Nurse-led home exercise programme improves physical function for people receiving haemodialysis

Nurse-led home exercise programme improves physical function for people receiving haemodialysis

Addressing the theory practice gap in nurse education: evaluating teaching through audit against NMC standards and final management placement

Introduction
This paper outlines an innovative approach to auditing and evaluating the content of a management and leadership module for undergraduate nursing students after their final management clinical placement. Normally evaluations of teaching in a module take place at the end of a teaching module and therefore do not properly reflect the value of the teaching in relation to practical clinical experience.
Aim
This audit and evaluation sought to explore both the practical value of the teaching and learning, and also the degree to which it the teaching reflected against the NMC Standards of Education and Learning (2010 domain 3).
Methods
Having piloted the evaluative tool with an earlier cohort of nursing students, this evaluation explored both a quantitative assessment employing a Personal Response System (n =172), together with a qualitative dimension (n=116), thus delivering paper-based comments and reflections from students on the value and practicality of the module teaching theory to their final clinical management experience. The quantitative audit data were analysed for frequencies and cross tabulation and the qualitative audit data were thematically analysed.
Results
Results suggest a significant proportion of the students, appreciated the quality of the standard of teaching, but more importantly, ‘valued or highly valued’ the teaching and learning in relation to how it helped to significantly inform their management placement experience. A smaller proportion of the students underlined limitations and areas in which further improvement can be made in teaching and learning to the module.
Conclusion
Significantly positive evaluation by the students of the practical value of teaching and learning, to the theoretical management module. This has proved a useful auditing approach in assessing the theoretical teaching to student’s Level 3 clinical experience, and facilitated significant recommendations as far as developing the teaching and learning to better reflect the practice needs of nursing students

Evaluating the objective structured long examination record for nurse education

An objective structured long examination record (OSLER) is a modification of the long-case clinical examination and is mainly used in medical education. This study aims to obtain nursing students' views of the OSLER compared with the objective structured clinical examination (OSCE), which is used to assess discrete clinical skills. A sample of third-year undergraduate nursing students (n=21) volunteered to participate from a cohort of 230 students. Participants undertook the OSLER under examination conditions. Pre-and post-test questionnaires gathered the students' views on the assessments and these were analysed from a mainly qualitative perspective. Teachers' and simulated patient views were also used for data triangulation. The findings indicate that the OSLER ensures more holistic assessment of a student's clinical skills and particularly essential skills such as communication, and that the OSLER, together with the OSCE, should be used to supplement the assessment of clinical competence in nursing education.

The neonatal nurse: advocating for breastfeeding mothers

Accurate information and support from healthcare professionals as well as respect for parental choice are all factors which contribute to effective breastfeeding in the neonatal unit; with this in mind, Colm Darby and Sharon Nurse discuss the potential problems in expressing breast milk and the interventions which might be effective in avoiding them. Advocacy is an inherent part of neonatal nurses' role whilst caring for sick, vulnerable babies. Colm Darby is a male neonatal nurse working in a predominantly female environment and passionately believes in supporting and advocating for mothers who want to provide breast milk for their babies. In this article, CoIm uses Borton's model of reflection to discuss how he acted as an effective advocate for such a mother.

From teaching clinician to research academic- changing focus within UK nurse education; a discussion paper

The focus of this discussion paper is the need for effective professional socialisation of student nurses and the degree to which core values and culture are transferred through University schools of nursing, the academic teaching staff and to the student nurses.
UK schools of nursing had progressively transferred into university institutions more than two decades ago. Schools of nursing and the teaching academics within them, to a greater or lesser extent, impact on and help to professionally socialize student nurses. Professed core values of universities whilst including a focus on excellence and innovation, perhaps also include, collegiality, integrity and social commitment to care. These are all qualities, which should be core values and elements
of the transferable professional culture to student nurses. Notwithstanding the professed core values, at least in some areas of UK universities there is some evidence of increasing competition and a disproportionate research market driven focus. This can reflect back into schools of nursing and is inconsistent with nursing professional values.

This paper explores the degree to which the professed core values of universities and the institutional culture are necessarily enacted, and the degree to which
any dissonance in the institutions professed/enacted core values and culture reflect through the schools of nursing and impact in the professional socialisation of student nurses. The paper also explores the degree to which effective leadership in schools of nursing can help to maintain professional core values and a culture of nursing professional

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.

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

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

'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.

A Decomposition, Construction and Post-Processing Approach for Nurse Rostering

This paper presents our work on decomposing a specific nurse rostering problem by cyclically assigning blocks of shifts, which are designed considering both hard and soft constraints, to groups of nurses. The rest of the shifts are then assigned to the nurses to construct a schedule based on the one cyclically generated by blocks. The schedules obtained by decomposition and construction can be further improved by a variable neighborhood search. Significant results are obtained and compared with a genetic algorithm and a variable neighborhood search approach on a problem that was presented to us by our collaborator, ORTEC bv, The Netherlands. We believe that the approach has the potential to be further extended to solve a wider range of nurse rostering problems.