A qualitative analysis of the lived experience of nursing staff developers in the process of changing to a new educational model

The purpose of this study was to document and critically analyze the lived experience of selected nursing staff developers in the process of moving toward a new model for hospital nursing education. Eleven respondents were drawn from a nation-wide population of about two hundred individuals involved in nursing staff development. These subjects were responsible for the implementation of the Performance Based Development System (PBDS) in their institutions.^ A purposive, criterion-based sampling technique was used with respondents being selected according to size of hospital, primary responsibility for orchestration of the change, influence over budgetary factors and managerial responsibility for PBDS. Data were gathered by the researcher through both in-person and telephone interviews. A semi-structured interview guide, designed by the researcher was used, and respondents were encouraged to amplify on their recollections as desired. Audiotapes were transcribed and resulting computer files were analyzed using the program "Martin". Answers to interview questions were compiled and reported across cases. The data was then reviewed a second time and interpreted for emerging themes and patterns.^ Two types of verification were used in the study. Internal verification was done through interview transcript review and feedback by respondents. External verification was done through review and feedback on data analysis by readers who were experienced in management of staff development departments.^ All respondents were female, so Gilligan's concept of the "ethic of care" was examined as a decision making strategy. Three levels of caring which influenced decision making were found. They were caring: (a) for the organization, (b) for the employee, and (c) for the patient. The four existentials of the lived experience, relationality, corporeality, temporality and spatiality were also examined to reveal the everydayness of making change. ^

An examination of attitude toward change as a predictor of policy/program implementation

Policy/program implementation, e.g., the process of fulfilling policy/program directives, is fundamentally tied to change. Implementation studies have examined the process, identifying many critical organizational variables although individuals perform the activities.^ Many of the studies are predicated on the rational, goal oriented model of organizations and examine implementation, presenting only the goal-oriented view. Organizational change and its resistance, however, are not fully explained by the rational model of organizations. There are other schools of thought providing different views of organizations from which explanation may emerge. Bolman and Deal (1984, 1991a, 1994) provide a different perspective for examining organizations Bolman and Deal argue organizations should be viewed through four different frames or lenses. Framing and reframing organizational action captures the complexity of action and provides better understanding of organizational processes. Understanding of implementation of policies/programs also will benefit from the use of the four-frame approach.^ The goal of this research is to provide a better understanding of the implementation process by examining individual attitudes toward change, the dependent variable of this research, and studying the relationship between the dependent variable and frame. The research was conducted in two phases. In Phase One, a survey was sent to 306 school administrators and teachers in magnet programs in Dade County, Florida. The survey instrument was composed of 55 questions including six from Bolman and Deal's Leadership Orientation Survey (1988) and 38 questions about organizational change. In Phase Two, more in-depth analysis of four school was conducted, to further explore the relationship between frame and attitude toward change.^ The results revealed that frame was a factor in explaining differences in personal Attitude Toward Change and Comfort Level with Change. Individuals using the symbolic frame had more positive attitudes toward change and were also more comfortable with change. The results of Phase Two of the research partially supported this finding in that the most fully implemented program was the product of an administrator who had chosen the symbolic frame. ^

A qualitative case study of teacher perceptions and concerns during the implementation of literacy folders in a select elementary school in Broward County, Florida

Classroom teachers are often required to implement new procedures or practices in response to local or federal education mandates. Attempts to implement innovations, often do not take into account the personal side of change; the perceptions, concerns and needs of those required to implement the innovation. One innovation that was required by the School Board of Broward County, Florida for all elementary classroom teachers was the implementation of Literacy Folders. ^ This study attempted to address the personal side of change by identifying teacher concerns during the implementation of Literacy Folders in a select elementary school in Broward County Florida. The Concerns Based Adoption model (CBAM) for change was used as the conceptual framework for this qualitative case study. ^ Sources of data for this study included participant interviews, observations and analysis of documents. Informal conversations with the participants and unscheduled classroom visits were also sources of data. Seven classroom teachers were interviewed using a predesigned interview guide developed based on the CBAM of change, specifically the Stages of Concern Dimension. Participant responses were coded into two categories, (a) recollections of past perceptions, and (b) present perceptions regarding the innovation. ^ Data analysis resulted in the emergence of one major theme and two subordinate themes. The themes were related to time and purpose of the innovation. The researcher also discovered that the participants exhibited responses typically representative of the CBAM for individuals who are in the process of adjusting to a new innovation. ^ Recommendations based on participant concerns are made for improving the implementation of the innovation. Recommendations for alternatives to the innovation and suggestions regarding areas for further research in the field of educational change are also made. ^

A case study of the Ellison model's use of mentoring as an approach toward inclusive community building

The Ellison Executive Mentoring Inclusive Community Building (ICB) Model is a paradigm for initiating and implementing projects utilizing executives and professionals from a variety of fields and industries, university students, and pre-college students. The model emphasizes adherence to ethical values and promotes inclusiveness in community development. It is a hierarchical model in which actors in each succeeding level of operation serve as mentors to the next. Through a three-step process—content, process, and product—participants must be trained with this mentoring and apprenticeship paradigm in conflict resolution, and they receive sensitivity and diversity training through an interactive and dramatic exposition. ^ The content phase introduces participants to the model's philosophy, ethics, values and methods of operation. The process used to teach and reinforce its precepts is the mentoring and apprenticeship activities and projects in which the participants engage and whose end product demonstrates their knowledge and understanding of the model's concepts. This study sought to ascertain from the participants' perspectives whether the model's mentoring approach is an effective means of fostering inclusiveness, based upon their own experiences in using it. The research utilized a qualitative approach and included data from field observations, individual and group interviews, and written accounts of participants' attitudes. ^ Participants complete ICB projects utilizing The Ellison Model as a method of development and implementation. They generally perceive that the model is a viable tool for dealing with diversity issues whether at work, at school, or at home. The projects are also instructional in that whether participants are mentored or serve as apprentices, they gain useful skills and knowledge about their careers. Since the model is relatively new, there is ample room for research in a variety of areas including organizational studies to determine its effectiveness in combating problems related to various kinds of discrimination. ^

An indexing structure and application model for vehicles moving on road networks

Moving objects database systems are the most challenging sub-category among Spatio-Temporal database systems. A database system that updates in real-time the location information of GPS-equipped moving vehicles has to meet even stricter requirements. Currently existing data storage models and indexing mechanisms work well only when the number of moving objects in the system is relatively small. This dissertation research aimed at the real-time tracking and history retrieval of massive numbers of vehicles moving on road networks. A total solution has been provided for the real-time update of the vehicles' location and motion information, range queries on current and history data, and prediction of vehicles' movement in the near future. ^ To achieve these goals, a new approach called Segmented Time Associated to Partitioned Space (STAPS) was first proposed in this dissertation for building and manipulating the indexing structures for moving objects databases. ^ Applying the STAPS approach, an indexing structure of associating a time interval tree to each road segment was developed for real-time database systems of vehicles moving on road networks. The indexing structure uses affordable storage to support real-time data updates and efficient query processing. The data update and query processing performance it provides is consistent without restrictions such as a time window or assuming linear moving trajectories. ^ An application system design based on distributed system architecture with centralized organization was developed to maximally support the proposed data and indexing structures. The suggested system architecture is highly scalable and flexible. Finally, based on a real-world application model of vehicles moving in region-wide, main issues on the implementation of such a system were addressed. ^

A static traffic assignment model combined with an artificial neural network delay model

As traffic congestion continues to worsen in large urban areas, solutions are urgently sought. However, transportation planning models, which estimate traffic volumes on transportation network links, are often unable to realistically consider travel time delays at intersections. Introducing signal controls in models often result in significant and unstable changes in network attributes, which, in turn, leads to instability of models. Ignoring the effect of delays at intersections makes the model output inaccurate and unable to predict travel time. To represent traffic conditions in a network more accurately, planning models should be capable of arriving at a network solution based on travel costs that are consistent with the intersection delays due to signal controls. This research attempts to achieve this goal by optimizing signal controls and estimating intersection delays accordingly, which are then used in traffic assignment. Simultaneous optimization of traffic routing and signal controls has not been accomplished in real-world applications of traffic assignment. To this end, a delay model dealing with five major types of intersections has been developed using artificial neural networks (ANNs). An ANN architecture consists of interconnecting artificial neurons. The architecture may either be used to gain an understanding of biological neural networks, or for solving artificial intelligence problems without necessarily creating a model of a real biological system. The ANN delay model has been trained using extensive simulations based on TRANSYT-7F signal optimizations. The delay estimates by the ANN delay model have percentage root-mean-squared errors (%RMSE) that are less than 25.6%, which is satisfactory for planning purposes. Larger prediction errors are typically associated with severely oversaturated conditions. A combined system has also been developed that includes the artificial neural network (ANN) delay estimating model and a user-equilibrium (UE) traffic assignment model. The combined system employs the Frank-Wolfe method to achieve a convergent solution. Because the ANN delay model provides no derivatives of the delay function, a Mesh Adaptive Direct Search (MADS) method is applied to assist in and expedite the iterative process of the Frank-Wolfe method. The performance of the combined system confirms that the convergence of the solution is achieved, although the global optimum may not be guaranteed.

Evaluating Effects of Everglades Restoration on American Crocodile Populations in South Florida Using a Spatially-Explicit, Stage-Based Population Model

The distribution and abundance of the American crocodile (Crocodylus acutus) in the Florida Everglades is dependent on the timing, amount, and location of freshwater flow. One of the goals of the Comprehensive Everglades Restoration Plan (CERP) is to restore historic freshwater flows to American crocodile habitat throughout the Everglades. To predict the impacts on the crocodile population from planned restoration activities, we created a stage-based spatially explicit crocodile population model that incorporated regional hydrology models and American crocodile research and monitoring data. Growth and survival were influenced by salinity, water depth, and density-dependent interactions. A stage-structured spatial model was used with discrete spatial convolution to direct crocodiles toward attractive sources where conditions were favorable. The model predicted that CERP would have both positive and negative impacts on American crocodile growth, survival, and distribution. Overall, crocodile populations across south Florida were predicted to decrease approximately 3 % with the implementation of CERP compared to future conditions without restoration, but local increases up to 30 % occurred in the Joe Bay area near Taylor Slough, and local decreases up to 30 % occurred in the vicinity of Buttonwood Canal due to changes in salinity and freshwater flows.

Advanced methodologies in dynamic traffic assignment modeling of managed lanes

Managed lane strategies are innovative road operation schemes for addressing congestion problems. These strategies operate a lane (lanes) adjacent to a freeway that provides congestion-free trips to eligible users, such as transit or toll-payers. To ensure the successful implementation of managed lanes, the demand on these lanes need to be accurately estimated. Among different approaches for predicting this demand, the four-step demand forecasting process is most common. Managed lane demand is usually estimated at the assignment step. Therefore, the key to reliably estimating the demand is the utilization of effective assignment modeling processes. ^ Managed lanes are particularly effective when the road is functioning at near-capacity. Therefore, capturing variations in demand and network attributes and performance is crucial for their modeling, monitoring and operation. As a result, traditional modeling approaches, such as those used in static traffic assignment of demand forecasting models, fail to correctly predict the managed lane demand and the associated system performance. The present study demonstrates the power of the more advanced modeling approach of dynamic traffic assignment (DTA), as well as the shortcomings of conventional approaches, when used to model managed lanes in congested environments. In addition, the study develops processes to support an effective utilization of DTA to model managed lane operations. ^ Static and dynamic traffic assignments consist of demand, network, and route choice model components that need to be calibrated. These components interact with each other, and an iterative method for calibrating them is needed. In this study, an effective standalone framework that combines static demand estimation and dynamic traffic assignment has been developed to replicate real-world traffic conditions. ^ With advances in traffic surveillance technologies collecting, archiving, and analyzing traffic data is becoming more accessible and affordable. The present study shows how data from multiple sources can be integrated, validated, and best used in different stages of modeling and calibration of managed lanes. Extensive and careful processing of demand, traffic, and toll data, as well as proper definition of performance measures, result in a calibrated and stable model, which closely replicates real-world congestion patterns, and can reasonably respond to perturbations in network and demand properties.^

On the possible implementation of nondirective teaching for English class in post secondary public schools in China

The paper explores how Chinese English teachers assume appropriate roles in applying nondirective teaching model to classrooms. After reviewing the current situation of English teaching and learning in China, it introduces the nondirective teaching model and its characteristics. Then, it focuses on the implementation of nondirective teaching model at the public schools in China. Finally it discusses the essential role that nondirective teaching model plays in helping students become powerful learners on English learning.

Vocabulary learning through use of the picture-word inductive model for young English learners in China: A mixed methods examination using Cognitive Load Theory

English has been taught as a core and compulsory subject in China for decades. Recently, the demand for English in China has increased dramatically. China now has the world's largest English-learning population. The traditional English-teaching method cannot continue to be the only approach because it merely focuses on reading, grammar and translation, which cannot meet English learners and users' needs (i.e., communicative competence and skills in speaking and writing). ^ This study was conducted to investigate if the Picture-Word Inductive Model (PWIM), a new pedagogical method using pictures and inductive thinking, would benefit English learners in China in terms of potential higher output in speaking and writing. With the gauge of Cognitive Load Theory (CLT), specifically, its redundancy effect, I investigated whether processing words and a picture concurrently would present a cognitive overload for English learners in China. ^ I conducted a mixed methods research study. A quasi-experiment (pretest, intervention for seven weeks, and posttest) was conducted using 234 students in four groups in Lianyungang, China (58 fourth graders and 57 seventh graders as an experimental group with PWIM and 59 fourth graders and 60 seventh graders as a control group with the traditional method). No significant difference in the effects of PWIM was found on vocabulary acquisition based on grade levels. Observations, questionnaires with open-ended questions, and interviews were deployed to answer the three remaining research questions. A few students felt cognitively overloaded when they encountered too many writing samples, too many new words at one time, repeated words, mismatches between words and pictures, and so on. Many students listed and exemplified numerous strengths of PWIM, but a few mentioned weaknesses of PWIM. The students expressed the idea that PWIM had a positive effect on their English teaching. ^ As integrated inferences, qualitative findings were used to explain the quantitative results that there were no significant differences of the effects of the PWIM between the experimental and control groups in both grade levels, from four contextual aspects: time constraints on PWIM implementation, teachers' resistance, how to use PWIM and PWIM implemented in a classroom over 55 students.^

A Static Traffic Assignment Model Combined with an Artificial Neural Network Delay Model

As traffic congestion continues to worsen in large urban areas, solutions are urgently sought. However, transportation planning models, which estimate traffic volumes on transportation network links, are often unable to realistically consider travel time delays at intersections. Introducing signal controls in models often result in significant and unstable changes in network attributes, which, in turn, leads to instability of models. Ignoring the effect of delays at intersections makes the model output inaccurate and unable to predict travel time. To represent traffic conditions in a network more accurately, planning models should be capable of arriving at a network solution based on travel costs that are consistent with the intersection delays due to signal controls. This research attempts to achieve this goal by optimizing signal controls and estimating intersection delays accordingly, which are then used in traffic assignment. Simultaneous optimization of traffic routing and signal controls has not been accomplished in real-world applications of traffic assignment. To this end, a delay model dealing with five major types of intersections has been developed using artificial neural networks (ANNs). An ANN architecture consists of interconnecting artificial neurons. The architecture may either be used to gain an understanding of biological neural networks, or for solving artificial intelligence problems without necessarily creating a model of a real biological system. The ANN delay model has been trained using extensive simulations based on TRANSYT-7F signal optimizations. The delay estimates by the ANN delay model have percentage root-mean-squared errors (%RMSE) that are less than 25.6%, which is satisfactory for planning purposes. Larger prediction errors are typically associated with severely oversaturated conditions. A combined system has also been developed that includes the artificial neural network (ANN) delay estimating model and a user-equilibrium (UE) traffic assignment model. The combined system employs the Frank-Wolfe method to achieve a convergent solution. Because the ANN delay model provides no derivatives of the delay function, a Mesh Adaptive Direct Search (MADS) method is applied to assist in and expedite the iterative process of the Frank-Wolfe method. The performance of the combined system confirms that the convergence of the solution is achieved, although the global optimum may not be guaranteed.

A Case Study of the Ellison Model's Use of Mentoring as an Approach Toward Inclusive Community Building

The Ellison Executive Mentoring Inclusive Community Building (ICB) Model is a paradigm for initiating and implementing projects utilizing executives and professionals from a variety of fields and industries, university students, and pre-college students. The model emphasizes adherence to ethical values and promotes inclusiveness in community development. It is a hierarchical model in which actors in each succeeding level of operation serve as mentors to the next. Through a three-step process--content, process, and product--participants must be trained with this mentoring and apprenticeship paradigm in conflict resolution, and they receive sensitivitiy and diversity training, through an interactive and dramatic exposition. The content phase introduces participants to the model's philosophy, ethics, values and methods of operation. The process used to teach and reinforce its precepts is the mentoring and apprenticeship activities and projects in which the participants engage and whose end product demontrates their knowledge and understanding of the model's concepts. This study sought to ascertain from the participants' perspectives whether the model's mentoring approach is an effective means of fostering inclusiveness, based upon their own experiences in using it. The research utilized a qualitative approach and included data from field observations, individual and group interviews, and written accounts of participants' attitudes. Participants complete ICB projects utilizing the Ellison Model as a method of development and implementation. They generally perceive that the model is a viable tool for dealing with diversity issues whether at work, at school, or at home. The projects are also instructional in that whether participants are mentored or seve as apprentices, they gain useful skills and knowledge about their careers. Since the model is relatively new, there is ample room for research in a variety of areas including organizational studies to dertmine its effectiveness in combating problems related to various kinds of discrimination.

An Indexing Structure and Application Model for Vehicles Moving on Road Networks

Moving objects database systems are the most challenging sub-category among Spatio-Temporal database systems. A database system that updates in real-time the location information of GPS-equipped moving vehicles has to meet even stricter requirements. Currently existing data storage models and indexing mechanisms work well only when the number of moving objects in the system is relatively small. This dissertation research aimed at the real-time tracking and history retrieval of massive numbers of vehicles moving on road networks. A total solution has been provided for the real-time update of the vehicles’ location and motion information, range queries on current and history data, and prediction of vehicles’ movement in the near future. To achieve these goals, a new approach called Segmented Time Associated to Partitioned Space (STAPS) was first proposed in this dissertation for building and manipulating the indexing structures for moving objects databases. Applying the STAPS approach, an indexing structure of associating a time interval tree to each road segment was developed for real-time database systems of vehicles moving on road networks. The indexing structure uses affordable storage to support real-time data updates and efficient query processing. The data update and query processing performance it provides is consistent without restrictions such as a time window or assuming linear moving trajectories. An application system design based on distributed system architecture with centralized organization was developed to maximally support the proposed data and indexing structures. The suggested system architecture is highly scalable and flexible. Finally, based on a real-world application model of vehicles moving in region-wide, main issues on the implementation of such a system were addressed.

Organic matter deposition/resuspension in a one-dimensional physical-biogeochemical model. A modelling study

The shallow water configuration of the gulf of Trieste allows the propagation of the stress due to wind and waves along the whole water column down to the bottom. When the stress overcomes a particular threshold it produces resuspension processes of the benthic detritus. The benthic sediments in the North Adriatic are rich of organic matter, transported here by many rivers. This biological active particulate, when remaining in the water, can be transported in all the Adriatic basin by the basin-wide circulation. In this work is presented a first implementation of a resuspension/deposition submodel in the oceanographic coupled physical-biogeochemical 1-dimensional numerical model POM-BFM. At first has been considered the only climatological wind stress forcing, next has been introduced, on the surface, an annual cycle of wave motion and finally have been imposed some exceptional wave event in different periods of the year. The results show a strong relationship between the efficiency of the resuspension process and the stratification of the water column. During summer the strong stratification can contained a great quantity of suspended matter near to the bottom, while during winter even a low concentration of particulate can reach the surface and remains into the water for several months without settling and influencing the biogeochemical system. Looking at the biologic effects, the organic particulate, injected in the water column, allow a sudden growth of the pelagic bacteria which competes with the phytoplankton for nutrients strongly inhibiting its growth. This happen especially during summer when the suspended benthic detritus concentration is greater.

Iterative Phase Optimization of Elementary Quantum Error Correcting Codes

Performing experiments on small-scale quantum computers is certainly a challenging endeavor. Many parameters need to be optimized to achieve high-fidelity operations. This can be done efficiently for operations acting on single qubits, as errors can be fully characterized. For multiqubit operations, though, this is no longer the case, as in the most general case, analyzing the effect of the operation on the system requires a full state tomography for which resources scale exponentially with the system size. Furthermore, in recent experiments, additional electronic levels beyond the two-level system encoding the qubit have been used to enhance the capabilities of quantum-information processors, which additionally increases the number of parameters that need to be controlled. For the optimization of the experimental system for a given task (e.g., a quantum algorithm), one has to find a satisfactory error model and also efficient observables to estimate the parameters of the model. In this manuscript, we demonstrate a method to optimize the encoding procedure for a small quantum error correction code in the presence of unknown but constant phase shifts. The method, which we implement here on a small-scale linear ion-trap quantum computer, is readily applicable to other AMO platforms for quantum-information processing.