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- Автор : Fejin
- 17.10.2021
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Prof frank bettinger book
The best thing I have seen out there that folks can pick up and apply. Christa M. Caspi draws skillfully on the literature and his own research to provide care providers with practical advice to keep residents with dementia safe and free from psychological harm. Allen Power, M. Caspi brings compassion and clarity to the critical issue of harmful interactions between residents with dementia living in long term care homes.
His evidence-informed strategies provide practical value for families and care providers who struggle to address and prevent these deeply concerning events. His insights and tools to prevent harmful interactions between residents can support us as we shift dementia care from crisis to comfort. Senior care providers across a range of settings will find the information highly useful in understanding what contributes to problematic interactions and in determining what to do about them.
I am particularly impressed with the level of sensitivity and thoughtful care Dr. Caspi expresses toward persons with dementia; his strength-based, compassionate explanations of the dementia experience provide important advocacy for this population and can promote greater empathy in the behavioral assessment and intervention process. Contributing to keeping this phenomenon invisible is that the CMS database has yet to implement a GAO recommendation that such incidents be tracked and submitted by state survey agencies.
He cautions us that words are powerful and capable of perpetuating harmful beliefs about persons with dementia that will cause some direct care providers to accept negative interactions rather than try to de-escalate through listening, drawing on pearls from the past, and building trust. His 12 principles for approaching DHRRIs provides a framework for direct care providers to better understand the triggers of DHRRI and strategies to prevent or de-escalate harmful interactions.
Add this book to your toolkit and appreciate that in better management of negative interactions, positive interactions of human expression will emerge. Silverstein, Ph. Eilon Caspi, the author gets to the heart of the problem concerning aggressive behaviors among people with dementia. He offers a series of evidence-based solutions and stresses the importance of recognizing basic feelings of sadness, gladness, madness, and fearfulness every person is born with being honored and respected.
Training and experienced in dementia care myself for over two decades, I highly recommend this book to care homes and nurse training programs. The reality is that without proper knowledge, understanding, compassion, and empathy for this condition, more and more incidents leading to serious injuries, or death, will continue to occur.
Rather than normalizing these altercations, he presents 12 steps to preventing them—a must read for nursing home administrators, front line staff, and caring families. He uses the power of language to increase understanding of behaviors our society normally rejects. The application of risk-based approaches that are not only hazard-focused in decision-making processes regarding Ecosystem-based Disaster Risk Reduction Eco-DRR is increasing [ 11 ].
These studies were conducted at different spatial scales from local [ 12 , 13 , 14 , 15 ] to regional [ 16 , 17 ] as well as national evaluations [ 18 ]; see also chapters [ 19 , 20 , 21 ] of this book. Bebi et al. One reason could be that the remaining considerable uncertainties in assessing the protective effects of forests against natural hazards are affecting the confidence in FbS in contrast to technical measures e.
These uncertainties could be addressed, for example, with Bayesian probability theory and Bayesian Networks [ 25 ]. However, there is a lack of openly available and easy-to-use tools to apply Bayesian Networks as a DSS for an IRM in practice, which also includes the essential spatial and temporal dimensions to implement the most suitable combinations of risk mitigation measures [ 26 ]. Figure 2. Considering the differences in DSS for natural hazard risk management see Figure 1 , these tools were developed following a complementary approach that allows for robust and comprehensive risk analysis at different scales.
They are still addressing rather an expert audience than non-experts or the general public; however, an extended group of stakeholders and policymakers was involved in their development, evaluation, and testing within the GreenRisk4ALPs project.
The required input data are a digital elevation model DEM and a release raster containing one or several starting cells. Together with two developed custom extensions, post-processing routines, and recommendations for the visualization of simulation results see chapter [ 10 ] of this book , Flow-Py can be used as DSS to support protective forest and risk management-related decisions. To develop an easy-to-apply procedure we asked: Where does the presence of forest reduce the impact of GMFs on elements potentially at risks such as exposed buildings, transport, or recreational infrastructure?
The objectives to answer this question are: to map the forest areas that may reduce the natural hazard risk for people and assets, to model potential GMF runout and intensity reductions due to the presence of forest and to quantify forest effects in reducing risk by assessing the reduced impact each GMF has on different types of assets, and to identify and visualize areas where the risk-reducing effect of forests is greatest within a region.
The freely available and open-source Flow-Py code allows users to customize GMF simulations by adjusting the parameterization or developing extensions to adapt calculations, input data, and model outputs as well as to apply their own post-processing routines and visualizations based on their specific questions and problems.
Figure 3. Example for a direct object protective forest map based on Flow-Py simulations. Purple shaded areas were modeled as forests with a direct object protective function, which can protect people and assets from large and very large dry snow slab avalanches. Green shaded are forested areas that are not considered protective forests against snow avalanches. Figure 4. Yellow to red colors show the difference in the kinetic energy from simulations with and without the effect of the forest.
Figure 5. Example for a map delineating locations of potentially highly effective protective forests against snow avalanches based on Flow-Py simulations. Dark blue are forested locations that were identified as highly effective for reducing the kinetic energy by forests of large and very large dry snow slab avalanches.
Figure 6. Forest relevance in reducing the impact of snow avalanches is classified into three levels in the far north of the GreenRisk4ALPs Pilot Action Region PAR Wipptal South IT , and forest relevance levels are combined with the building and infrastructure priority classes. The bar chart indicates for the entire PAR the square meters of assets for which the forest has relevance in reducing the potential risk from snow avalanches.
Building and transport infrastructure footprints are shown with black outlines. Figure 7. Building hotspots are shown for three levels of forest relevance to reduce the risk of snow avalanches. This extension changes Flow-Py from a pure runout model to one that can identify terrain associated with endangering assets by storing the path that a GMF traveled to reach an infrastructure in computer memory see also [ 33 ].
To apply the back-tracking extension an input raster including the location of infrastructure in the modeling domain is therefore required. The simulation output is a spatially explicit subset of the GMF release areas, transit, and runout zones that were modeled to endanger infrastructure, which can be united with the spatial distribution of forested areas for details see chapter [ 10 ] of this book. The resulting map highlights the location of forests with a direct object protective function for definitions see chapter [ 3 ] of this book.
The map provides information at a regional scale about the approximate geographic extent of protective forests and how they are distributed in the landscape. Similar maps have been produced in Switzerland [ 34 , 35 ] and Austria [ 36 , 37 ]; however, such protective forest maps are not available for all Alpine Space countries. To account for the increase in energy dissipation or friction in the parts of a GMF path that are located in a forest, the forest extension adjusts the runout angle to a steeper angle dependent on the length of the forested slope, the forest structure and the kinetic energy of the GMF.
For this, an additional Forest Structure Index FSI input raster ranging between 0 and 1, which summarizes how developed a forest is regarding its optimal protective effect is needed. Figure 5 shows areas where forests are assumed to be highly effective in reducing GMF runout and intensity based on maximum thresholds in the kinetic energy of a GMF calculated with the Flow-Py simulation tool. To be considered as highly effective or having the potential to be , the location must lie between a release area of a GMF and elements potentially at risk.
The maximum kinetic energy threshold, which indicates where forests can reduce GMF runout and intensity considerably, is dependent on the GMF type and dictated by the different forest-GMF interactions. Other criteria used to identify such locations are the same for all GMF types since they characterize the forest growing conditions by simple terrain characteristics. The aim of the exposure assessment was to spatially identify those forest areas that have significant relevance in reducing the impact of GMFs on assets.
We considered buildings, transport, and recreational infrastructure as exposed assets and classified each asset type into high and low priority provided the required input data was available following the recommendations of Perzl et al. Perzl et al. However, the simplified scheme fits our purpose and goal to produce an overview of assets potentially at risk enabling to compare model outcomes of different PARs and countries that can be followed by a more detailed risk assessment.
Accordingly, buildings used for residential, commercial, and industrial purposes were categorized as high priority and all other buildings e. Regarding transport infrastructure, highways, and primary and secondary roads were assigned a high priority whereas tertiary roads, for example, roads within settlements were categorized as a lower priority; forest roads were excluded from the analysis.
Recreational infrastructure, such as cable cars, campgrounds, ski runs, golf courses, and sports grounds were considered assets of lower priority. The spatial data needed for the exposure assessment were available in different formats representing different levels of detail thematic and spatial for five PARs of the GreenRisk4ALPs project. In the first step, the required features, buildings, transport, and recreational infrastructure, were extracted from the original data sets.
All asset information was subsequently converted into m resolution gridded data sets. To spatially identify those areas where the forest has significant relevance in reducing the impact of GMFs, the IRI data sets that were computed based on Flow-Py simulation results for method and result description see subsection 2. Building and infrastructure classes combined with forest relevance levels were visualized on maps.
This spatial overlay allowed to quantify for the entire study area the square meters of each combination of forest relevance and asset priority level Figure 6. The forest relevance maps were used to identify hotspots where protective forests are particularly relevant for reducing risk Figure 7.
To define these hotspots of forest relevance, we considered those building types and infrastructure of a higher priority and combined them with the high level of forest relevance. Aggregating those features to larger pixel sizes allows to increase their visibility in a map showing a region at a scale of approximately , and for a qualitative consultation and discussion with local stakeholders. The aim of the tool is to present an assessment of the effectiveness of protective forest and ecosystem-based risk management compared to other solutions such as technical measures or avoidance strategies.
MT4 FOREX INDICATOR
Supposedly he was sickly for the remainder of his life because of this episode. In , Frank's brother Ward returned home from the west coast and had a number of ideas how to better the house. They tore it down and rebuilt it, during which time they slept in a small barn behind the house Source: Laurentine Mullin Bettinger Rogers.
Frank E. He was a farmer and lived in the homestead and farm that his grandfather had built. Frank married Helen Marley Johnson Bulen in she had been living with his family for five years and they had two children: Esther Marley Betty b and Roy b Frank died at the age of Davies, pastor of Sandy Creek Congregational church. Burial was at Ellisburg.
Bettinger was taken ill Tuesday and on Wednesday was taken to the House of the Good Samaritan, Watertown, where he died at that night, following a major operation. Bettinger was born in the Redden house at Ellisburg, son of George E. At the time of his death, he resided in the homestead which his grandfather built. He married Helen Marley Bulen 30 years ago.
Surviving are his wife; one daughter, Mrs. Bettinger was taken to the hospital Wednesday. He underwent an operation immediately after his arrival. He died without regaining consciousness. Two years later, in , he had worked his way up to the majors with St.
Louis, but his baseball career was cut short by an arm injury. During a moment of reflection, he remembered what his baseball manager told him about his lack of enthusiasm. So he made a commitment to himself to start acting enthusiastically in his insurance presentations. He also met a successful salesman and took his advice to read the Autobiography of Benjamin Franklin. A light went on in his mind when he realized that Franklin's Socratic method of asking "key" questions might work with selling policies.
He tried it, it worked, and Bettger began to perfect his technique with great enthusiasm. Bettger also gave a series of lectures to Jaycees organizations nationwide with Dale Carnegie. The book focused on his life and lessons he learned during his short baseball career, and reflected how those lessons learned at an early age helped mold him into the success he became.
Consequently, he and his wife outlived their assets. Members of the National Speakers Association contributed to help the couple meet their financial needs.
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