Currently no standard or universal outcome measure for return to work (RTW) programs exists making the evaluation and comparison of such programs difficult. RTW outcomes are often measured using nominal scales based on administrative data but these fail to take the perspectives of workers and other stakeholders into consideration. In order to gain that perspective this study was conducted to identify what outcomes are of interest and importance to RTW stakeholders. RTW stakeholders identified indicators of successful RTW in order to develop a conceptual framework of successful RTW.
A total of 24 RTW stakeholders participated, representing both RTW consumers and providers from Southwestern Ontario.
This study used a mixed-method integrated form of concept mapping, which qualitatively generates and interprets data, and quantitatively analyzes data using multidimensional scaling and hierarchical cluster analysis.
Participants generated 48 statements, which were subsequently clustered into the following six concepts; worker performance, worker job satisfaction, human rights, worker well-being, seamless RTW process through collaborative communication, and satisfaction of stakeholders other than workers.
The results reflect the perspectives of stakeholders and suggest that RTW outcome measures are needed that not only evaluate all aspects of the worker's life, but the RTW process as well. Aside from confirming the inadequacy of nominal, administrative type outcomes, these findings imply that the actual RTW process is intimately tied to outcome. Implications and relevance are discussed for planning RTW programs and towards developing a RTW outcome tool.
Geographical differences in the frequencies of eight common surnames in Jutland (Denmark) are analysed using data from telephone directories of 121 exchanges. All the names showed a significant geographical surplus variance, which was divided into trend and patchiness components reflecting the history of the names. The surplus variance of surnames with restricted areas of origin was dominated by a large trend component; for surnames with an originally more even distribution, the patchiness component was dominant. The patterns of distribution were affected by processes which modelled natural selection with linkage disequilibrium. The combined patchiness within the clusters of exchanges gave information about patterns of local migration and level of social integration in the communities. Areas situated in boundary regions show high levels of patchiness.
We studied the isonymic structure of the Republic of Sakha (Yakutia), in the Russian Federation, using the surname distributions of 491,259 citizens above 18 years registered as residents in 2002. These were distributed in 35 districts and 497 towns and settlements of the Republic. The number of different surnames was 44,625. Matrices of isonymic distances between the 35 districts were tested for correlation with the geographic distance between the population centers of gravity of the districts. We found that, for the whole of Yakutia, Nei's distance was correlated with geographic distance (r = 0.693 +/- 0.027). A dendrogram of the 35 districts was built from the distance matrix, using the UPGMA method. The clusters identified by the dendrogram correlate with the geographic position of the districts. The correlation of random inbreeding calculated from isonymy, F(ST), with latitude was positive and highly significant but weak (r = 0.23). So, inbreeding was highest in the Arctic districts, and lowest in the South. Average alpha for 497 towns was 107, for 35 districts it was 311, and for the Republic 433. The value of alpha was higher for Russian than for the local languages. The geographical distribution of alpha, high in the Center and South-East and lower in the North-West, is compatible with the settlement of groups of migrants moving from the South-East toward the center and the North of Yakutia. It is proposed that low-density demic diffusion of human populations results in high inbreeding and may have been a general phenomenon in the early phases of human radiations.
Using the Mantel-Haenszel (MH) Procedure, we analyzed data for 7,087 American and 4,022 Russian Grade 8 students from the Third International Mathematics and Science Study (TIMSS) to compare mathematics achievement in the two countries on each of the 124 multiple-choice items. The results of the analyses indicate that the performance of the students on individual multiple-choice mathematics items vary by country. The results also suggest that the relationship between country and item performance differ as a function of content area. A total score of a country's achievement does not provide the whole picture of achievement dynamics; it averages out potentially important information on student achievement and the causes of their performance relative to other countries. The dynamics of achievement across countries will not be revealed unless the analyses are done at the item level.
Data on the frequencies of all (50 412) surnames in a total population of 849 399 people have been treated by various methods of multivariate statistics (cluster analysis, multidimensional scaling, and factor analysis) to show that 22 district populations of the Central Chernozem region of Russia form a definite, ordered system of population groups. All raions (administrative districts) of Belgorod oblast (administrative region) have been grouped into four clusters corresponding to the actual geographic locations of the populations. Districts of the eastern cluster are characterized by very specific spectrum and frequencies of surnames; districts of the western cluster differ in a high proportion of Ukrainian surnames.
Sport Medicine Centre, Roger Jackson Centre for Health and Wellness Research, Faculty of Kinesiology, Community Health Sciences, Faculty of Medicine, University of Calgary, Calgary, Alberta, Canada. firstname.lastname@example.org
In the field of sport medicine and injury prevention in sport, prospective study designs implementing cluster randomization or grouping of subjects by cluster (ie, team, clinic, school, community) are becoming increasingly common. However, there are very few published studies in the field that adequately account for clustering effects in the design and analysis, leading to potentially spurious conclusions. This paper will review the implications of using a cluster RCT or other intervention or observational design grouping individuals by cluster and to highlight the practical implications of appropriate analysis considering the effects of clustering.
Previously published papers have provided a foundation of expertise to discuss the often neglected impact of ignoring the effects of cluster in the design and analysis of cluster RCT and other study designs that group individuals by cluster in sport medicine.
The loss of statistical efficiency inherent when a study design implements randomization or grouping by cluster is reviewed. Specifically, the effect of cluster design on sample size considerations and analysis are discussed in the context of data from a recently published cluster RCT examining the effectiveness of a balance training prevention strategy in youth basketball.
Researchers in sport medicine are encouraged and challenged to consider appropriate research design and analytical techniques more consistently when study subjects function in the context of a cluster in order to avoid spurious results and misleading conclusions.
The first cancer maps of Finland based on small geographical areas, municipalities (mean population 5000 inhabitants), were drawn by the Finnish Cancer Registry in late 1950s. Since then several cancer maps based on larger administrative units, such as counties or central hospital districts, have been produced. Because of the heterogeneity of large administrative areas in terms of way of life and possible cancer risk determinants, different methods were tried to portray the geographical pattern of cancer incidence by municipality. Two major problems were encountered: (1) because of the small numbers of cases per municipality the random variation was disturbingly large when single municipality-specific rates were presented and (2) the areas of municipalities with largest populations (cities) were so small that these most important points were hardly visible on the map. After the development of computerized mapping programs, a method based on smoothed averages of municipality-based cancer incidences was selected for the Atlas of Cancer Incidence in Finland 1953-82. These maps are combinations of municipality-specific observation and the background illustrating the average cancer incidence in different parts of the country. Because of the weighting by population, a large town whose rate deviates from the level of the surrounding areas is more visible on the map than a small municipality which differs from its background in the same way. The maps show the present situation for total cancer and for the 20 most interesting specific cancer sites using a 21-color scale. In addition some comparisons are illustrated, for example geographical time trends, male/female differences, and urban/rural variations.(ABSTRACT TRUNCATED AT 250 WORDS)
The goal of this study is to investigate entity recognition within Electronic Health Records (EHRs) focusing on Spanish and Swedish. Of particular importance is a robust representation of the entities. In our case, we utilized unsupervised methods to generate such representations.
The significance of this work stands on its experimental layout. The experiments were carried out under the same conditions for both languages. Several classification approaches were explored: maximum probability, CRF, Perceptron and SVM. The classifiers were enhanced by means of ensembles of semantic spaces and ensembles of Brown trees. In order to mitigate sparsity of data, without a significant increase in the dimension of the decision space, we propose the use of clustered approaches of the hierarchical Brown clustering represented by trees and vector quantization for each semantic space.
The results showed that the semi-supervised approaches significantly improved standard supervised techniques for both languages. Moreover, clustering the semantic spaces contributed to the quality of the entity recognition while keeping the dimension of the feature-space two orders of magnitude lower than when directly using the semantic spaces.
The contributions of this study are: (a) a set of thorough experiments that enable comparisons regarding the influence of different types of features on different classifiers, exploring two languages other than English; and (b) the use of ensembles of clusters of Brown trees and semantic spaces on EHRs to tackle the problem of scarcity of available annotated data.