Nursing care mapping for patients at risk of falls in the Nursing Interventions Classification

Objective: Identifying the prescribed nursing care for hospitalized patients at risk of falls and comparing them with the interventions of the Nursing Interventions Classifications (NIC). Method: A cross-sectional study carried out in a university hospital in southern Brazil. It was a retrospective data collection in the nursing records system. The sample consisted of 174 adult patients admitted to medical and surgical units with the Nursing Diagnosis of Risk for falls. The prescribed care were compared with the NIC interventions by the cross-mapping method. Results: The most prevalent care were the following: keeping the bed rails, guiding patients/family regarding the risks and prevention of falls, keeping the bell within reach of patients, and maintaining patients’ belongings nearby, mapped in the interventions Environmental Management: safety and Fall Prevention. Conclusion: The treatment prescribed in clinical practice was corroborated by the NIC reference.


GIS Mapping: Creating Solutions for Rock Creek Lake, 2004

Rock Creek Lake is at risk. The centerpiece of Rock Creek State Park in Jasper County, Rock Creek Lake offers visitors a range of recreational opportunities, including the second busiest campground in Iowa and great fishing. However, many different factors are threatening the lake. Action is being taken to improve the lake for today’s visitors and to preserve the lake for future generations. GIS mapping is helping to make those improvements possible.

DNR Interactive Mapping: A tool for Iowa Agriculture, 2006

The planning tools you need to improve both your farming operation and Iowa’s streams and lakes are right at your fingertips. With DNR interactive mapping online, you can access a large amount of information for free and without special software.

Mapping Nursing language terms of Parkinson's disease

OBJECTIVE Implementing cross-mapping of Nursing language terms with the terminology of NANDA International, contained in records of patients with Parkinson's disease in rehabilitation. METHOD Descriptive study of cross mapping, carried out in three steps. A simple random sample of 67 files of patients who participated in the rehabilitation in the period between March 2009 and April 2013. RESULTS We identified 454 terms of Nursing language that resulted in 54 diagnoses after cross-mapping, present in 11 of the 13 taxonomy domains. The most mapped diagnosis was "Impaired urinary elimination" (59.7%), followed by "Urgent urinary incontinence" (55.2%), "Willingness to self-control improved health" (50.7%), "Constipation" (47.8%) and "Compromised physical mobility" (29.9%). Seven described terms were not mapped due to a corresponding defining characteristic being absent. CONCLUSION It was possible to determine the profile of patients, as well as the complexity of nursing care in the rehabilitation of patients with Parkinson's disease.

MP2RAGE, a self bias-field corrected sequence for improved segmentation and T1-mapping at high field.

The large spatial inhomogeneity in transmit B(1) field (B(1)(+)) observable in human MR images at high static magnetic fields (B(0)) severely impairs image quality. To overcome this effect in brain T(1)-weighted images, the MPRAGE sequence was modified to generate two different images at different inversion times, MP2RAGE. By combining the two images in a novel fashion, it was possible to create T(1)-weighted images where the result image was free of proton density contrast, T(2) contrast, reception bias field, and, to first order, transmit field inhomogeneity. MP2RAGE sequence parameters were optimized using Bloch equations to maximize contrast-to-noise ratio per unit of time between brain tissues and minimize the effect of B(1)(+) variations through space. Images of high anatomical quality and excellent brain tissue differentiation suitable for applications such as segmentation and voxel-based morphometry were obtained at 3 and 7 T. From such T(1)-weighted images, acquired within 12 min, high-resolution 3D T(1) maps were routinely calculated at 7 T with sub-millimeter voxel resolution (0.65-0.85 mm isotropic). T(1) maps were validated in phantom experiments. In humans, the T(1) values obtained at 7 T were 1.15+/-0.06 s for white matter (WM) and 1.92+/-0.16 s for grey matter (GM), in good agreement with literature values obtained at lower spatial resolution. At 3 T, where whole-brain acquisitions with 1 mm isotropic voxels were acquired in 8 min, the T(1) values obtained (0.81+/-0.03 s for WM and 1.35+/-0.05 for GM) were once again found to be in very good agreement with values in the literature.

Mapping of the genes coding for the two major vaccinia virus core polypeptides.

We have mapped the genes coding for two major structural polypeptides of the vaccinia virus core by hybrid selection and transcriptional mapping. First, RNA was selected by hybridization to restriction fragments of the vaccinia virus genome, translated in vitro and the products were immunoprecipitated with antibodies against the two polypeptides. This approach allowed us to map the genes to the left hand end of the largest Hind III restriction fragment of 50 kilobase pairs. Second, transcriptional mapping of this region of the genome revealed the presence of the two expected RNAs. Both RNAs are transcribed from the leftward reading strand and the 5'-ends of the genes are separated by about 7.5 kilobase pairs of DNA. Thus, two genes encoding structural polypeptides with a similar location in the vaccinia virus particle are clustered at approximately 105 kilobase pairs from the left hand end of the 180 kilobase pair vaccinia virus genome.

Beyond Smith's rule: An optimal dynamic index, rule for single machine stochastic scheduling with convex holding costs

Most research on single machine scheduling has assumedthe linearity of job holding costs, which is arguablynot appropriate in some applications. This motivates ourstudy of a model for scheduling $n$ classes of stochasticjobs on a single machine, with the objective of minimizingthe total expected holding cost (discounted or undiscounted). We allow general holding cost rates that are separable,nondecreasing and convex on the number of jobs in eachclass. We formulate the problem as a linear program overa certain greedoid polytope, and establish that it issolved optimally by a dynamic (priority) index rule,whichextends the classical Smith's rule (1956) for the linearcase. Unlike Smith's indices, defined for each class, ournew indices are defined for each extended class, consistingof a class and a number of jobs in that class, and yieldan optimal dynamic index rule: work at each time on a jobwhose current extended class has larger index. We furthershow that the indices possess a decomposition property,as they are computed separately for each class, andinterpret them in economic terms as marginal expected cost rate reductions per unit of expected processing time.We establish the results by deploying a methodology recentlyintroduced by us [J. Niño-Mora (1999). "Restless bandits,partial conservation laws, and indexability. "Forthcomingin Advances in Applied Probability Vol. 33 No. 1, 2001],based on the satisfaction by performance measures of partialconservation laws (PCL) (which extend the generalizedconservation laws of Bertsimas and Niño-Mora (1996)):PCL provide a polyhedral framework for establishing theoptimality of index policies with special structure inscheduling problems under admissible objectives, which weapply to the model of concern.

Mapping of Environmental Data Using Kernel-Based Methods

Recently, kernel-based Machine Learning methods have gained great popularity in many data analysis and data mining fields: pattern recognition, biocomputing, speech and vision, engineering, remote sensing etc. The paper describes the use of kernel methods to approach the processing of large datasets from environmental monitoring networks. Several typical problems of the environmental sciences and their solutions provided by kernel-based methods are considered: classification of categorical data (soil type classification), mapping of environmental and pollution continuous information (pollution of soil by radionuclides), mapping with auxiliary information (climatic data from Aral Sea region). The promising developments, such as automatic emergency hot spot detection and monitoring network optimization are discussed as well.

Improving Transition Outcomes: Resource Mapping Workshop: Additional Information

Information used/presented during the Improving Transition Outcomes Resource Mapping Workshops

Improving Transition Outcomes: Resource Mapping Workshop: Service Matrix

Resource Mapping tool from the Improving Transition Outcomes Resource Mapping Workshops

Improving Transition Outcomes: Resource Mapping Workshop: Resource List

Resource Mapping resources shared during the Improving Transition Outcomes Resource Mapping Workshops

Improving Transition Outcomes: Resource Mapping Workshop: Business Survey

Business survey tool from the Improving Transition Outcomes Resource Mapping Workshops