An Assessment of How Nurse Practitioners Create Access to Primary Care in Canadian Residential Long-Term Care Settings

The aim of this paper is to explore the role and activities of nurse practitioners (NPs) working in long-term care (LTC) to understand concepts of access to primary care for residents. Utilizing the "FIT" framework developed by Penchanksy and Thomas, we used a directed content analysis method to analyze data from a pan-Canadian study of NPs in LTC. Individual and focus group interviews were conducted at four sites in western, central and eastern regions of Canada with 143 participants, including NPs, RNs, regulated and unregulated nursing staff, allied health professionals, physicians, administrators and directors and residents and family members. Participants emphasized how the availability and accessibility of the NP had an impact on access to primary and urgent care for residents. Understanding more about how NPs affect access in Canadian LTC will be valuable for nursing practice and healthcare planning and policy and may assist other countries in planning for the introduction of NPs in LTC settings to increase access to primary care.

The influence that politicians, community leaders and the media have on confidence in the police in Northern Ireland

n January 2014, the Northern Ireland Policing Board (NIPB) commissioned the University of Ulster to conduct research into public confidence in policing to help inform the work of the Board and its oversight of police service delivery. More specifically, the research team were tasked with exploring ‘the influence that politicians, community leaders and the media have on public confidence in policing in Northern Ireland’. To date, the subject of ‘confidence in policing’ within a Northern Ireland context has been relatively under researched, both in academic and policy terms. Thus, the present research is the first empirical research to be produced in Northern Ireland which considers the issue of confidence in policing from the perspective of community leaders, politicians and the media – including the key influences and dynamics which underpin police confidence at a community level.

The report begins with a comprehensive review of academic literature, policy documents and contemporary events related to confidence in policing. The research then provides an overview of the methodology used to undertake the research, with the remainder of the report comprised of the findings from the discussions with representatives from the media, political parties and the community and voluntary sector who participated. The report concludes with an overview of the central findings along with a series of recommendations.

[Review of the Book The Challenge of Organizational Change: How Companies Experience It and Leaders Guide It]

[Excerpt] Overall, this book is an important contribution to the organizational studies literature. The Big-Three Model and the case studies supporting it provide new insights regarding the ways in which changes affect individuals and organizations. Further theoretical developments should build on this framework, and incorporate elements of this model into more detailed schemas of change. The authors have exposed many new research questions and provided many lessons for practitioners. I look forward to additional work from them.

Concrete Nurse Logs: Spawning Biodiversity from Ballard’s Century-Old Locks

Thesis (Master's)--University of Washington, 2016-08

A Video Analysis of Intra- and Inter-professional Leadership Behaviours Within “The Burns Suite”: Identifying Key Leadership Models

Objective Leadership is particularly important in complex highly interprofessional health care contexts involving a number of staff, some from the same specialty (intraprofessional), and others from different specialties (interprofessional). The authors recently published the concept of “The Burns Suite” (TBS) as a novel simulation tool to deliver interprofessional and teamwork training. It is unclear which leadership behaviors are the most important in an interprofessional burns resuscitation scenario, and whether they can be modeled on to current leadership theory. The purpose of this study was to perform a comprehensive video analysis of leadership behaviors within TBS. Methods A total of 3 burns resuscitation simulations within TBS were recorded. The video analysis was grounded-theory inspired. Using predefined criteria, actions/interactions deemed as leadership behaviors were identified. Using an inductive iterative process, 8 main leadership behaviors were identified. Cohen’s κ coefficient was used to measure inter-rater agreement and calculated as κ = 0.7 (substantial agreement). Each video was watched 4 times, focusing on 1 of the 4 team members per viewing (senior surgeon, senior nurse, trainee surgeon, and trainee nurse). The frequency and types of leadership behavior of each of the 4 team members were recorded. Statistical significance to assess any differences was assessed using analysis of variance, whereby a p < 0.05 was taken to be significant. Leadership behaviors were triangulated with verbal cues and actions from the videos. Results All 3 scenarios were successfully completed. The mean scenario length was 22 minutes. A total of 362 leadership behaviors were recorded from the 12 participants. The most evident leadership behaviors of all team members were adhering to guidelines (which effectively equates to following Advanced Trauma and Life Support/Emergency Management of Severe Burns resuscitation guidelines and hence “maintaining standards”), followed by making decisions. Although in terms of total frequency the senior surgeon engaged in more leadership behaviors compared with the entire team, statistically there was no significant difference between all 4 members within the 8 leadership categories. This analysis highlights that “distributed leadership” was predominant, whereby leadership was “distributed” or “shared” among team members. The leadership behaviors within TBS also seemed to fall in line with the “direction, alignment, and commitment” ontology. Conclusions Effective leadership is essential for successful functioning of work teams and accomplishment of task goals. As the resuscitation of a patient with major burns is a dynamic event, team leaders require flexibility in their leadership behaviors to effectively adapt to changing situations. Understanding leadership behaviors of different team members within an authentic simulation can identify important behaviors required to optimize nontechnical skills in a major resuscitation. Furthermore, attempting to map these behaviors on to leadership models can help further our understanding of leadership theory. Collectively this can aid the development of refined simulation scenarios for team members, and can be extrapolated into other areas of simulation-based team training and interprofessional education.

Seeking To Understand, Aspiring to Teach: Exploring How Life Shapes Emotional and Personal Development in Global Health Leaders

Thesis (Master's)--University of Washington, 2016-08

School leaders as both colonized and colonizers: understanding professional identity in an era of no child left behind

This study positioned the federal No Child Left Behind (NCLB) Act of 2002 as a reified colonizing entity, inscribing its hegemonic authority upon the professional identity and work of school principals within their school communities of practice. Pressure on educators and students intensifies each year as the benchmark for Adequate Yearly Progress under the NCLB policy is raised, resulting in standards-based reform, scripted curriculum and pedagogy, absence of elective subjects, and a general lack of autonomy critical to the work of teachers as they approach each unique class and student (Crocco & Costigan, 2007; Mabry & Margolis, 2006). Emphasis on high stakes standardized testing as the indicator for student achievement (Popham, 2005) affects educators’ professional identity through dramatic pedagological and structural changes in schools (Day, Flores, & Viana, 2007). These dramatic changes to the ways our nation conducts schooling must be understood and thought about critically from school leaders’ perspectives as their professional identity is influenced by large scale NCLB school reform. The author explored the impact No Child Left Behind reform had on the professional identity of fourteen, veteran Illinois principals leading in urban, small urban, suburban, and rural middle and elementary schools. Qualitative data were collected during semi-structured interviews and focus groups and analyzed using a dual theoretical framework of postcolonial and identity theories. Postcolonial theory provided a lens from which the author applied a metaphor of colonization to principals’ experiences as colonized-colonizers in a time of school reform. Principal interview data illustrated many examples of NCLB as a colonizing authority having a significant impact on the professional identity of school leaders. This framework was used to interpret data in a unique and alternative way and contributed to the need to better understand the ways school leaders respond to district-level, state-level, and national-level accountability policies (Sloan, 2000). Identity theory situated principals as professionals shaped by the communities of practice in which they lead. Principals’ professional identity has become more data-driven as a result of NCLB and their role as instructional leaders has intensified. The data showed that NCLB has changed the work and professional identity of principals in terms of use of data, classroom instruction, Response to Intervention, and staffing changes. Although NCLB defines success in terms of meeting or exceeding the benchmark for Adequate Yearly Progress, principals’ view AYP as only one measurement of their success. The need to meet the benchmark for AYP is a present reality that necessitates school-wide attention to reading and math achievement. At this time, principals leading in affluent, somewhat homogeneous schools typically experience less pressure and more power under NCLB and are more often labeled “successful” school communities. In contrast, principals leading in schools with more heterogeneity experience more pressure and lack of power under NCLB and are more often labeled “failing” school communities. Implications from this study for practitioners and policymakers include a need to reexamine the intents and outcomes of the policy for all school communities, especially in terms of power and voice. Recommendations for policy reform include moving to a growth model with multi-year assessments that make sense for individual students rather than one standardized test score as the measure for achievement. Overall, the study reveals enhancements and constraints NCLB policy has caused in a variety of school contexts, which have affected the professional identity of school leaders.

'A Bayesian Optimisation Algorithm for the Nurse Scheduling Problem'

Abstract- A Bayesian optimization algorithm for the nurse scheduling problem is presented, which involves choosing a suitable scheduling rule from a set for each nurse's assignment. Unlike our previous work that used GAs to implement implicit learning, the learning in the proposed algorithm is explicit, i.e. eventually, we will be able to identify and mix building blocks directly. The Bayesian optimization algorithm is applied to implement such explicit learning by building a Bayesian network of the joint distribution of solutions. The conditional probability of each variable in the network is computed according to an initial set of promising solutions. Subsequently, each new instance for each variable is generated by using the corresponding conditional probabilities, until all variables have been generated, i.e. in our case, a new rule string has been obtained. Another set of rule strings will be generated in this way, some of which will replace previous strings based on fitness selection. If stopping conditions are not met, the conditional probabilities for all nodes in the Bayesian network are updated again using the current set of promising rule strings. Computational results from 52 real data instances demonstrate the success of this approach. It is also suggested that the learning mechanism in the proposed approach might be suitable for other scheduling problems.

'Bayesian Optimisation for Nurse Scheduling'

A Bayesian optimisation algorithm for a nurse scheduling problem is presented, which involves choosing a suitable scheduling rule from a set for each nurse's assignment. When a human scheduler works, he normally builds a schedule systematically following a set of rules. After much practice, the scheduler gradually masters the knowledge of which solution parts go well with others. He can identify good parts and is aware of the solution quality even if the scheduling process is not yet completed, thus having the ability to finish a schedule by using flexible, rather than fixed, rules. In this paper, we design a more human-like scheduling algorithm, by using a Bayesian optimisation algorithm to implement explicit learning from past solutions. A nurse scheduling problem from a UK hospital is used for testing. Unlike our previous work that used Genetic Algorithms to implement implicit learning [1], the learning in the proposed algorithm is explicit, i.e. we identify and mix building blocks directly. The Bayesian optimisation algorithm is applied to implement such explicit learning by building a Bayesian network of the joint distribution of solutions. The conditional probability of each variable in the network is computed according to an initial set of promising solutions. Subsequently, each new instance for each variable is generated by using the corresponding conditional probabilities, until all variables have been generated, i.e. in our case, new rule strings have been obtained. Sets of rule strings are generated in this way, some of which will replace previous strings based on fitness. If stopping conditions are not met, the conditional probabilities for all nodes in the Bayesian network are updated again using the current set of promising rule strings. For clarity, consider the following toy example of scheduling five nurses with two rules (1: random allocation, 2: allocate nurse to low-cost shifts). In the beginning of the search, the probabilities of choosing rule 1 or 2 for each nurse is equal, i.e. 50%. After a few iterations, due to the selection pressure and reinforcement learning, we experience two solution pathways: Because pure low-cost or random allocation produces low quality solutions, either rule 1 is used for the first 2-3 nurses and rule 2 on remainder or vice versa. In essence, Bayesian network learns 'use rule 2 after 2-3x using rule 1' or vice versa. It should be noted that for our and most other scheduling problems, the structure of the network model is known and all variables are fully observed. In this case, the goal of learning is to find the rule values that maximize the likelihood of the training data. Thus, learning can amount to 'counting' in the case of multinomial distributions. For our problem, we use our rules: Random, Cheapest Cost, Best Cover and Balance of Cost and Cover. In more detail, the steps of our Bayesian optimisation algorithm for nurse scheduling are: 1. Set t = 0, and generate an initial population P(0) at random; 2. Use roulette-wheel selection to choose a set of promising rule strings S(t) from P(t); 3. Compute conditional probabilities of each node according to this set of promising solutions; 4. Assign each nurse using roulette-wheel selection based on the rules' conditional probabilities. A set of new rule strings O(t) will be generated in this way; 5. Create a new population P(t+1) by replacing some rule strings from P(t) with O(t), and set t = t+1; 6. If the termination conditions are not met (we use 2000 generations), go to step 2. Computational results from 52 real data instances demonstrate the success of this approach. They also suggest that the learning mechanism in the proposed approach might be suitable for other scheduling problems. Another direction for further research is to see if there is a good constructing sequence for individual data instances, given a fixed nurse scheduling order. If so, the good patterns could be recognized and then extracted as new domain knowledge. Thus, by using this extracted knowledge, we can assign specific rules to the corresponding nurses beforehand, and only schedule the remaining nurses with all available rules, making it possible to reduce the solution space. Acknowledgements The work was funded by the UK Government's major funding agency, Engineering and Physical Sciences Research Council (EPSRC), under grand GR/R92899/01. References [1] Aickelin U, "An Indirect Genetic Algorithm for Set Covering Problems", Journal of the Operational Research Society, 53(10): 1118-1126,

'The application of Bayesian Optimization and Classifier Systems in Nurse Scheduling'

Abstract. Two ideas taken from Bayesian optimization and classifier systems are presented for personnel scheduling based on choosing a suitable scheduling rule from a set for each person's assignment. Unlike our previous work of using genetic algorithms whose learning is implicit, the learning in both approaches is explicit, i.e. we are able to identify building blocks directly. To achieve this target, the Bayesian optimization algorithm builds a Bayesian network of the joint probability distribution of the rules used to construct solutions, while the adapted classifier system assigns each rule a strength value that is constantly updated according to its usefulness in the current situation. Computational results from 52 real data instances of nurse scheduling demonstrate the success of both approaches. It is also suggested that the learning mechanism in the proposed approaches might be suitable for other scheduling problems.

'Exploiting problem structure in a genetic algorithm approach to a nurse rostering problem'

There is considerable interest in the use of genetic algorithms to solve problems arising in the areas of scheduling and timetabling. However, the classical genetic algorithm paradigm is not well equipped to handle the conflict between objectives and constraints that typically occurs in such problems. In order to overcome this, successful implementations frequently make use of problem specific knowledge. This paper is concerned with the development of a GA for a nurse rostering problem at a major UK hospital. The structure of the constraints is used as the basis for a co-evolutionary strategy using co-operating sub-populations. Problem specific knowledge is also used to define a system of incentives and disincentives, and a complementary mutation operator. Empirical results based on 52 weeks of live data show how these features are able to improve an unsuccessful canonical GA to the point where it is able to provide a practical solution to the problem.

Building Better Nurse Scheduling Algorithms

The aim of this research is twofold: Firstly, to model and solve a complex nurse scheduling problem with an integer programming formulation and evolutionary algorithms. Secondly, to detail a novel statistical method of comparing and hence building better scheduling algorithms by identifying successful algorithm modifications. The comparison method captures the results of algorithms in a single figure that can then be compared using traditional statistical techniques. Thus, the proposed method of comparing algorithms is an objective procedure designed to assist in the process of improving an algorithm. This is achieved even when some results are non-numeric or missing due to infeasibility. The final algorithm outperforms all previous evolutionary algorithms, which relied on human expertise for modification.

An Estimation of Distribution Algorithm for Nurse Scheduling

Schedules can be built in a similar way to a human scheduler by using a set of rules that involve domain knowledge. This paper presents an Estimation of Distribution Algorithm (EDA) for the nurse scheduling problem, which involves choosing a suitable scheduling rule from a set for the assignment of each nurse. Unlike previous work that used Genetic Algorithms (GAs) to implement implicit learning, the learning in the proposed algorithm is explicit, i.e. we identify and mix building blocks directly. The EDA is applied to implement such explicit learning by building a Bayesian network of the joint distribution of solutions. The conditional probability of each variable in the network is computed according to an initial set of promising solutions. Subsequently, each new instance for each variable is generated by using the corresponding conditional probabilities, until all variables have been generated, i.e. in our case, a new rule string has been obtained. Another set of rule strings will be generated in this way, some of which will replace previous strings based on fitness selection. If stopping conditions are not met, the conditional probabilities for all nodes in the Bayesian network are updated again using the current set of promising rule strings. Computational results from 52 real data instances demonstrate the success of this approach. It is also suggested that the learning mechanism in the proposed approach might be suitable for other scheduling problems.