Using Repeated Oral Assisted Reading To Improve The Fluency Of Students With Learning Disabilities

This study explores the research question: How much does repeated reading improve the reading rate, accuracy, and prosody of students with learning disabilities in fifth grade? Three key topics: learning disabilities, fluency, and repeated reading are discussed. The author was greatly influenced by Timothy Rasinski and his work in the area of fluency. Repeated Oral Assisted Reading was administered to monitor and document accuracy, rate, and prosody utilizing both daily instructional-level reading probes and monthly grade-level reading probes as well as prosodic reading rubrics and a daily journal. The improvement in rate was compared to scores on nationally normed fluency assessments. Findings suggest that Repeated Oral Assisted Reading is an effective intervention for improving both reading rate and prosody in students with learning disabilities

Using Core Competencies to Help Staff Remain Proficient

Problem: Implement a competency program that engages staff compared to usual computer-based training modules. Objective: Design a multi-modal competency program for bedside nursing staff.https://digitalcommons.centracare.com/nursing_posters/1053/thumbnail.jp

Shifting the Paradigm of Classroom Nursing Orientation

Purpose Statement: The purpose of the Evidence-Based Practice Nursing Orientation Project is to find evidence to support a change in teaching strategy for newly hired RNs (both New Grads as well as experienced nurses), from the current process in order to improve outcomes of clinical competence, independence and nurse confidence, and retention.https://digitalcommons.centracare.com/nursing_posters/1050/thumbnail.jp

Nursing Bed Side Report: Changing our Relationship with our Patients

To improve bed side shift reporting in order to standardize practices, to increase efficiency, to decrease RN late clock outs, and to increase patient satisfaction.https://digitalcommons.centracare.com/nursing_posters/1020/thumbnail.jp

Improving land change detection based on uncertain survey maps using fuzzy sets

In this paper we present a method for correcting inherent classification bias in historical survey maps with which subsequent land cover change analysis can be improved. We linked generalized linear modelling techniques for spatial uncertainty prediction to fuzzy set based operations. The predicted uncertainty information was used to compute fuzzy memberships of forest and non-forest classes at each location. These memberships were used to reclassify the original map based on decision rules, which take into consideration the differences in identification likelihood during the historical mapping. Since the forest area was underestimated in the original mapping, the process allows to correct this bias by favouring forest, especially where uncertainty was high. The analyses were performed in a cross-wise manner between two study areas in order to examine whether the bias correction algorithm would still hold in an independent test area. Our approach resulted in a significant improvement of the original map as indicated by an increase of the Normalized Mutual Information from 0.26 and 0.36 to 0.38 and 0.45 for the cross-wise test against reference maps in Pontresina and St. Moritz, respectively. Consequently subsequent land cover change assessments could be considerably improved by reducing the deviations from a reference change by almost 50 percent. We concluded that the use of logistic regression techniques for uncertainty modelling based on topographic gradients and fuzzy set operations are useful tools for predictively reducing uncertainty in maps and land cover change models. The procedure allows to get more reliable area estimates of crisp classes and it improves the computation of the fuzzy areas of classes. The approach has limitations when the original map shows high initial accurac

Salmonella serology - which samples should be used: comparison of meatjuice and serum samples of the same pigs

Contamination of pork with Salmonella typhimurium is a potential source for fatal food born Salmonella-infections in humans. Screening programs are used in a number of countries to categorize pig farms into 3-4 Salmonella-risk-categories. A similar program will soon be implemented by the German government as well

Creating Successful Mentoring Relationships

Literature suggests that a formal mentoring program increases nursing engagement and retention. Objective: Develop a nurse mentor program to enhance new nurse satisfaction and retention.https://digitalcommons.centracare.com/nursing_posters/1034/thumbnail.jp

Successful Mentoring Relationships

A poster presentation on the structure, process, and outcomes of a successful mentoring program.https://digitalcommons.centracare.com/nursing_posters/1032/thumbnail.jp

Estimating a largest eigenvector by polynomial algorithms with a random start

In 7 and 8 the power and Lanczos algorithms with random start for estimating the largest eigenvalue of an n x n large symmetric positive definite matrix were analyzed. In this paper we continue this study by estimating an eigenvector corresponding to the largest eigenvalue. We analyze polynomial algorithms using Krylov information for two error criteria the randomized error and the randomized residual error

A framework for scale-sensitive, spatially explicit accuracy assessment of binary built-up surface layers

To better understand the dynamics of human settlements, thorough knowledge of the uncertainty in geospatial built-up surface datasets is critical. While frameworks for localized accuracy assessments of categorical gridded data have been proposed to account for the spatial non-stationarity of classification accuracy, such approaches have not been applied to (binary) built-up land data. Such data differs from other data such as land cover data, due to considerable variations of built-up surface density across the rural-urban continuum resulting in switches of class imbalance, causing sparsely populated confusion matrices based on small underlying sample sizes. In this paper, we aim to fill this gap by testing common agreement measures for their suitability and plausibility to measure the localized accuracy of built-up surface data. We examine the sensitivity of localized accuracy to the assessment support, as well as to the unit of analysis, and analyze the relationships between local accuracy and density / structure-related properties of built-up areas, across rural-urban trajectories and over time. Our experiments are based on the multi-temporal Global Human Settlement Layer (GHSL) and a reference database for the state of Massachusetts (USA). We find strong variation of suitability among commonly used agreement measures, and varying levels of sensitivity to the assessment support. We then apply our framework to assess localized GHSL data accuracy over time from 1975 to 2014. Besides increasing accuracy along the rural-urban gradient, we find that accuracy generally increases over time, mainly driven by peri-urban densification processes in our study area. Moreover, we find that localized densification measures derived from the GHSL tend to overestimate peri-urban densification processes that occurred between 1975 and 2014, due to higher levels of omission errors in the GHSL epoch 1975.Comment: 28 pages, 17 figure