The Outsiders: Examining the Effects of Political Appointments on Public-Sector Employee Engagement

Scholars have long examined the inherent trade-offs between control and capability when presidents politicize the executive branch through their appointment powers, including through appointments. Research has consistently connected high ratios of appointees to career leaders with decreased agency performance and higher voluntary turnover at the career senior ranks. However, far less attention has been paid to the cumulative effect of such appointments on the engagement of the civil service workforce, a factor shown to influence organizational performance. Using the 2012 and 2016 Federal Employee Viewpoint Surveys, I evaluate the relationship between degree of agency politicization and self-reported measures of engagement among civil servants. Preliminary analysis indicates the use of political appointments by presidents can impede agency efforts to build and sustain an engaged workforce. The findings suggest the negative outcomes associated with these appointments are both broader and more enduring than the tenure of a single appointee, presenting a new perspective for scholarly understanding of the dynamics at play when presidents politicize the agencies they are entrusted to lead

Differential trajectories of tobacco smoking in people at ultra-high risk for psychosis: Associations with clinical outcomes

Objective: People at ultra-high risk (UHR) for psychosis have a high prevalence of tobacco smoking, and rates are even higher among the subgroup that later develop a psychotic disorder. However, the longitudinal relationship between the course of tobacco smoking and clinical outcomes in UHR subjects is unknown. Methods: We investigated associations between tobacco smoking and clinical outcomes in a prospective study of UHR individuals (n = 324). Latent class mixed model analyses were used to identify trajectories of smoking severity. Mixed effects models were applied to investigate associations between smoking trajectory class and the course of attenuated psychotic symptoms (APS) and affective symptoms, as assessed using the CAARMS. Results: We identified four different classes of smoking trajectory: (i) Persistently High (n = 110), (ii) Decreasing (n = 29), (iii) Persistently Low (n = 165) and (iv) Increasing (n = 20). At two-year follow-up, there had been a greater increase in APS in the Persistently High class than for both the Persistently Low (ES = 9.77, SE = 4.87, p = 0.046) and Decreasing (ES = 18.18, SE = 7.61, p = 0.018) classes. There were no differences between smoking classes in the incidence of psychosis. There was a greater reduction in the severity of emotional disturbance and general symptoms in the Decreasing class than in the High (ES = −10.40, SE = 3.41, p = 0.003; ES = −22.36, SE = 10.07, p = 0.027), Increasing (ES = −11.35, SE = 4.55, p = 0.014; ES = −25.58, SE = 13.17, p = 0.050) and Low (ES = −11.38, SE = 3.29, p = 0.001; ES = −27.55, SE = 9.78, p = 0.005) classes, respectively. Conclusions: These findings suggests that in UHR subjects persistent tobacco smoking is associated with an unfavorable course of psychotic symptoms, whereas decrease in the number of cigarettes smoked is associated with improvement in affective symptoms. Future research into smoking cessation interventions in the early stages of psychoses is required to shine light on the potential of modifying smoking behavior and its relation to clinical outcomes.</p

Cross-classified multilevel models improved standard error estimates of covariates in clinical outcomes – a simulation study

Objective: To compare estimates of effect and variability resulting from standard linear regression analysis and hierarchical multilevel analysis with cross-classified multilevel analysis under various scenarios. Study design and setting: We performed a simulation study based on a data structure from an observational study in clinical mental health care. We used a Markov chain Monte Carlo approach to simulate 18 scenarios, varying sample sizes, cluster sizes, effect sizes and between group variances. For each scenario, we performed standard linear regression, multilevel regression with random intercept on patient level, multilevel regression with random intercept on nursing team level and cross-classified multilevel analysis. Results: Applying cross-classified multilevel analyses had negligible influence on the effect estimates. However, ignoring cross-classification led to underestimation of the standard errors of the covariates at the two cross-classified levels and to invalidly narrow confidence intervals. This may lead to incorrect statistical inference. Varying sample size, cluster size, effect size and variance had no meaningful influence on these findings. Conclusion: In case of cross-classified data structures, the use of a cross-classified multilevel model helps estimating valid precision of effects, and thereby, support correct inferences

Association between characteristics of nursing teams and patients' aggressive behavior in closed psychiatric wards

PURPOSE: Estimate the effect of nursing, shift, and patient characteristics on patients' aggression. DESIGN AND METHODS: Follow‐up study on a closed psychiatric ward was performed to estimate the effect of nursing team characteristics and patient characteristics on the incidence of aggression. FINDINGS: The incidence of aggression (n = 802 in sample) was lower in teams with >75% male nurses. Teams scoring high on extraversion experienced more verbal aggression and teams scoring high on neuroticism experienced more physical aggression. Younger patients and/or involuntarily admitted patients were more frequently aggressive. PRACTICE IMPLICATIONS: These findings could stimulate support for nurses to prevent aggression