Validación de la versión española del cuestionario sobre la práctica basada en la evidencia en enfermería

Background: The lack of adequate instruments prevents the possibility of assessing the competence of health care staff in evidence-based decision making and further, the identification of areas for improvement with tailored strategies. The aim of this study is to report about the validation process in the Spanish context of the Evidence-Based Practice Questionnaire (EBPQ) from Upton y Upton. Methods: A multicentre, cross-sectional, descriptive psychometric validation study was carried out. For cultural adaptation, a bidirectional translation was developed, accordingly to usual standards. The measuring model from the questionnaire was undergone to contrast, reproducing the original structure by Exploratory Factorial Analysis (EFA) and Confirmatory Factorial Analysis (CFA), including the reliability of factors. Results: Both EFA (57.545% of total variance explained) and CFA (chi2=2359,9555; gl=252; p<0.0001; RMSEA=0,1844; SRMR=0,1081), detected problems with items 7, 16, 22, 23 and 24, regarding to the original trifactorial version of EBPQ. After deleting some questions, a reduced version containing 19 items obtained an adequate factorial structure (62.29% of total variance explained), but the CFA did not fit well. Nevertheless, it was significantly better than the original version (chi2=673.1261; gl=149; p<0.0001; RMSEA=0.1196; SRMR=0.0648). Conclusions: The trifactorial model obtained good empiric evidence and could be used in our context, but the results invite to advance with further refinements into the factor “attitude”, testing it in more contexts and with more diverse professional profiles.

Effective diagnosis of Alzheimer's disease by means of large margin-based methodology.

BACKGROUND Functional brain images such as Single-Photon Emission Computed Tomography (SPECT) and Positron Emission Tomography (PET) have been widely used to guide the clinicians in the Alzheimer's Disease (AD) diagnosis. However, the subjectivity involved in their evaluation has favoured the development of Computer Aided Diagnosis (CAD) Systems. METHODS It is proposed a novel combination of feature extraction techniques to improve the diagnosis of AD. Firstly, Regions of Interest (ROIs) are selected by means of a t-test carried out on 3D Normalised Mean Square Error (NMSE) features restricted to be located within a predefined brain activation mask. In order to address the small sample-size problem, the dimension of the feature space was further reduced by: Large Margin Nearest Neighbours using a rectangular matrix (LMNN-RECT), Principal Component Analysis (PCA) or Partial Least Squares (PLS) (the two latter also analysed with a LMNN transformation). Regarding the classifiers, kernel Support Vector Machines (SVMs) and LMNN using Euclidean, Mahalanobis and Energy-based metrics were compared. RESULTS Several experiments were conducted in order to evaluate the proposed LMNN-based feature extraction algorithms and its benefits as: i) linear transformation of the PLS or PCA reduced data, ii) feature reduction technique, and iii) classifier (with Euclidean, Mahalanobis or Energy-based methodology). The system was evaluated by means of k-fold cross-validation yielding accuracy, sensitivity and specificity values of 92.78%, 91.07% and 95.12% (for SPECT) and 90.67%, 88% and 93.33% (for PET), respectively, when a NMSE-PLS-LMNN feature extraction method was used in combination with a SVM classifier, thus outperforming recently reported baseline methods. CONCLUSIONS All the proposed methods turned out to be a valid solution for the presented problem. One of the advances is the robustness of the LMNN algorithm that not only provides higher separation rate between the classes but it also makes (in combination with NMSE and PLS) this rate variation more stable. In addition, their generalization ability is another advance since several experiments were performed on two image modalities (SPECT and PET).