The Relationship Between Utilization of the Elsevier Online Remediation Tool and the HESI Exit Exam for Student Nurses Preparing for the NCLEX-RN

Nursing schools are operating at full capacity in order to address an impending shortage of registered nurses that may exceed 500,000 by the year 2025. This pressure on scarce resources elevates the importance of NCLEX-RN preparedness for nursing faculty, nursing students, and the public at large. Additionally, the ability to successfully prepare students to sit for the NCLEX-RN exam can affect the reputation of nursing programs throughout the United States. Nursing schools frequently utilize commercially prepared standardized exams to assess student readiness and identify students in need of remediation. The HESI E2 Exit Exam distributed by Elsevier is one such exam. Built into this exam is a student-centered online remediation tool that allows students to customize their study based on exam results. In response to low NCLEX-RN pass rates, a BSN program in the northeastern United States developed a remediation policy requiring students to complete a prescribed number of remediation hours based on their earned score. General systems theory was the framework that guided this analytical policy analysis. Once a policy is created as a result of a systematic assessment of a problem, it is necessary to evaluate the policy for effectiveness. This ex post facto analysis addresses a gap in the literature of high quality quantitative remediation policies that are reproducible throughout multiple programs. Using multiple regression this study explored the relationship between utilization of the Elsevier online remediation resource and scores on the HESI V2 Exit Exam for senior-level nursing students. Variables explored were GPA, HESI V1 scores, gender, cohort (traditional or second degree), semester (spring, summer, or fall), and hours of remediation. GPA significantly predicted 15% to 18% of the variance in scores on the HESI V2 exam. When additional variables are entered into the model, the predictive value of GPA was reduced to 3% to 9%. HESI Version 1 significantly predicted 3% to 18% of the variance in scores on the HESI V2 while controlling for GPA. Completion of online remediation hours did not significantly contribute to scores on the HESI V2 Exit Exam for senior-level nursing students in this northeastern BSN program

The Relationship Between Utilization of the Elsevier Online Remediation Tool and the HESI Exit Exam for Student Nurses Preparing for the NCLEX-RN

Nursing schools are operating at full capacity in order to address an impending shortage of registered nurses that may exceed 500,000 by the year 2025. This pressure on scarce resources elevates the importance of NCLEX-RN preparedness for nursing faculty, nursing students, and the public at large. Additionally, the ability to successfully prepare students to sit for the NCLEX-RN exam can affect the reputation of nursing programs throughout the United States. Nursing schools frequently utilize commercially prepared standardized exams to assess student readiness and identify students in need of remediation. The HESI E2 Exit Exam distributed by Elsevier is one such exam. Built into this exam is a student-centered online remediation tool that allows students to customize their study based on exam results. In response to low NCLEX-RN pass rates, a BSN program in the northeastern United States developed a remediation policy requiring students to complete a prescribed number of remediation hours based on their earned score. General systems theory was the framework that guided this analytical policy analysis. Once a policy is created as a result of a systematic assessment of a problem, it is necessary to evaluate the policy for effectiveness. This ex post facto analysis addresses a gap in the literature of high quality quantitative remediation policies that are reproducible throughout multiple programs. Using multiple regression this study explored the relationship between utilization of the Elsevier online remediation resource and scores on the HESI V2 Exit Exam for senior-level nursing students. Variables explored were GPA, HESI V1 scores, gender, cohort (traditional or second degree), semester (spring, summer, or fall), and hours of remediation. GPA significantly predicted 15% to 18% of the variance in scores on the HESI V2 exam. When additional variables are entered into the model, the predictive value of GPA was reduced to 3% to 9%. HESI Version 1 significantly predicted 3% to 18% of the variance in scores on the HESI V2 while controlling for GPA. Completion of online remediation hours did not significantly contribute to scores on the HESI V2 Exit Exam for senior-level nursing students in this northeastern BSN program

Working paper 25: Strategies for enhancing and restoring rare plants and their habitats in the face of climate change and habitat destruction in the intermountain west

Adopting Leopold's sage advice to "keep every cog and wheel," the International Union for Conservation of Nature and Natural Resources regards "the maintenance of existing genetic diversity and viable populations of all taxa in the wild in order to maintain biological interactions, ecological processes and function" (IUCN 2002, p. 1) as a fundamental conservation goal. Such an outlook is shared by many conservation-oriented organizations, including federal land management agencies in the United States. This Ecological Restoration Institute working paper will review various strategies land managers can use to maintain one segment of the plant world - rare plants - as we experience the current period of changing climate. Rare plants may be seen as the "seemingly useless parts," but they deserve attention. "Intelligent tinkering" through innovative biological conservation and ecological restoration strategies will be necessary to provide them with the kinds of habitat they will need for their continued survival and growth

Estimating the Location and Spatial Extent of a Covert Anthrax Release

Rapidly identifying the features of a covert release of an agent such as anthrax could help to inform the planning of public health mitigation strategies. Previous studies have sought to estimate the time and size of a bioterror attack based on the symptomatic onset dates of early cases. We extend the scope of these methods by proposing a method for characterizing the time, strength, and also the location of an aerosolized pathogen release. A back-calculation method is developed allowing the characterization of the release based on the data on the first few observed cases of the subsequent outbreak, meteorological data, population densities, and data on population travel patterns. We evaluate this method on small simulated anthrax outbreaks (about 25–35 cases) and show that it could date and localize a release after a few cases have been observed, although misspecifications of the spore dispersion model, or the within-host dynamics model, on which the method relies can bias the estimates. Our method could also provide an estimate of the outbreak's geographical extent and, as a consequence, could help to identify populations at risk and, therefore, requiring prophylactic treatment. Our analysis demonstrates that while estimates based on the first ten or 15 observed cases were more accurate and less sensitive to model misspecifications than those based on five cases, overall mortality is minimized by targeting prophylactic treatment early on the basis of estimates made using data on the first five cases. The method we propose could provide early estimates of the time, strength, and location of an aerosolized anthrax release and the geographical extent of the subsequent outbreak. In addition, estimates of release features could be used to parameterize more detailed models allowing the simulation of control strategies and intervention logistics

Prediction of chronic disability in work-related musculoskeletal disorders: a prospective, population-based study

BACKGROUND: Disability associated with work-related musculoskeletal disorders is an increasingly serious societal problem. Although most injured workers return quickly to work, a substantial number do not. The costs of chronic disability to the injured worker, his or her family, employers, and society are enormous. A means of accurate early identification of injured workers at risk for chronic disability could enable these individuals to be targeted for early intervention to promote return to work and normal functioning. The purpose of this study is to develop statistical models that accurately predict chronic work disability from data obtained from administrative databases and worker interviews soon after a work injury. Based on these models, we will develop a brief instrument that could be administered in medical or workers' compensation settings to screen injured workers for chronic disability risk. METHODS: This is a population-based, prospective study. The study population consists of workers who file claims for work-related back injuries or carpal tunnel syndrome (CTS) in Washington State. The Washington State Department of Labor and Industries claims database is reviewed weekly to identify workers with new claims for work-related back injuries and CTS, and these workers are telephoned and invited to participate. Workers who enroll complete a computer-assisted telephone interview at baseline and one year later. The baseline interview assesses sociodemographic, employment-related, biomedical/health care, legal, and psychosocial risk factors. The follow-up interview assesses pain, disability, and work status. The primary outcome is duration of work disability over the year after claim submission, as assessed by administrative data. Secondary outcomes include work disability status at one year, as assessed by both self-report and work disability compensation status (administrative records). A sample size of 1,800 workers with back injuries and 1,200 with CTS will provide adequate statistical power (0.96 for low back and 0.85 for CTS) to predict disability with an alpha of .05 (two-sided) and a hazard ratio of 1.2. Proportional hazards regression models will be constructed to determine the best combination of predictors of work disability duration at one year. Regression models will also be developed for the secondary outcomes