Classifying nursing organization in wards in Norwegian hospitals: self-identification versus observation

<p>Abstract</p> <p>Background</p> <p>The organization of nursing services could be important to the quality of patient care and staff satisfaction. However, there is no universally accepted nomenclature for this organization. The objective of the current study was to classify general hospital wards based on data describing organizational practice reported by the ward nurse managers, and then to compare this classification with the name used in the wards to identify the organizational model (self-identification).</p> <p>Methods</p> <p>In a cross-sectional postal survey, 93 ward nurse managers in Norwegian hospitals responded to questions about nursing organization in their wards, and what they called their organizational models. K-means cluster analysis was used to classify the wards according to the pattern of activities attributed to the different nursing roles and discriminant analysis was used to interpret the solutions. Cross-tabulation was used to validate the solutions and to compare the classification obtained from the cluster analysis with that obtained by self-identification. The bootstrapping technique was used to assess the generalizability of the cluster solution.</p> <p>Results</p> <p>The cluster analyses produced two alternative solutions using two and three clusters, respectively. The three-cluster solution was considered to be the best representation of the organizational models: 32 team leader-dominated wards, 23 primary nurse-dominated wards and 38 wards with a hybrid or mixed organization. There was moderate correspondence between the three-cluster solution and the models obtained by self-identification. Cross-tabulation supported the empirical classification as being representative for variations in nursing service organization. Ninety-four per cent of the bootstrap replications showed the same pattern as the cluster solution in the study sample.</p> <p>Conclusions</p> <p>A meaningful classification of wards was achieved through an empirical cluster solution; this was, however, only moderately consistent with the self-identification. This empirical classification is an objective approach to variable construction and can be generally applied across Norwegian hospitals. The classification procedure used in the study could be developed into a standardized method for classifying hospital wards across health systems and over time.</p

Occurrence and Treatment of Bone Atrophic Non-Unions Investigated by an Integrative Approach

Recently developed atrophic non-union models are a good representation of the clinical situation in which many nonunions develop. Based on previous experimental studies with these atrophic non-union models, it was hypothesized that in order to obtain successful fracture healing, blood vessels, growth factors, and (proliferative) precursor cells all need to be present in the callus at the same time. This study uses a combined in vivo-in silico approach to investigate these different aspects (vasculature, growth factors, cell proliferation). The mathematical model, initially developed for the study of normal fracture healing, is able to capture essential aspects of the in vivo atrophic non-union model despite a number of deviations that are mainly due to simplifications in the in silico model. The mathematical model is subsequently used to test possible treatment strategies for atrophic non-unions (i.e. cell transplant at post-osteotomy, week 3). Preliminary in vivo experiments corroborate the numerical predictions. Finally, the mathematical model is applied to explain experimental observations and identify potentially crucial steps in the treatments and can thereby be used to optimize experimental and clinical studies in this area. This study demonstrates the potential of the combined in silico-in vivo approach and its clinical implications for the early treatment of patients with problematic fractures