2 resultados para factors models

em Dalarna University College Electronic Archive


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Background: Evidence-based practice (EBP) is emphasized to increase the quality of care and patient safety. EBP is often described as a process consisting of distinct activities including, formulating questions, searching for information, compiling the appraised information, implementing evidence, and evaluating the resulting practice. To increase registered nurses' (RNs') practice of EBP, variables associated with such activities need to be explored. The aim of the study was to examine individual and organizational factors associated with EBP activities among RNs 2 years post graduation. Methods: A cross-sectional design based on a national sample of RNs was used. Data were collected in 2007 from a cohort of RNs, included in the Swedish Longitudinal Analyses of Nursing Education/Employment study. The sample consisted of 1256 RNs (response rate 76%). Of these 987 RNs worked in healthcare at the time of the data collection. Data was self-reported and collected through annual postal surveys. EBP activities were measured using six single items along with instruments measuring individual and work-related variables. Data were analyzed using logistic regression models. Results: Associated factors were identified for all six EBP activities. Capability beliefs regarding EBP was a significant factor for all six activities (OR = 2.6 - 7.3). Working in the care of older people was associated with a high extent of practicing four activities (OR = 1.7 - 2.2). Supportive leadership and high collective efficacy were associated with practicing three activities (OR = 1.4 - 2.0). Conclusions: To be successful in enhancing EBP among newly graduated RNs, strategies need to incorporate both individually and organizationally directed factors.

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Background: Genetic variation for environmental sensitivity indicates that animals are genetically different in their response to environmental factors. Environmental factors are either identifiable (e.g. temperature) and called macro-environmental or unknown and called micro-environmental. The objectives of this study were to develop a statistical method to estimate genetic parameters for macro- and micro-environmental sensitivities simultaneously, to investigate bias and precision of resulting estimates of genetic parameters and to develop and evaluate use of Akaike’s information criterion using h-likelihood to select the best fitting model. Methods: We assumed that genetic variation in macro- and micro-environmental sensitivities is expressed as genetic variance in the slope of a linear reaction norm and environmental variance, respectively. A reaction norm model to estimate genetic variance for macro-environmental sensitivity was combined with a structural model for residual variance to estimate genetic variance for micro-environmental sensitivity using a double hierarchical generalized linear model in ASReml. Akaike’s information criterion was constructed as model selection criterion using approximated h-likelihood. Populations of sires with large half-sib offspring groups were simulated to investigate bias and precision of estimated genetic parameters. Results: Designs with 100 sires, each with at least 100 offspring, are required to have standard deviations of estimated variances lower than 50% of the true value. When the number of offspring increased, standard deviations of estimates across replicates decreased substantially, especially for genetic variances of macro- and micro-environmental sensitivities. Standard deviations of estimated genetic correlations across replicates were quite large (between 0.1 and 0.4), especially when sires had few offspring. Practically, no bias was observed for estimates of any of the parameters. Using Akaike’s information criterion the true genetic model was selected as the best statistical model in at least 90% of 100 replicates when the number of offspring per sire was 100. Application of the model to lactation milk yield in dairy cattle showed that genetic variance for micro- and macro-environmental sensitivities existed. Conclusion: The algorithm and model selection criterion presented here can contribute to better understand genetic control of macro- and micro-environmental sensitivities. Designs or datasets should have at least 100 sires each with 100 offspring.