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Background: Health care workers are 4 times more likely to suffer violence than workers in other industries. Purpose: The aim was to examine types of patients' verbal/physical abuse against the nursing workforce observed through patient-engaged video surveillance (PEVS) and interventions initiated by monitor technicians. Methods: A descriptive study was conducted to analyze all types of patient-initiated abuse, physical and verbal, reported from 73 hospitals and patient response to PEVS. Results: Of 150 434 patients whom RNs enrolled into 24-hour PEVS, 5034 patients (3%) were identified by RNs as at risk for aggressive/violent behavior as their primary or secondary reason for PEVS enrollment, and 32 (0.60%) patients exhibited such behavior. A total of 221 patients demonstrated aggressive/violent behaviors, 32 (15%) were identified as at risk, and 189 (85%) were not. However, 5002 (99%; 5002/5034) of the patients identified as a risk for aggressive/violent behaviors did not exhibit these behaviors. Conclusions: Patient-engaged video surveillance is an effective method to track and trend patient aggression toward nursing staff, increasing patient and nursing workforce safety. Because 99% of the patients who exhibited aggressive/violent behavior were not identified by RNs as at risk, organizations should consider adding violence risk tools as part of patients' admission assessment.

Earth’s Radiation Budget (ERB) is an accounting of all incoming energy from the sun and outgoing energy reflected and radiated to space by earth’s surface and atmosphere. The National Aeronautics and Space Administration (NASA)/Global Energy and Water Cycle Experiment (GEWEX) Surface Radiation Budget (SRB) project produces and archives long-term datasets representative of this energy exchange system on a global scale. The data are comprised of the longwave and shortwave radiative components of the system and is algorithmically derived from satellite and atmospheric assimilation products, and acquired atmospheric data. It is stored as 3-hourly, daily, monthly/3-hourly, and monthly averages of 1°x1° grid cells. Input parameters used by the algorithms are a key source of variability in the resulting output data sets. Sensitivity studies have been conducted to estimate the effects this variability has on the output data sets using linear techniques. This entails varying one input parameter at a time while keeping all others constant or by increasing all input parameters by equal random percentages, in effect changing input values for every cell for every three hour period and for every day in each month. This equates to almost 11 million independent changes without ever taking into consideration the interactions or dependencies among the input parameters. A more comprehensive method is proposed here for the evaluating the shortwave algorithm to identify both the input parameters and parameter interactions that most significantly affect the output data. This research utilized designed experiments that systematically and simultaneously varied all of the input parameters of the shortwave algorithm. A D-Optimal design of experiments (DOE) was chosen to accommodate the 14 types of atmospheric properties computed by the algorithm and to reduce the number of trials required by a full factorial study from millions to 128. A modified version of the algorithm was made available for testing such that global calculations of the algorithm were tuned to accept information for a single temporal and spatial point and for one month of averaged data. The points were from each of four atmospherically distinct regions to include the Amazon Rainforest, Sahara Desert, Indian Ocean and Mt. Everest. The same design was used for all of the regions. Least squares multiple regression analysis of the results of the modified algorithm identified those parameters and parameter interactions that most significantly affected the output products. It was found that Cosine solar zenith angle was the strongest influence on the output data in all four regions. The interaction of Cosine Solar Zenith Angle and Cloud Fraction had the strongest influence on the output data in the Amazon, Sahara Desert and Mt. Everest Regions, while the interaction of Cloud Fraction and Cloudy Shortwave Radiance most significantly affected output data in the Indian Ocean region. Second order response models were built using the resulting regression coefficients. A Monte Carlo simulation of each model extended the probability distribution beyond the initial design trials to quantify variability in the modeled output data.