Brief Psychotherapy with Children in a Specialist School: An Exploration of the Underlying Issues for Children and Mainstream Schools

This thesis explores brief psychotherapy with children on placement at a specialist school setting, as part of an on-site, child psychotherapy outreach provision. The study sought to explore two research questions concerning the themes that could emerge in brief work with children and how these themes could be discussed in relation to the understanding formed by their mainstream school teachers. A qualitative research design was used to investigate these questions. The methods used to collect data were case studies, concerning the brief psychotherapy with 4 boys, aged 7 years, and and semi-structured interviews were conducted with the teachers. Thematic analysis was used to explore the data. The themes that were derived from the analysis were described in detail. The research found that brief work has considerable benefit for children and mainstream schools. Through the brief work intervention, the children all made significant progress in all areas of their lives a school. Contributions that the research makes to related fields, the implications that it has for policy and practice and recommendations for future research were all discussed.

Myths and Realities of Inter-professional Working

Introduction: Childhood cancers are rare and community based health care professionals have limited experience in caring for these children and often even less experience in providing their palliative care. It is well recognised that the provision of palliative care falls beyond the remit of any one profession, thus inter professional working is the standard model. This qualitative study aims to examine the experiences of the range of health care professionals involved in providing palliative care at home for children with cancer, focusing on how knowledge is exchanged; the level of communication and support both interprofessionally and at the community/specialist interface. It also aims to examine interprofessional collaboration in palliative care; identifying healthcare professional's perceptions of problems involved, interprofessional boundaries, specific areas of the organisation or provision of care that could be enhanced through changes in practice, support issues and the educational needs of health professionals. Methods The study involves three types of data collection; in-depth interviews, facilitated case discussion (FCD) and field notes from up to 20 cases (a "case" refers to the provision of palliative care to one child). Cases are selected from children who were treated at one regional childhood caner centre. For each case the community based health care professionals (for example the GP, community nurse or health visitor) involved in the care of the child at home are invited to participate in a one-to-one tape recorded in-depth interview followed by a group discussion in the form of a FCD. Field notes are completed following each interview. Data analysis follows a grounded theory approach. The term "social worlds theory" (SWT) his used to define a type of social organisation with no fixed or formal boundaries (such as membership boundaries), for example the range of health professionals that work together to provide palliative care. The boundaries of SW's are determined by the interaction and communication between recognised organisations, such as community nursing teams and general practitioners. SWT examines encounters between different professional groups and can be used to extend knowledge in both the organisation (for example general practice) and the content of what is being provisioned (for example, palliative care). The use of SWT in the analysis of the data is through examining the ethos of the different professions and the associated individual approaches to palliative care, exploring how this determines their roles in the provision of palliative care. Results 10 cases have so far been completed: 47 1:1 interviews (with a range of between 2-7 health care professionals being involved in each case): ( 9 x GP, 19 x CCN, 4 x DN, 3 x HV, 1 x HV assistant 7 x paediatric palliative care nurses, 1 x home support worker, 1 x OT, 1 x physiotherapist, 1 x community paediatrician) and 5 x FCD. The range of participants in the FCDs reflected that of the individual interview sampler. Data obtained to date gives clear insight into the personal experience of the individual health care professional in providing palliative care. Two themes emerging from the data will be focused upon: the continuity of care provision throughout treatment and palliation and the emotional burden experiences by the health care professional. Conclusions SWT can provide a useful framework in examining the social worlds of a disparate group of health care professionals working together for the first and maybe, the only time. A wide variation in the continuity of care provision has been found not only between professions, but also within professions. The emotional burden is evident across the professions.

Care planning for aggression management in a specialist secure mental health service: user involvement

This paper describes an audit of prevention and management of violence and aggression care plans and incident reporting forms which aimed to: (i) report the compliance rate of completion of care plans; (ii) identify the extent to which patients contribute to and agree with their care plan; (iii) describe de-escalation methods documented in care plans; and (iv) ascertain the extent to which the de-escalation methods described in the care plan are recorded as having been attempted in the event of an incident. Care plans and incident report forms were examined for all patients in men's and women's mental health care pathways who were involved in aggressive incidents between May and October 2012. In total, 539 incidents were examined, involving 147 patients and 121 care plans. There was no care plan in place at the time of 151 incidents giving a compliance rate of 72%. It was documented that 40% of patients had contributed to their care plans. Thematic analysis of de-escalation methods documented in the care plans revealed five de-escalation themes: staff interventions, interactions, space/quiet, activities and patient strategies/skills. A sixth category, coercive strategies, was also documented. Evidence of adherence to de-escalation elements of the care plan was documented in 58% of incidents. The reasons for the low compliance rate and very low documentation of patient involvement need further investigation. The inclusion of coercive strategies within de-escalation documentation suggests that some staff fundamentally misunderstand de-escalation.

Patients' satisfaction and quality of life in coronary artery disease

OBJECTIVES: To assess satisfaction of survivors of coronary artery diseases (CAD) with healthcare services and to determine whether specific components of standard health-related quality of life (HRQL) assessment tools might identify areas of satisfaction and dissatisfaction. METHOD: A specific tool developed to provide a comprehensive assessment of healthcare needs was administered concomitantly with generic and specific HRQL instruments, on 242 patients with CAD, admitted to an acute coronary unit during a single year. RESULTS: 92.5% of patients confirmed their trust in and satisfaction with the care given by their General Practitioner; even so, one third experienced difficulty getting an appointment and a quarter wanted more time for each consultation or prompt referral to a specialist when needed. Around a third expressed dissatisfaction with advice from the practice nurse or hospital consultant. Overall 54% were highly satisfied with services, 33% moderately satisfied and 13% dissatisfied.Cronbach's alpha was 0.87; the corrected total-item correlation ranged between 0.55-0.75, with trivial 'floor' score and low 'ceiling' effect. Several domains in all three HRQL tools correlated with items relating to satisfaction. The Seattle Angina Questionnaire Treatment Score correlated significantly with all satisfaction items and with the global satisfaction score. CONCLUSION: Cardiac patients' demanded better services and advice from, and more time with, health professionals and easier surgery access. The satisfaction tool showed acceptable psychometric properties. In this patient group, disease-specific HRQL tools seem more appropriate than generic tools for surveys of patient satisfaction

'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.

An Estimation of Distribution Algorithm with Intelligent Local Search for Rule-based Nurse Rostering

This paper proposes a new memetic evolutionary algorithm to achieve explicit learning in rule-based nurse rostering, which involves applying a set of heuristic rules for each nurse's assignment. The main framework of the algorithm is an estimation of distribution algorithm, in which an ant-miner methodology improves the individual solutions produced in each generation. Unlike our previous work (where learning is implicit), the learning in the memetic estimation of distribution algorithm is explicit, i.e. we are able to identify building blocks directly. The overall approach learns by building a probabilistic model, i.e. an estimation of the probability distribution of individual nurse-rule pairs that are used to construct schedules. The local search processor (i.e. the ant-miner) reinforces nurse-rule pairs that receive higher rewards. A challenging real world nurse rostering problem is used as the test problem. Computational results show that the proposed approach outperforms most existing approaches. It is suggested that the learning methodologies suggested in this paper may be applied to other scheduling problems where schedules are built systematically according to specific rules.