking for a Job: An Exploratory Study of Career Decision Making Among Self-Identified Sexual Minorities at an Urban Historically Black College/University

This thematically analyzed study seeks to explore the career decision perceptions of sexual minority college students at an urban historically black college/university (HBCU). This qualitative focus group study delved into how sexual minorities feel their visible variables of race, gender expression, and degree of disclosure influence their career thought process. Theories relative to the study included Krumboltz’s social learning theory of career decision-making, gender role theory, racial socialization, Cass’s homosexual identity model, and impression management. Though participants initially proclaimed they did not allow their sexual minority identity to affect their career decisions, their overall responses indicated otherwise.     

Hiding Customer Complaints: Studying the Motivations and Forms of Service Employees' Complaint Concealment Behaviours

This study aims to explore both how and why customer service employees conceal the complaints made by customers. Using an exploratory qualitative approach, data were gathered through the in‐depth interviewing of 25 managers, 25 supervisors and 57 front‐line employees of UK general retailers/supermarkets. Data analysis reveals that the concealment of customer complaints by store employees of all hierarchical levels is common. Front‐line, customer‐contact employees were found to conceal complaints in four ways: through (1) misleading customers, (2) complaint resolution without logging, (3) concealing recorded complaints and (4) falsification of recorded complaints. Store‐level supervisory and managerial employees were also found to conceal complaints in four ways: through (1) complaint resolution without logging, (2) concealing recorded complaints, (3) the non‐recording of complaints and (4) the disregarding of ‘minor’ complaints. The motives for complaint concealment differed between managerial/supervisory and front‐line employees but included reasons of (1) personal protection, (2) perceived customer unpleasantness, (3) serial complaint avoidance, (4) alienation, (5) friends or family protection, (6) instrumental gain, (7) avoidance of additional work, (8) perceived unfairness and (9) limited time. These findings suggest that theorists and practitioners need to acknowledge the existence and prevalence of these motives and behaviours and incorporate them into their conceptualizations and practices.     

Strategic Experimentation

This paper extends the classic two-armed bandit problem to a many-agent setting in which N players each face the same experimentation problem. The main change from the single-agent problem is that an agent can now learn from the current experimentation of other agents. Information is therefore a public good, and a free-rider problem in experimentation naturally arises. More interestingly, the prospect of future experimentation by others encourages agents to increase current experimentation, in order to bring forward the time at which the extra information generated by such experimentation becomes available. The paper provides an analysis of the set of stationary Markov equilibria in terms of the free-rider effect and the encouragement effect.     

Quantitative Risk Assessment: Developing a Bayesian Approach to Dichotomous Dose–Response Uncertainty

Model averaging for dichotomous dose–response estimation is preferred to estimate the benchmark dose (BMD) from a single model, but challenges remain regarding implementing these methods for general analyses before model averaging is feasible to use in many risk assessment applications, and there is little work on Bayesian methods that include informative prior information for both the models and the parameters of the constituent models. This article introduces a novel approach that addresses many of the challenges seen while providing a fully Bayesian framework. Furthermore, in contrast to methods that use Monte Carlo Markov Chain, we approximate the posterior density using maximum a posteriori estimation. The approximation allows for an accurate and reproducible estimate while maintaining the speed of maximum likelihood, which is crucial in many applications such as processing massive high throughput data sets. We assess this method by applying it to empirical laboratory dose–response data and measuring the coverage of confidence limits for the BMD. We compare the coverage of this method to that of other approaches using the same set of models. Through the simulation study, the method is shown to be markedly superior to the traditional approach of selecting a single preferred model (e.g., from the U.S. EPA BMD software) for the analysis of dichotomous data and is comparable or superior to the other approaches.