Individualised Care Scale-Nurse: Construct validity and internal consistency of the Spanish version

BackgroundIndividualising the provided care is mandatory in nursing and is essential in clinical practice. Therefore, there is a need to develop accurate instruments to evaluate the quality of care. Moreover, there is no validated instrument to assess nurses’ views of individualised care in Spanish-speaking countries.AimTo assess the construct validity and internal consistency of the Spanish version of the Individualised Care Scale-Nurse.MethodsA cross-sectional study including 108 nursing professionals (40.84 ± 9.51 years old, 86.1% female) was used to validate the Spanish Individualised Care Scale-Nurse version. A forward-back translation method with an expert panel and a cross-sectional study was used for transcultural adaptation and psychometric validation purposes. Psychometric properties of feasibility, reliability and validity were assessed. Construct validity was examined through a confirmatory factor analysis and fit indices of the overall model were computed. Internal consistency was explored through McDonald’s omega and Cronbach’s alpha coefficients among other correlation measures.ResultsThe back-translation concluded both Spanish and English Individualised Care Scale-Nurse versions to be equivalent. The original structure of the Individualised Care Scale-Nurse was verified in the Spanish version through the confirmatory factor analysis (factor loadings >0.3; acceptable fit indices: SRMR ≈ 0.08, CFI ≈ 0.9, RMSEA ≈ 0.09 after posteriori modifications). McDonald's omega exceeded 0.7 for both subscales and complete scales revealing an adequate internal consistency.</p

Efficient algorithms for constructing D- and I-optimal exact designs for linear and non-linear models in mixture experiments

The problem of finding optimal exact designs is more challenging than that of approximate optimal designs. In the present paper, we develop two efficient algorithms to numerically construct exact designs for mixture experiments. The first is a novel approach to the well-known multiplicative algorithm based on sets of permutation points, while the second uses genetic algorithms. Using (i) linear and non-linear models, (ii) D/- and I-optimality criteria, and (iii) constraints on the ingredients, both approaches are explored through several practical problems arising in the chemical, pharmaceutical and oil industry

Diseño óptimo de experimentos para modelos de mezclas aplicados en la ingeniería y en las ciencias experimentales

El presente trabajo tiene como objetivo determinar estrategias de construcción de diseños óptimos en problemas de experimentos con mezclas, en los que las variables controlables por el experimentador son proporciones. Para este propósito se han desarrollado herramientas teóricas y numéricas para resolver diferentes situaciones reales en las que surgen este tipo de problemas. A continuación exponemos de manera resumida los principales objetivos y contribuciones de la memoria. El capítulo 1 introduce la finalidad del diseño óptimo de experimentos y presenta las motivaciones por las que surge. Se establecen formalmente las bases sobre las que se construye esta teoría y se exponen conceptos, notaciones y resultados fundamentales sobre los que se apoya. Se definen los principales criterios de optimización, con especial énfasis sobre los criterios de D- e I-optimización, que serán los de mayor interés para el desarrollo de esta memoria. La segunda parte de este capítulo introduce una importante herramienta en la teoría de diseño óptimo, la derivada direccional de una función criterio. Se proporciona también el teorema fundamental del diseño óptimo de experimentos, el teorema general de equivalencia, además de otros resultados fundamentales. El capítulo concluye con la extensión de esta teoría para modelos no lineales. El capítulo 2 comienza justificando la necesidad de desarrollar métodos numéricos para el cálculo de soluciones aproximadas. A continuación se realiza una revisión bibliográfica de las técnicas algorítmicas más utilizadas en la literatura: el algoritmo Wynn-Fedorov y el algoritmo multiplicativo. El primero de los resultados desarrollados en este trabajo es un nuevo algoritmo para el cálculo de diseños óptimos aproximados: el Algoritmo Combinado. Aunque puede aplicarse para cualquier función criterio, la convergencia ha sido probada para D-optimización. La eficacia del nuevo algoritmo se muestra a lo largo de diferentes ejemplos. El capítulo 3 se dedica al estudio de los experimentos con mezclas. El capítulo comienza definiendo este tipo de problemas y la región de diseño donde tienen sentido, el simplex. A continuación se describen los diseños de mezclas estándar que han recibido mayor atención en la literatura, así como los modelos más utilizados para explicar este tipo de comportamientos. En particular, se hace especial hincapié sobre los polinomios canónicos de Scheffé. La última sección es una revisión de los trabajos más destacados sobre diseños óptimos para modelos de mezclas. El capítulo 4 comienza justificando la necesidad de desarrollar técnicas generales para resolver problemas de diseño óptimo en experimentos con mezclas. En este trabajo se proponen dos algoritmos para la construcción de diseños D-óptimos exactos en este tipo de problemas. El primero de ellos consiste en extender el algoritmo multiplicativo para una clase de diseños restringidos, los diseños de permutación. Este nuevo algoritmo permite resolver problemas de mezclas considerando modelos no lineales, aunque no permite abordar problemas de mezclas con restricciones. Como alternativa heurística se propone otro método, basado en algoritmos genéticos, capaz de obtener soluciones en problemas restringidos que también puede utilizarse para modelos no lineales. El desarrollo de técnicas de construcción de los diseños óptimos para esta clase de modelos no han sido estudiados en la literatura en este contexto. Varios ejemplos que surgen en la industria farmacéutica, química y petroquímica ilustran los resultados obtenidos por las nuevas metodologías. El capítulo 5 contiene diferentes estrategias para la construcción de diseños D- e I-óptimos robustos exactos y continuos para modelos de mezclas. El desarrollo de estas estrategias viene motivado por el ejemplo que aparece en Hernández et al. (2008) en el que se busca la composición óptima de un sucedáneo que simule al diésel en el autoencendido bajo condiciones de motor HCCI. El uso de la metodología desarrollada en este capítulo permite abordar la falta de especificidad del modelo que presenta este problema. En primer lugar se analizó el problema de mezclas binaras y se obtuvieron resultados teóricos que permitieron obtener la expresión analítica de los diseños D-óptimos. Para más de dos ingredientes, se proporciona un algoritmo general basado en algoritmos genéticos, ya que no es posible tratar el problema de manera analítica. Por otro lado, se propone una nueva familia de diseños restringidos, los diseños intercambiables, que presentan buenas propiedades como generadores de los diseños óptimos robustos. El capítulo 6 es una síntesis de los resultados y aportaciones obtenidas a lo largo de la realización de este trabajo. En la última parte se presenta una discusión sobre las líneas de investigación futuras

Face-to-Face or Online Learning in Applied Statistics in Health Sciences? Failed Experiment or Opportunity after COVID-19?

The rapid spread of the COVID-19 worldwide led to the migration of the traditional education system based on the face-to-face classroom into an improvised online system, among many other preventive measures. Thus, all teaching methods had to be adapted to this new modality. This work is aimed at studying the viability of the online teaching of the subject of Applied Statistics in Health Sciences in higher education based on the teaching experience lived during COVID-19. In addition to this, possible technological difficulties and COVID-19-derived problems were investigated. A retrospective observational cross-sectional study was performed to analyze the students&rsquo; satisfaction according to the teaching methodologies in both face-to-face and online modalities. An exploratory and inferential analysis revealed that online teaching is feasible for the subject under study, although face-to-face learning still continues to significantly revert in favor of the quality of teaching. Therefore, further research is required to develop new online teaching methods given the feasibility of the proposal found in this research. Most of the students reported not having technological learning difficulties, whether related to their connectivity or technological resources, which did not have a significative impact on their teaching perception. Despite the psychological sequalae of COVID-19, this did not affect the students&rsquo; teaching satisfaction

Diseño óptimo robusto para experimentos con mezclas

Los experimentos con mezclas ocupan un lugar importante en las ciencias experimentales. Muchas investigaciones persiguen determinar las proporciones de los ingredientes de una mezcla que describen las características de sus productos de manera óptima. En esta etapa, el diseño óptimo de Experimentos (OED) juega un papel fundamental. Sin embargo, en general existe poca información disponible sobre la adecuación del modelo antes de llevar a cabo los experimentos y los diseños óptimos dependen fuertemente de esta elección. En este trabajo se proponen diseños óptimo robustos para mezclas binarias y ternarias donde la respuesta puede variar en vecindario del modelo considerado en un sentido minimax. Las técnicas de construcción de Daemi y Wiens (2013) han sido adaptadas a este tipo de problemas y se ha desarrollado una metodología general basada en algoritmos genéticos. Se propone además una clase de diseños restringidos, fáciles de implementar, que suponen un gran ahorro computacional

Optimal-robust selection of a fuel surrogate for homogeneous charge compression ignition modeling.

Homogeneous Charge Compression Ignition (HCCI) combustion is a potential candidate for dealing with the stringent regulations on vehicle emissions while still providing very good energy efficiency. Despite the promising results obtained in preliminary studies, the lack of autoignition control has delayed its launch in the engine industry. In the development of the HCCI concept, the availability of reliable computer models has proved extremely valuable, due to their flexibility and lower cost compared with experiments using real engines. In order to obtain the best formulation of a fuel surrogate formulated with n-heptane, toluene and cyclohexane that efficiently estimate the autoignition behaviour, regression adjustments are made to the Root-Mean-Square Errors (RMSE) of experimental Starts of Combustion (SOC) from the modeled SOC. The canonical form of the Scheffé polynomials is widely used to fit the data from mixture experiments, however the experimenter might have only partial knowledge. In this paper we present the adaptation of the robust methodology for possibly misspecified blending model and an algorithm to obtain tailor-made optimal designs for mixture experiments, instead of using standard designs which are indiscriminately employed, to make good estimations of the parameters blending model. We maximize the determinant of the mean squared error matrix of the least square estimator over a realistic neighbourhood of the fitted regression mixture model. The maximized determinant is then minimized over the class of possible designs, yielding an optimal design. Thus, the computed desings are robust to the exact form of the true blending model. Standard mixture designs, as the simplex lattice, are around 25% efficient for estimation purposes compared with the designs obtained in this work when deviances from the considered model occur during the experiments. Once an optimal-robust design was selected (based on the level of certainty about model adequacy), we computed the optimal mixture that best reproduces the combustion property to be imitated. Optimal mixtures obtained when the considered model is inadequate agree with the results achieved in empirical studies, which validates the methodology proposed in this work

Efficient algorithms for constructing D- and I-optimal exact designs for linear and non-linear models in mixture experiments

The problem of finding optimal exact designs is more challenging than that of approximate optimal designs. In the present paper, we develop two efficient algorithms to numerically construct exact designs for mixture experiments. The first is a novel approach to the well-known multiplicative algorithm based on sets of permutation points, while the second uses genetic algorithms. Using (i) linear and non-linear models, (ii) D/- and I-optimality criteria, and (iii) constraints on the ingredients, both approaches are explored through several practical problems arising in the chemical, pharmaceutical and oil industry

Recent Advances in Robust Design for Accelerated Failure Time Models with Type I Censoring

Many fields including clinical and manufacturing areas usually perform life-testing experiments and accelerated failure time models (AFT) play an essential role in these investigations. In these models the covariate causes an accelerant effect on the course of the event through the term named acceleration factor (AF). Despite the influence of this factor on the model, recent studies state that the form of AF is weakly or partially known in most real applications. In these cases, the classical optimal design theory may produce low efficient designs since they are highly model dependent. This work explores planning and techniques that can provide the best robust designs for AFT models with type I censoring when the form of the AF is misspecified, which is an issue little explored in the literature. Main idea is focused on considering the AF to vary over a neighbourhood of perturbation functions and assuming the mean square error matrix as the basis for measuring the design quality. A key result of this research was obtaining the asymptotic MSE matrix for type I censoring under the assumption of known variance regardless the selected failure time distribution. In order to illustrate the applicability of previous result to a study case, analytical characterizations and numerical approaches were developed to construct optimal robust designs under different contaminating scenarios for a failure time following a log-logistic distribution

Recent Advances in Robust Design for Accelerated Failure Time Models with Type I Censoring

Many fields including clinical and manufacturing areas usually perform life-testing experiments and accelerated failure time models (AFT) play an essential role in these investigations. In these models the covariate causes an accelerant effect on the course of the event through the term named acceleration factor (AF). Despite the influence of this factor on the model, recent studies state that the form of AF is weakly or partially known in most real applications. In these cases, the classical optimal design theory may produce low efficient designs since they are highly model dependent. This work explores planning and techniques that can provide the best robust designs for AFT models with type I censoring when the form of the AF is misspecified, which is an issue little explored in the literature. Main idea is focused on considering the AF to vary over a neighbourhood of perturbation functions and assuming the mean square error matrix as the basis for measuring the design quality. A key result of this research was obtaining the asymptotic MSE matrix for type I censoring under the assumption of known variance regardless the selected failure time distribution. In order to illustrate the applicability of previous result to a study case, analytical characterizations and numerical approaches were developed to construct optimal robust designs under different contaminating scenarios for a failure time following a log-logistic distribution

El enfoque morfogenético y cuantitativo aplicado al estudio de las formas urbanas y la diversidad de usos: el caso de Toledo = The morphogenetic and quantitative approach applied to the study of urban forms and land-use mix: the case of Toledo

Tradicionalmente, la forma urbana se ha usado como medio para entender los espacios urbanos. A pesar de ello, este conocimiento se ha efectuado especialmente en cascos antiguos y grandes ciudades, y el alcance de sus enfoques y métodos ha sido muy limitado en planificación y diseño. En base a estas limitaciones el artículo plantea (a) la identificación y caracterización de formas urbanas de los nuevos paisajes urbanos contemporáneos apoyándose en un enfoque morfogenético, y (b) la evaluación de la influencia en la configuración espacial sobre la diversidad de usos apoyándose en el enfoque cuantitativo. Mientras que un enfoque morfogenético revela diferentes formas urbanas con relación a la configuración espacial de las manzanas, parcelas, edificios, calles, plazas y zonas verdes, el enfoque cuantitativo, apoyado en las técnicas spacemate, diagrama n y el modelo de mezcla urbana funcional nos informa de propiedades espaciales de las formas urbanas y, a través del análisis factorial e inferencial, nos ayuda a evaluar la influencia de dichas propiedades en la diversidad de usos. Este artículo contribuye al debate conceptual y combinación de métodos en el estudio del grado de influencia del espacio (forma urbana) en la actividad humana (diversidad de usos) sirviendo además para guiar la planificación y diseño urbanos. ----------ABSTRACT---------- Traditionally, urban form has been used to understand urban spaces. However, this knowledge has mainly focused on historic city centers and large cities, and the scope of their approaches and methods has been very limited in the planning practice and urban design. Based on these limitations, the article proposes (a) the identification andcharacterization of urban forms of new contemporary urban landscapes based on a morphogenetic approach, and (b) the evaluation of the influence of spatial configuration on land-use mix based on a quantitative approach. On the one hand, the morphogenetic approach reveals different urban forms in relation to the spatial configuration of blocks, plots, buildings, streets, plazas and green areas. On the other, the quantitative approach, supported by the spacemate technique, the n diagram and the functional urban mix model, firstly informs us of the spatial properties of urban forms and secondly, through factorial and inferential analyses, helps us to evaluate the influence of these properties on the diversity of uses. To sum up, this article contributes to the conceptual debate and combination of methods in the study of the degree of influence of space (urban form) on human activity (land use mix) but also serves to guide urban planning and design