Predictors of Intent to Stay of Clinical Care Managers in the Inpatient and Outpatient Settings: A Causal-Comparative Study

This quantitative research study was conducted to determine if employee engagement, belief in mission and values, and employee support predict intent to stay of clinical care managers in the inpatient and outpatient settings. The present research study also examined the construct validity of the survey instrument, which was utilized by the subject organization in 2018. Through exploratory factor analysis (EFA), the constructs of employee engagement, belief in mission and values, and employee support were analyzed. Secondary data was collected from the 2018 completed surveys of 160 licensed nurses and social workers considered as clinical care managers from a large health care system in the western United States. The results of the EFA supported the validity of the constructs of belief in mission and values that explained 53.6% of the variability with a .912 reliability coefficient and employee support explained 8.96% of the variability with a .828 reliability coefficient. Employee engagement was deemed invalid due to cross-loadings of the factors with employee support and belief in mission and values. Binary regression analysis did not reveal statistically significant findings due to small sample size and lack of variability of the dichotomous outcome variable measurement. Post hoc analysis was conducted using multiple linear regression, which revealed predictive qualities of belief in mission and values (p \u3c .05) and employee support (p \u3c .05) on intent to stay of clinical care managers in the inpatient and outpatient settings

Characterizing Kepler Objects of Interest Using the Algorithm EXONEST

Recent observations across the galaxy have led to the conclusion that there exist many different extrasolar systems. Using photometric effects, the amount of data that has been produced on exoplanets has significantly increased and will continue to rise. Finding new methods of data analysis to broaden the spectrum of research has therefore become a necessity. Exonest is an algorithm currently in development that uses Bayesian methods, and notably nested sampling, to infer characteristics about an exoplanet from its observed light curve. In this paper, Exonest was tested by being used to study three extra-solar systems: each containing a single confirmed hot Jupiter (a large planet orbiting close to its host-star). The planets selected for this test were Kepler-428b, Kepler-40b, and Kepler-44b. Three parameters were computed and compared with the published values by NASA: the mass, the radius and the relative orbital inclination of the planet. The values returned by the algorithm are generally in agreement with NASA, which would tend to validate Exonest as a robust and powerful analysis tool. In the case of Kepler-428b, the dayside and nightside temperatures were also determined, although no other estimation of these parameters was available for comparison

EXONEST: The Bayesian Exoplanetary Explorer

The fields of astronomy and astrophysics are currently engaged in an unprecedented era of discovery as recent missions have revealed thousands of exoplanets orbiting other stars. While the Kepler Space Telescope mission has enabled most of these exoplanets to be detected by identifying transiting events, exoplanets often exhibit additional photometric effects that can be used to improve the characterization of exoplanets. The EXONEST Exoplanetary Explorer is a Bayesian exoplanet inference engine based on nested sampling and originally designed to analyze archived Kepler Space Telescope and CoRoT (Convection Rotation et Transits plan\'etaires) exoplanet mission data. We discuss the EXONEST software package and describe how it accommodates plug-and-play models of exoplanet-associated photometric effects for the purpose of exoplanet detection, characterization and scientific hypothesis testing. The current suite of models allows for both circular and eccentric orbits in conjunction with photometric effects, such as the primary transit and secondary eclipse, reflected light, thermal emissions, ellipsoidal variations, Doppler beaming and superrotation. We discuss our new efforts to expand the capabilities of the software to include more subtle photometric effects involving reflected and refracted light. We discuss the EXONEST inference engine design and introduce our plans to port the current MATLAB-based EXONEST software package over to the next generation Exoplanetary Explorer, which will be a Python-based open source project with the capability to employ third-party plug-and-play models of exoplanet-related photometric effects.Comment: 30 pages, 8 figures, 5 tables. Presented at the 37th International Workshop on Bayesian Inference and Maximum Entropy Methods in Science and Engineering (MaxEnt 2017) in Jarinu/SP Brasi

Means of Introducing an Analyte into Liquid Sampling Atmospheric Pressure Glow Discharge

A liquid sampling, atmospheric pressure, glow discharge (LS-APGD) device as well as systems that incorporate the device and methods for using the device and systems are described. The LS-APGD includes a hollow capillary for delivering an electrolyte solution to a glow discharge space. The device also includes a counter electrode in the form of a second hollow capillary that can deliver the analyte into the glow discharge space. A voltage across the electrolyte solution and the counter electrode creates the microplasma within the glow discharge space that interacts with the analyte to move it to a higher energy state (vaporization, excitation, and/or ionization of the analyte)

The 3D OrbiSIMS—label-free metabolic imaging with subcellular lateral resolution and high mass-resolving power

We report the development of a 3D OrbiSIMS instrument for label-free biomedical imaging. It combines the high spatial resolution of secondary ion mass spectrometry (SIMS; under 200 nm for inorganic species and under 2 μm for biomolecules) with the high mass-resolving power of an Orbitrap (>240,000 at m/z 200). This allows exogenous and endogenous metabolites to be visualized in 3D with subcellular resolution. We imaged the distribution of neurotransmitters—gamma-aminobutyric acid, dopamine and serotonin—with high spectroscopic confidence in the mouse hippocampus. We also putatively annotated and mapped the subcellular localization of 29 sulfoglycosphingolipids and 45 glycerophospholipids, and we confirmed lipid identities with tandem mass spectrometry. We demonstrated single-cell metabolomic profiling using rat alveolar macrophage cells incubated with different concentrations of the drug amiodarone, and we observed that the upregulation of phospholipid species and cholesterol is correlated with the accumulation of amiodarone

Predictors of Intent to Stay of Clinical Care Managers in the Inpatient and Outpatient Settings: A Causal-Comparative Study

This quantitative research study was conducted to determine if employee engagement, belief in mission and values, and employee support predict intent to stay of clinical care managers in the inpatient and outpatient settings. The present research study also examined the construct validity of the survey instrument, which was utilized by the subject organization in 2018. Through exploratory factor analysis (EFA), the constructs of employee engagement, belief in mission and values, and employee support were analyzed. Secondary data was collected from the 2018 completed surveys of 160 licensed nurses and social workers considered as clinical care managers from a large health care system in the western United States. The results of the EFA supported the validity of the constructs of belief in mission and values that explained 53.6% of the variability with a .912 reliability coefficient and employee support explained 8.96% of the variability with a .828 reliability coefficient. Employee engagement was deemed invalid due to cross-loadings of the factors with employee support and belief in mission and values. Binary regression analysis did not reveal statistically significant findings due to small sample size and lack of variability of the dichotomous outcome variable measurement. Post hoc analysis was conducted using multiple linear regression, which revealed predictive qualities of belief in mission and values (p \u3c .05) and employee support (p \u3c .05) on intent to stay of clinical care managers in the inpatient and outpatient settings