Background: Advanced statistical modeling techniques may help predict health outcomes. However, it is not the case that these modeling techniques always outperform traditional techniques such as regression techniques. In this study, external validation was carried out for five modeling strategies for the prediction of the disability of community-dwelling older people in the Netherlands. Methods: We analyzed data from five studies consisting of community-dwelling older people in the Netherlands. For the prediction of the total disability score as measured with the Groningen Activity Restriction Scale (GARS), we used fourteen predictors as measured with the Tilburg Frailty Indicator (TFI). Both the TFI and the GARS are self-report questionnaires. For the modeling, five statistical modeling techniques were evaluated: general linear model (GLM), support vector machine (SVM), neural net (NN), recursive partitioning (RP), and random forest (RF). Each model was developed on one of the five data sets and then applied to each of the four remaining data sets. We assessed the performance of the models with calibration characteristics, the correlation coefficient, and the root of the mean squared error. Results: The models GLM, SVM, RP, and RF showed satisfactory performance characteristics when validated on the validation data sets. All models showed poor performance characteristics for the deviating data set both for development and validation due to the deviating baseline characteristics compared to those of the other data sets. Conclusion: The performance of four models (GLM, SVM, RP, RF) on the development data sets was satisfactory. This was also the case for the validation data sets, except when these models were developed on the deviating data set. The NN models showed a much worse performance on the validation data sets than on the development data sets.
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A culture change within an organization may be of importance in this turbulent world. An assessment of the current and desired cultural profiles can help estimate as to whether any changes are required. In this study the organizational culture of a housing association was examined from both the staff’s and external stakeholders’ perspectives. How does the current culture compare with the desired culture? Do the external stakeholders perceive the organization’s culture in a similar way? Do the staff’s and external stakeholders’ perceptions coincide with the organization’s intended image? The results demonstrate that the external stakeholders’ perceptions of the organizational culture in this case study are similar to those of the organization’s staff.
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Limited evidence is available about (non)-representativeness of participants in health-promoting interventions. The Dutch Healthy Primary School of the Future (HPSF)-study is a school-based study aiming to improve health through altering physical activity and dietary behaviour, that started in 2015 (registered in ClinicalTrials.gov on14-06-2016, NCT02800616). The study has a response rate of 60%. A comprehensive non-responder analysis was carried out, and responders were compared with schoolchildren from the region and the Netherlands using a cross-sectional design. External sources were consulted to collect non-responder, regional, and national data regarding relevant characteristics including sex, demographics, health, and lifestyle. The Chi-square test, Mann-Whitney U test, or Student's t-test were used to analyse differences.
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BACKGROUND: Prediction models and prognostic scores have been increasingly popular in both clinical practice and clinical research settings, for example to aid in risk-based decision making or control for confounding. In many medical fields, a large number of prognostic scores are available, but practitioners may find it difficult to choose between them due to lack of external validation as well as lack of comparisons between them.METHODS: Borrowing methodology from network meta-analysis, we describe an approach to Multiple Score Comparison meta-analysis (MSC) which permits concurrent external validation and comparisons of prognostic scores using individual patient data (IPD) arising from a large-scale international collaboration. We describe the challenges in adapting network meta-analysis to the MSC setting, for instance the need to explicitly include correlations between the scores on a cohort level, and how to deal with many multi-score studies. We propose first using IPD to make cohort-level aggregate discrimination or calibration scores, comparing all to a common comparator. Then, standard network meta-analysis techniques can be applied, taking care to consider correlation structures in cohorts with multiple scores. Transitivity, consistency and heterogeneity are also examined.RESULTS: We provide a clinical application, comparing prognostic scores for 3-year mortality in patients with chronic obstructive pulmonary disease using data from a large-scale collaborative initiative. We focus on the discriminative properties of the prognostic scores. Our results show clear differences in performance, with ADO and eBODE showing higher discrimination with respect to mortality than other considered scores. The assumptions of transitivity and local and global consistency were not violated. Heterogeneity was small.CONCLUSIONS: We applied a network meta-analytic methodology to externally validate and concurrently compare the prognostic properties of clinical scores. Our large-scale external validation indicates that the scores with the best discriminative properties to predict 3 year mortality in patients with COPD are ADO and eBODE.
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The pervasiveness of wearable technology has opened the market for products that analyse running biomechanics and provide feedback to the user. To improve running technique feedback should target specific running biomechanical key points and promote an external focus. Aim for this study was to define and empirically test tailored feedback requirements for optimal motor learning in four consumer available running wearables. First, based on desk research and observations of coaches, a screening protocol was developed. Second, four wearables were tested according to the protocol. Third, results were reviewed, and four experts identified future requirements. Testing and reviewing the selected wearables with the protocol revealed that only two less relevant running biomechanical key points were measured. Provided feedback promotes an external focus of the user. Tailoring was absent in all wearables. These findings indicate that consumer available running wearables have a potential for optimal motor learning but need improvements as well.
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Abstract: Clinicians find it challenging to engage with patients who engage in self-harm. Improving the self-efficacy of professionals who treat self-harm patients may be an important step toward accomplishing better treatment of self-harm. However, there is no instrument available that assesses the self-efficacy of clinicians dealing with self-harm. The aim of this study is to describe the development and validation of the Self-Efficacy in Dealing with Self-Harm Questionnaire (SEDSHQ). This study tests the questionnaire’s feasibility, test-retest reliability, internal consistency, content validity, construct validity (factor analysis and convergent validity) and sensitivity to change. The Self-Efficacy in Dealing with Self-Harm Questionnaire is a 27-item instrument which has a 3-factor structure, as found in confirmatory factor analysis. Testing revealed high content validity, significant correlation with a subscale of the Attitude Towards Deliberate Self-Harm Questionnaire (ADSHQ), satisfactory test-retest correlation and a Cronbach’s alpha of 0.95. Additionally, the questionnaire was able to measure significant changes after an intervention took place, indicating sensitivity to change. We conclude that the present study indicates that the Self-Efficacy in Dealing with Self-Harm Questionnaire is a valid and reliable instrument for assessing the level of self-efficacy in response to self-harm.
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This report describes "a framework that delivers insight into the tangible and intangible effects of a mobile (IT) system, before it is being implemented".
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Het doel van dit onderzoek is te onderzoeken onder welke omstandigheden en onder welke condities relatief moderne modelleringstechnieken zoals support vector machines, neural networks en random forests voordelen zouden kunnen hebben in medisch-wetenschappelijk onderzoek en in de medische praktijk in vergelijking met meer traditionele modelleringstechnieken, zoals lineaire regressie, logistische regressie en Cox regressie.
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Background: The aim of this study is to validate a newly developed nurses' self-efficacy sources inventory. We test the validity of a five-dimensional model of sources of self-efficacy, which we contrast with the traditional four-dimensional model based on Bandura's theoretical concepts. Methods: Confirmatory factor analysis was used in the development of the newly developed self-efficacy measure. Model fit was evaluated based upon commonly recommended goodness-of-fit indices, including the χ2 of the model fit, the Root Mean Square Error of approximation (RMSEA), the Tucker-Lewis Index (TLI), the Standardized Root Mean Square Residual (SRMR), and the Bayesian Information Criterion (BIC). Results: All 22 items of the newly developed five-factor sources of self-efficacy have high factor loadings (range .40-.80). Structural equation modeling showed that a five-factor model is favoured over the four-factor model. Conclusions and implications: Results of this study show that differentiation of the vicarious experience source into a peer- and expert based source reflects better how nursing students develop self-efficacy beliefs. This has implications for clinical learning environments: a better and differentiated use of self-efficacy sources can stimulate the professional development of nursing students.
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This study evaluates psychometric properties of the Individual Recovery Outcomes Counter (I.ROC) in a Dutch population of participants with a schizophrenia spectrum disorder (SSD). B. Esther Sportel1*† , Hettie Aardema1†, Nynke Boonstra2 , Johannes Arends1 , Bridey Rudd3 , Margot J. Metz4 , Stynke Castelein5 and Gerdina H.M. Pijnenborg6
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