The association between parenting behavior and somatization in adolescents explained by physiological responses in adolescents

Introduction: This study adds to the knowledge on somatization in adolescents by exploring its relation with parenting behavior and the mediating/moderating role of physiological responses in adolescents to parenting behavior. Method: Eighteen adolescents with high and 18 adolescents with low somatization scores and their mothers completed a discussion task, from which observed parenting behavior scores were derived. Skin conductance in adolescents was measured before and during the discussion. Results: For adolescents with high levels of physiological responses, unadaptive parenting was related to a higher chance of high somatization scores. For low physiologically responsive adolescents, the relation between parenting behavior and somatization was not significant. Conclusion: Parenting behavior is not univocally related to somatization in adolescents, but the association depends on physiological responses in adolescents. (C) 2014 Elsevier B.V. All rights reserved

Drastische levertijdreductie door meer-niveau afstemming

no abstrac

How Advanced Change Patterns Impact the Process of Process Modeling

Process model quality has been an area of considerable research efforts. In this context, correctness-by-construction as enabled by change patterns provides promising perspectives. While the process of process modeling (PPM) based on change primitives has been thoroughly investigated, only little is known about the PPM based on change patterns. In particular, it is unclear what set of change patterns should be provided and how the available change pattern set impacts the PPM. To obtain a better understanding of the latter as well as the (subjective) perceptions of process modelers, the arising challenges, and the pros and cons of different change pattern sets we conduct a controlled experiment. Our results indicate that process modelers face similar challenges irrespective of the used change pattern set (core pattern set versus extended pattern set, which adds two advanced change patterns to the core patterns set). An extended change pattern set, however, is perceived as more difficult to use, yielding a higher mental effort. Moreover, our results indicate that more advanced patterns were only used to a limited extent and frequently applied incorrectly, thus, lowering the potential benefits of an extended pattern set

Investigating the trade-off between the effectiveness and efficiency of process modeling

Despite recent efforts to improve the quality of process models, we still observe a significant dissimilarity in quality between models. This paper focuses on the syntactic condition of process models, and how it is achieved. To this end, a dataset of 121 modeling sessions was investigated. By going through each of these sessions step by step, a separate ‘revision’ phase was identified for 81 of them. Next, by cutting the modeling process off at the start of the revision phase, a partial process model was exported for these modeling sessions. Finally, each partial model was compared with its corresponding final model, in terms of time, effort, and the number of syntactic errors made or solved, in search for a possible trade-off between the effectiveness and efficiency of process modeling. Based on the findings, we give a provisional explanation for the difference in syntactic quality of process models

The origin and evolution of syntax errors in simple sequence flow models in BPMN

How do syntax errors emerge? What is the earliest moment that potential syntax errors can be detected? Which evolution do syntax errors go through during modeling? A provisional answer to these questions is formulated in this paper based on an investigation of a dataset containing the operational details of 126 modeling sessions. First, a list is composed of the different potential syntax errors. Second, a classification framework is built to categorize the errors according to their certainty and severity during modeling (i.e., in partial or complete models). Third, the origin and evolution of all syntax errors in the dataset are identified. This data is then used to collect a number of observations, which form a basis for future research

Development and internal validation of a machine learning prediction model for low back pain non-recovery in patients with an acute episode consulting a physiotherapist in primary care

BACKGROUND: While low back pain occurs in nearly everybody and is the leading cause of disability worldwide, we lack instruments to accurately predict persistence of acute low back pain. We aimed to develop and internally validate a machine learning model predicting non-recovery in acute low back pain and to compare this with current practice and 'traditional' prediction modeling. METHODS: Prognostic cohort-study in primary care physiotherapy. Patients (n = 247) with acute low back pain (≤ one month) consulting physiotherapists were included. Candidate predictors were assessed by questionnaire at baseline and (to capture early recovery) after one and two weeks. Primary outcome was non-recovery after three months, defined as at least mild pain (Numeric Rating Scale > 2/10). Machine learning models to predict non-recovery were developed and internally validated, and compared with two current practices in physiotherapy (STarT Back tool and physiotherapists' expectation) and 'traditional' logistic regression analysis. RESULTS: Forty-seven percent of the participants did not recover at three months. The best performing machine learning model showed acceptable predictive performance (area under the curve: 0.66). Although this was no better than a'traditional' logistic regression model, it outperformed current practice. CONCLUSIONS: We developed two prognostic models containing partially different predictors, with acceptable performance for predicting (non-)recovery in patients with acute LBP, which was better than current practice. Our prognostic models have the potential of integration in a clinical decision support system to facilitate data-driven, personalized treatment of acute low back pain, but needs external validation first

Change Patterns in Use: A Critical Evaluation

Process model quality has been an area of considerable research efforts. In this context, the correctness-by-construction principle of change patterns provides promising perspectives. However, using change patterns for model creation imposes a more structured way of modeling. While the process of process modeling (PPM) based on change primitives has been investigated, little is known about this process based on change patterns. To obtain a better understanding of the PPM when using change patterns, the arising challenges, and the subjective perceptions of process designers, we conduct an exploratory study. The results indicate that process designers face little problems as long as control-flow is simple, but have considerable problems with the usage of change patterns when complex, nested models have to be created. Finally, we outline how effective tool support for change patterns should be realized.This research is supported by Austrian Science Fund (FWF): P23699-N23.Weber, B.; Pinggera, J.; Torres Bosch, MV.; Reichert, M. (2013). Change Patterns in Use: A Critical Evaluation. En Enterprise, Business-Process and Information Systems Modeling, BPMDS 2013. Springer Verlag. 261-276. https://doi.org/11007/978-3-642-38484-4_19S26127

From Expert Discipline to Common Practice: A Vision and Research Agenda for Extending the Reach of Enterprise Modeling

The benefits of enterprise modeling (EM) and its contribution to organizational tasks are largely undisputed in business and information systems engineering. EM as a discipline has been around for several decades but is typically performed by a limited number of people in organizations with an affinity to modeling. What is captured in models is only a fragment of what ought to be captured. Thus, this research note argues that EM is far from its maximum potential. Many people develop some kind of model in their local practice without thinking about it consciously. Exploiting the potential of this “grass roots modeling” could lead to groundbreaking innovations. The aim is to investigate integration of the established practices of modeling with local practices of creating and using model-like artifacts of relevance for the overall organization. The paper develops a vision for extending the reach of EM, identifies research areas contributing to the vision and proposes elements of a future research Agenda