Investimento na vida pessoal da pessoa idosa institucionalizada

Enquadramento: Nas últimas décadas, tem-se assistido a um envelhecimento populacional crescente e a um progressivo aumento da institucionalização dos idosos. A institucionalização representa frequentemente para o idoso uma rutura com o seu passado, levando à perda da sua individualidade e consequentemente a um processo de isolamento que poderá contribuir para níveis elevados de insatisfação com a vida. O Investimento na vida pessoal remete-nos para a valorização e atribuição de objetivos de vida ao ser humano, naquela que é a última etapa da sua vida, em todos os seus atributos e caraterísticas, inclusive na sua complexa estrutura física, intelectual e espiritual. Objetivos: Avaliar o nível de Investimento na vida pessoal percecionado pelos idosos institucionalizados e analisar a sua relação com as variáveis socio demográficas, clínicas, e psicossociais. Métodos: Trata-se de um estudo não experimental, transversal, descritivo-correlacional e de caráter quantitativo, que foi realizado numa amostra não probabilística por conveniência, constituída por 90 pessoas idosas a residir nas ERPI do concelho de Vila Nova de Paiva. Para a mensuração das variáveis utilizou-se um instrumento de colheita de dados que integra uma secção de caraterização sócio demográfica, e uma secção de caraterização clínico-funcional (índice de Barthel), caraterização familiar (Escala de Apgar Familiar) e situacional, lazer (Índice de Atividades de Lazer), espiritualidade (Escala da Espiritualidade), perceção da vida atual (Escala de Satisfação com a Vida) e futura, e por fim a Escala de Avaliação de Investimento na Vida Pessoal. Resultados: Os dados mostram que a perceção dos idosos sobre o investimento na sua vida pessoal se distribui de uma forma relativamente equitativa por três níveis. Contudo, o maior grupo percentual (37,8%) perceciona o seu investimento como elevado, enquanto 32,2% o entende como baixo e o grupo mais reduzido (30,0%) o considera moderado. A análise por género mostra que os homens tendem a avaliar o investimento de uma forma mais positiva que as mulheres; também os idosos com habilitações académicas superiores (p= 0,041) e com maior nível de independência funcional (p= 0,037) são os que percecionam um maior investimento na vida pessoal. Constatamos ainda que a família tem um efeito significativo (p= 0,020) no nível do investimento na vida do idoso, à semelhança da existência de mais esperança (p= 0,002), maior satisfação com a vida (p= 0,013) e do desenvolvimento de planos para o futuro (p= 0,032). Conclusão: As evidências encontradas neste estudo mostram que há níveis diferenciados de Investimento na vida pessoal entre os idosos. Este Investimento correlaciona-se de forma significativa com diversas variáveis independentes que depois de devidamente identificadas devem ser promovidas para assegurar aos idosos um envelhecimento ativo e com qualidade. As competências atribuídas ao enfermeiro especialista em reabilitação são de uma mais-valia inegualável no desenvolvimento do referido envelhecimento com qualidade, quando inserido numa equipa multidisciplinar, nas Estruturas Residenciais Para Idosos. Palavras-chave: idosos, institucionalização, satisfação com a vida, investimento na vida pessoal.

'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,

'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.

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.

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.

An Indirect Genetic Algorithm for a Nurse Scheduling Problem

This paper describes a Genetic Algorithms approach to a manpower-scheduling problem arising at a major UK hospital. Although Genetic Algorithms have been successfully used for similar problems in the past, they always had to overcome the limitations of the classical Genetic Algorithms paradigm in handling the conflict between objectives and constraints. The approach taken here is to use an indirect coding based on permutations of the nurses, and a heuristic decoder that builds schedules from these permutations. Computational experiments based on 52 weeks of live data are used to evaluate three different decoders with varying levels of intelligence, and four well-known crossover operators. Results are further enhanced by introducing a hybrid crossover operator and by making use of simple bounds to reduce the size of the solution space. The results reveal that the proposed algorithm is able to find high quality solutions and is both faster and more flexible than a recently published Tabu Search approach.

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.

'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.

Determinantes das perturbações musculoesqueléticas nos trabalhadores de cuidados pessoais em residências de apoio ao idoso

Introdução: O enfermeiro especialista em reabilitação é o profissional com competências e conhecimentos para, após o diagnóstico, implementar e monitorizar os resultados dos programas de redução do risco das perturbações musculosqueléticas relacionadas com o trabalho (PME), junto dos trabalhadores de cuidados pessoais em residências de apoio ao idoso, avaliando e introduzindo no processo de prestação de cuidados os necessários ajustamentos, promovendo assim, práticas mais seguras e eficazes. Assim, o presente estudo centrou-se em identificar os determinantes das PME nestes trabalhadores e suas repercussões na saúde. Métodos: Estudo de natureza quantitativa, de tipologia transversal e descritivocorrelacional, com recurso a uma amostra não probabilística por conveniência, constituída por 120 indivíduos, na sua maioria do género feminino (95,8%) e com uma média de idades de 43,21 anos (Dp=10,812 anos). Como instrumento de colheita de dados utilizou-se o inquérito de saúde e trabalho (INSAT), aferido para este domínio de investigação. Resultados: Estes cuidadores formais manifestam défices de saúde com principal relevância para os relacionados com a mobilidade física e dor, quer pela existência de constrangimentos de natureza física e biomecânica, organizacional e psicossocial, bem como de natureza individual. Os problemas de saúde identificados por estes trabalhadores, resultantes das condições e características do trabalho foram: dores de costas (90,8%), dores musculares e articulares (82,5%), varizes (64,2%), dores de cabeça (49,2%) e ansiedade ou irritabilidade (47,5%). Ser do género feminino, ter idade entre os 49-58 anos, ser viúvo ou divorciado, ter doenças crónicas, tomar medicação e efetuar horário diurno, revelaram-se como determinantes percursores das PME assim como, a nível laboral, as características e os constrangimentos organizacionais e relacionais relacionados com o esforço físico, a intensidade e tempo de trabalho, as exigências emocionais, a insuficiência de autonomia e a má qualidade das relações sociais. Conclusão: Estes resultados apontam para a necessidade de desenvolvimento de estratégias preventivas das PME neste grupo profissional, onde é fundamental a intervenção do enfermeiro de reabilitação na implementação de programas de promoção da saúde, gestão do stresse e riscos psicossociais e formação profissional. Palavras-chave: Doenças musculosqueléticas; Enfermagem de Reabilitação; Saúde Ocupacional.

Cuidados de enfermagem à pessoa em situação neurocrítica, potencial dadora de órgãos/tecidos.

Relatório de Trabalho de Projeto apresentado para cumprimento dos requisitos necessários à obtenção do grau de Mestre em Enfermagem Médico-Cirúrgica

Introducing the National Library for Health Skin Conditions Specialist Library

Background: This paper introduces the new National Library for Health Skin Conditions Specialist Library http://www.library.nhs.uk/skin. Description: The aims, scope and audience of the new NLH Skin Conditions Specialist Library, and the composition and functions of its core Project Team, Editorial Team and Stakeholders Group are described. The Library's collection building strategy, resource and information types, editorial policies, quality checklist, taxonomy for content indexing, organisation and navigation, and user interface are all presented in detail. The paper also explores the expected impact and utility of the new Library, as well as some possible future directions for further development. Conclusion: The Skin Conditions Specialist Library is not just another new Web site that dermatologists might want to add to their Internet favourites then forget about it. It is intended to be a practical, "one-stop shop" dermatology information service for everyday practical use, offering high quality, up-to-date resources, and adopting robust evidence-based and knowledge management approaches.

Women's perceptions of Nurse-Midwives' caring behaviours during perinatal loss in Lilongwe, Malawi: An Exploratory study

Objective The Objective for this study was to explore women’s perceptions of and satisfaction with nursing care they received following stillbirth and neonatal death in villages around a community hospital in Lilongwe. Methods This qualitative, exploratory study through a mixture of purposive and snowball sampling, recruited 20 women who had lost a child through stillbirth or neonatal death in the past 2 years. Data were collected through semi-structured interviews in the privacy of the homes of the women. All interviews were tape-recorded and transcribed verbatim and were analyzed using thematic analysis. Results Almost half of the respondents expressed satisfaction with the way nurses cared for them after experiencing perinatal loss, although some felt unable to comment on the quality of care received. However, several bereaved women were dissatisfied with how nurses handled their loss. They noted nurses not providing attention or explanations and some even attributed the death of their child to nurses’ neglect. Conclusions Interventions are needed which foster awareness where nurses become more sensitive to the mothers’ emotional needs in an equally sensitive health care system. There is also need for more research into care provided following perinatal deaths in resource-poor settings to increase the evidence-base for informed and improved care for women who have experienced child loss.

Pharmacological and local toxicity studies of a liposomal formulation for the novel local anaesthetic ropivacaine

This study reports an investigation of the pharmacological activity, cytotoxicity, and local effects of a liposomal formulation of the novel local anaesthetic ropivacaine (RVC) compared with its plain solution. RVC was encapsulated into large unilamellar vesicles (LUVs) composed of egg phosphatidylcholine, cholesterol and a-tocopherol (4:3:0.07, mole %). Particle size, partition coefficient determination and in-vitro release studies were used to characterize the encapsulation process. Cytotoxicity was evaluated by the tetrazolium reduction test using sciatic nerve Schwann cells in culture. Local anaesthetic activity was assessed by mouse sciatic and rat infraorbital nerve blockades. Histological analysis was performed to verify the myotoxic effects evoked by RVC formulations. Plain (RVCPLAIN) and liposomal RVC (RVCLUV) samples were tested at 0.125%, 0.25% and 0.5% concentrations. Vesicle size distribution showed liposomal populations of 370 and 130 nm (85 and 15%, respectively), without changes after RVC encapsulation. The partition coefficient value was 132 26 and in-vitro release assays revealed a decrease in RVC release rate (1.5 fold, P < 0.001) from liposomes. RVCLUV presented reduced cytotoxicity (P < 0.001) when compared with RVCPLAIN Treatment with RVCLUV increased the duration (P < 0.001) and intensity of the analgesic effects either on sciatic nerve blockade (1.4-1.6 fold) and infraorbital nerve blockade tests (1.5 fold), in relation to RVCPLAIN. Regarding histological analysis, no morphological tissue changes were detected in the area of injection and sparse inflammatory cells were observed in only one of the animals treated with RVCPLAIN or RVCLUV at 0.5%. Despite the differences between these preclinical studies and clinical conditions, we suggest RVCLUV as a potential new formulation, since RVC is a new and safe local anaesthetic agent.