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Clinical learning is an integral part of nursing education; yet, clinical education has been problematic. Why are some clinical experiences better than others? Various studies have indicated that not all practice settings are able to provide student nurses with a positive learning environment. The clinical learning environment is a multidimensional entity with a complex social context. Classroom learning environment research based on psychosocial educational conceptual frameworks has been well established in the past 30 years, with evidential benefits. Previous research on clinical learning environment was examined. However, minimal studies have been conducted on hospital learning environments from the psychosocial educational perspective. To maximize nursing students' clinical learning experiences, the author developed the Clinical Learning Environment Inventory based on the theoretical framework in psychosocial education.
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The effect of acoustic environment on the impression of the landscape and the effect of the landscape on the impression of the acoustic environment were investigated by a multivariate technique using a semantic differential method. The resulting five dimensions were interpreted as "Calmness", "Activity", "Naturalness", "Uniqueness", and "Volume (Magnitude)" for both landscape and sound evaluations. These common psychological qualities are considered to be the intermodalities of visual and auditory processing. Further, the consonance phenomenon through the intermodalities, a kind of interaction between auditory and visual processing which changes the psychological qualities to the same direction, was observed when the combination of the acoustic environment and landscape was evaluated to be matched. These results may be the basis of the soundscape design under various kinds of landscape.
Within the global urban system, the statistical relationship between urban eco-environment (UE) and urban competitiveness (UC) (RUEC) is researched. Data showed that there is a statistically inverted-U relationship between UE and UC. Eco-environmental factor is put into the classification of industries, and gets six industrial types by two indexes viz. industries' eco-environmental demand and pressure. The statistical results showed that there is a strong relationship, for new industrial classification, between the changes of industrial structure and evolvement of UE. The drive mechanism of the evolvement of urban eco-environment, with human demand and global work division was analyzed. The conclusion is that the development stratege, industrial policies of cities, and environmental policies fo cities must be fit with their ranks among the global urban system. At the era of globalization, so far as the environmental policies, their rationality could not be assessed with the level of strictness, but it can enhance cities' competitiveness when they are fit with cities' capabilities to attract and control some sections of the industry's value-chain. None but these kinds of environmental policies can probably enhance the UC.
Latent environmental effects of genotype by environment interactions could be predicted from observed environmental covariates. Predictions into the wider target population of environments revealed greater insights. Wheat is grown across a diverse range of environments in Australia with contrasting environmental constraints. Targeted breeding to optimise genotypes in target environments is hindered by large and ubiquitous genotype by environment interactions (GEI). Common GEI in multi-environment trial experiments, which sample the target population of environments, can be efficiently modelled using latent environmental effects from factor analytic mixed models. However, generalised prediction into the full target population of environments is difficult without a clear link to observed environmental covariates (ECs) that are defined from high-resolution weather and soil data. Here, we used a large wheat multi-environment trial dataset and demonstrated that latent environmental effects can be associated with and predicted from observed ECs. We found GEI-based environment classes could be defined by combinations of key ECs. Prediction of main and latent effects in a wider set of environments covering the full TPE across the Australian grain belt over 13 years revealed the complex trends of environmental effects and GEI over regional scales demonstrating high year-to-year variability. Regional environment types often shifted year-to-year. Cross-validation of forward genomic prediction into untested year environments demonstrated that increased accuracy is possible if estimated genetic effects are also accurate and ECs of new environments are known. These findings may guide Australian wheat breeders to better target specifically adapted material to mega-environments defined by static GEI while also considering broad adaptability and non-static GEI resulting from year-to-year variability.
Dairy cattle breeding organizations tend to sell semen to breeders operating in different environments and genotype x environment interaction may play a role. The objective of this study was to investigate optimization of dairy cattle breeding programs for 2 environments with genotype x environment interaction. Breeding strategies differed in 1) including 1 or 2 environments in the breeding goal, 2) running either 1 or 2 breeding programs, and 3) progeny testing bulls in 1 or 2 environments. Breeding strategies were evaluated on average genetic gain of both environments, which was predicted by using a pseudo-BLUP selection index model. When both environments were equally important and the genetic correlation was higher than 0.61, the highest average genetic gain was achieved with a single breeding program with progeny-testing all bulls in both environments. When the genetic correlation was lower than 0.61, it was optimal to have 2 environment-specific breeding programs progeny-testing an equal number of bulls in their own environment only. Breeding strategies differed by 2 to 12% in average genetic gain, when the genetic correlation ranged between 0.50 and 1.00. Ranking of breeding strategies, based on the highest average genetic gain, was relatively insensitive to heritability, number of progeny per bull, and the relative importance of both environments, but was very sensitive to selection intensity. With more intense selection, running 2 environment-specific breeding programs was optimal for genetic correlations up to 0.70-0.80, but this strategy was less appropriate for situations where 1 of the 2 environments had a relative importance less than 10 to 20%. Results of this study can be used as guidelines to optimize breeding programs when breeding dairy cattle for different parts of the world.
The experiments reported here investigated the mechanisms of drug tolerance acquisition in environments differing in distinctiveness. Specifically, they examined the hypothesis that tolerance acquired in non-distinctive environments might involve habituation while tolerance acquired in distinctive environments involves a classical conditioning or associative learning mechanism. In Experiment 1, rats pre-exposed to injection-ritual cues (placebo injections) prior to acquisition of tolerance to morphine analgesia in distinctive or non-distinctive environments showed a typically attenuated tolerance response on an environment-change test. The magnitude of the attenuation was not affected by the distinctiveness of the acquisition environment. In Experiment 2, rats acquiring tolerance in distinctive or non-distinctive environments, but without prior injection-ritual pre-exposure, did not demonstrate an attenuation of tolerance on an environment-change test. Tolerance acquired in either environment was unaffected by a subsequent rest period in the colony room, but was attenuated by a subsequent period of daily placebo injections in the colony room. It is argued that failure to observe environment-specific tolerance, as in Experiment 2 and in previous reports in the literature, may reflect overshadowing of environmental stimuli by injection-ritual stimuli, and are not indicative of a fundamental difference between the mechanisms of tolerance acquisition in environments varying in distinctiveness.
The effects of quantitative trait loci (QTL) on phenotypic development may depend on the environment (QTL x environment interaction), other QTL (genetic epistasis), or both. In this article, we present a new statistical model for characterizing specific QTL that display environment-dependent genetic expressions and genotype x environment interactions for developmental trajectories. Our model was derived within the maximum-likelihood-based mixture model framework, incorporated by biologically meaningful growth equations and environment-dependent genetic effects of QTL, and implemented with the EM algorithm. With this model, we can characterize the dynamic patterns of genetic effects of QTL governing growth curves and estimate the global effect of the underlying QTL during the course of growth and development. In a real example with rice, our model has successfully detected several QTL that produce differences in their genetic expression between two contrasting environments. These detected QTL cause significant genotype x environment interactions for some fundamental aspects of growth trajectories. The model provides the basis for deciphering the genetic architecture of trait expression adjusted to different biotic and abiotic environments and genetic relationships for growth rates and the timing of life-history events for any organism.
A review of contemporary nursing research reveals a tendency to focus on select aspects of the hospital environment such as noise, light, and music. Although studies such as these shed light on discrete aspects of the hospital environment, this body of literature contributes little to an understanding of the entirety of that world as the patient in the sickbed experiences it. The purpose of the study detailed in this article was to describe the patient's experience of the acute care hospital environment. Nondirective, in-depth phenomenological interviews were conducted, then transcribed verbatim, and analyzed for themes. Against the backdrop of "I lived and that's all that matters," there were 3 predominant themes in patients' experience of the acute care environment: (1) disconnection/connection, (2) fear/less fear, and (3) confinement/freedom. In this environment, human-to-human contact increased security and power in an environment that was described as sterile, disorienting, and untrustworthy. Acute and critical care nurses and other caregivers can use the findings to create less noxious hospital environments.
The competence model of aging emphasizes transactions between the person and his/her environment. Adaptation of the environment to the person's resources gets equal status to the adaptation of the person to environmental demands. This becomes relevant for persons in need of help and care. Adapting their environment leads to a reduction of their environmental docility. It also becomes relevant for persons with higher potentials: Optimizing their environment contributes to environmental proactivity. For all groups, an increase of quality of life is correlated to optimized environments. While human factors design has created favorable conditions in the work place for many persons, it has not been applied often to the construction of home environments and to the production of equipment for everyday use. Results from a study on "Chances for and limitations of independent life in old age" are presented. They prove that elderly people in the new states of the Federal Republic of Germany not only differ from those in the old states with regard to the help and care they need. In the new states, environmental factors contribute considerably to limitations of independent life. Potentials of an optimally designed environment for the reduction of help and care are utilized even less in the eastern states of Germany than in the west. Arguments for the design of the furniture, equipment for communication and transportation, etc. are presented which emphasize ergonomic principles. They require that products should be adapted to the sensory potentials of their users, that they should allow an understanding or an easy cognitive processing of their functions, and that they should match the motor potentials of persons handling them. In short, products should be designed for a use that matches the "human model" as closely as possible. Thus, they will contribute to enhance the environmental proactivity of elderly people.
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The European Environment and Health Process (EEHP), led by the World Health Organization (WHO) Regional Office for Europe, aims to support WHO Member States as they plan and implement national and international environment and health policies. An evaluation of the impact of the EEHP in the UK was conducted in preparation for the fourth Ministerial Conference on Environment and Health in Budapest, 2004. The evaluation identified a number of impacts and influences of the EEHP. This concluded that the process had only a marginal direct influence on policy within the UK. However, it was also concluded that the process had resulted in several indirect influences, including better cooperation between government departments, greater awareness of environment and health issues from an international perspective, and a higher political profile of environment and health issues. A few outcomes of the EEHP also appear to have been taken into account in some national and local policy documents. The National Environmental Health Action Plan, which was produced as a direct result of the EEHP, appears to have had little direct impact in the UK, probably because of the lack of an implementation process and indicators, and because it was superseded by other policy initiatives relatively soon after publication. A need for better coordination and promotion of the EEHP amongst stakeholders responsible for environment and health policy areas was also identified.
The significance of the environment for health is increasingly being recognized. There is a need for systematic approaches to assessment of environmental factors most relevant to health, health outcomes most influenced by the environment, and the relationships between them, as well as for approaches to representing the results of such assessments in policy deliberations. As a step in the development of such methods, we used findings and data from the environmental protection and public health sectors to develop a set of measures representing topics relevant to children's environmental health. We used a definition of the environment that emphasized contaminants and a process that involved both analytic and deliberative elements. The steps in this process were to a) develop a conceptual framework to depict relationships between environment and health with relevant types of data and information, b) select topic areas of significance for children, c) identify best available data sources and devise measures, d) assess possible surrogate data sources and measures when needed, e) design and implement metrics for computation of measures using specified data elements, f) select graphical representations of measures, g) identify related measures, and h) identify data gaps. Representatives of policy and stakeholder audiences participated in this process. The measures are presented in three groups that reflect contaminants in the environment, contaminants in human tissues, and diseases and disorders. The measures present scientifically based representations of data understandable to stakeholders and policy makers that integrate key information from the health and environment sectors in a consistent format.
Increasing attention is being paid to environment characterisation as a means of identifying the environmental factors determining grain protein content (GPC) in durum wheat. New insights in crop physiology and agronomy have led to the development of crop simulation models. Those models can reconstruct plant development for past cropping seasons. One major advantage of these models is that they can also indicate the intensity of limiting factors affecting plants during particular developmental stages. The main environmental factors determining GPC in durum wheat can be investigated by introducing the intensity of limiting factors into genotype x environment (GxE) models. In our case, limiting factors corresponding to water deficit and nitrogen availability were calculated for the development period between booting and heading. These variables were then introduced into a clustering model. This model is an extension of factorial regression applied to discrete environment and genotypic variables. This procedure effectively described the environment main effect: around 30.9% of the sum of squares of the environment main effect was accounted for, using less than 33% of the degrees of freedom. It also partially accounted for GxE interaction. Our methodology, coupling the use of crop simulation and GxE analysis models, is of potential value for improving our understanding of the main development stages and identification of environmental limiting factors for the development of GPC.
The authors distinguished 3 approaches to the study of perceived person-environment fit (P-E fit): (a) atomistic, which examines perceptions of the person and environment as separate entities; (b) molecular, which concerns the perceived comparison between the person and environment; and (c) molar, which focuses on the perceived similarity, match, or fit between the person and environment. Distinctions among these approaches have fundamental implications for theory, measurement, and the subjective experience of P-E fit, yet research has treated these approaches as interchangeable. This study investigated the meaning and relationships among the atomistic, molecular, and molar approaches to fit and examined factors that influence the strength of these relationships. Results showed that the relationships among the approaches deviate markedly from the theoretical logic that links them together. Supplemental analyses indicated that molar fit overlaps with affect and molecular fit gives different weight to atomistic person and environment information depending on how the comparison is framed. These findings challenge fundamental assumptions underlying P-E fit theories and have important implications for future research.
An approach is presented here for quantitative trait loci (QTL) mapping analysis that allows for QTL x environment (E) interaction across multiple environments, without necessarily increasing the number of parameters. The main distinction of the proposed model is in the chosen way of approximation of the dependence of putative QTL effects on environmental states. We hypothesize that environmental dependence of a putative QTL effect can be represented as a function of environmental mean value of the trait. Such a description can be applied to take into account the effects of any cosegregating QTLs from other genomic regions that also may vary across environments. The conducted Monte-Carlo simulations and the example of barley multiple environments experiment demonstrate a high potential of the proposed approach for analyzing QTL x E interaction, although the results are only approximated by definition. However, this drawback is compensated by the possibility to utilize information from a potentially unlimited number of environments with a remarkable reduction in the number of parameters, as compared to previously proposed mapping models with QTL x E interactions.