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Background Patient enablement is a concept developed to measure quality in primary health care. The comparative analysis of patient enablement in an international context is lacking. Objective To explain variation in patient enablement between patients, general practitioners (GPs) and countries. To find independent variables associated with enablement. Design We constructed multi‐level logistic regression models encompassing variables from patient, GP and country levels. The proportions of explained variances at each level and odds ratios for independent variables were calculated. Setting and Participants A total of 7210 GPs and 58 930 patients in 31 countries were recruited through the Quality and Costs of Primary Care in Europe (QUALICOPC) study framework. In addition, data from the Primary Health Care Activity Monitor for Europe (PHAMEU) study and Hofstede's national cultural dimensions were combined with QUALICOPC data. Results In the final model, 50.6% of the country variance and 18.4% of the practice variance could be explained. Cultural dimensions explained a major part of the variation between countries. Several patient‐level and only a few practice‐level variables showed statistically significant associations with patient enablement. Structural elements of the relevant health‐care system showed no associations. From the 20 study hypotheses, eight were supported and four were partly supported. Discussion and Conclusions There are large differences in patient enablement between GPs and countries. Patient characteristics and patients’ perceptions of consultation seem to have the strongest associations with patient enablement. When comparing patient‐reported measures as an indicator of health‐care system performance, researchers should be aware of the influence of cultural elements.

Innovation and entrepreneurship are the most important catalysts of dynamism in market economies. While it is known that entrepreneurial activities are locally embedded, mutual effects of entrepreneurs and their local regional environment have not been adequately addressed in the existing literature. In this article, we use agent‐based simulation experiments to investigate the role of entrepreneurship in the emergence of regional industrial clusters. We present fundamental extensions to the Simulating Knowledge Dynamics in Innovation Networks model (Ahrweiler et al., Industry and Labor Dynamics: The Agent‐based Computational Economics Approach; World Scientific: Singapore, 2004; pp 284–96) by using a multilevel modeling approach. We analyze the effects of changing entrepreneurial character of regions on the development industrial clusters in two simultaneously simulated regions. We find that an increase in the entrepreneurship of one region has a negative effect on the other region due to competition for factors of production and innovative outputs. The major policy implication of this finding is the limitation it posits on regional innovation and development policies that aspire to support clusters in similar areas of industrial specialization. © 2014 Wiley Periodicals, Inc. Complexity 19: 14–29, 2014

Even if the literature on the effects of pupil composition has been extensive, no clear consensus has been reached concerning the significance and magnitude of this effect. The first objective of this article is to estimate the magnitude of the school composition effect in primary schools (6th grade) in French-speaking Belgium. Different indicators of school composition are used: academic, socio-cultural, 'language' and sex composition. Except for sex composition, the results show that the school composition effect explains significant amount of between schools variance even after controlling for pupils' initial performance, socio-cultural background, and non-cognitive dispositions. The second objective is to examine covariance between school composition and several organisational variables and their joint effect on school performance. The second set of analyses is intended to question the conceptual nature of the school composition effect, establishing whether it is direct or indirect.

BACKGROUND: There is growing interest in designing cities that support not only walking, but also cycling. Bike Score® is a metric capturing environmental characteristics associated with cycling that is now available for over 160 US and Canadian cities. Our aim was to determine if Bike Score was associated with between and within-city variability in cycling behavior. METHODS: We used linear regression to model associations between Bike Score and journey to work cycling mode share (US: American Community Survey, 2013 or 2012 5-year estimates; Canada: 2011 National Household Survey) for 5664 census tracts in 24 US and Canadian cities. RESULTS: At the city level, the correlation between mean Bike Score and mean journey to work cycling mode share was moderate (r = 0.52). At the census tract level, the correlation was 0.35; a ten-unit increase in Bike Score was associated with a 0.5 % (95 % CI: 0.5 to 0.6) increase in the proportion of population cycling to work, a meaningful difference given the low modal shares (mean = 1.9 %) in many North American cities. Census tracts with the highest Bike Scores (>90 to 100) had mode shares 4.0 % higher (β = 4.0, 95 % CI: 2.9 to 5.0) than the lowest Bike Score areas (0–25). City specific analyses indicated between-city variability in associations, with regression estimates between Bike Score and mode share ranging from 0.2 to 3.5 %. CONCLUSIONS: The Bike Score metric was associated bicycle mode share between and within cities, suggesting its utility for planning bicycle infrastructure.

National Basketball Association (NBA) athletes experience a high rate of injuries. Injury prevention requires identifying observable and controllable risk factors.   To examine the relationship among game load, fatigue, and injuries in NBA athletes.   Cross-sectional study.   Game statistics and injury reports over 3 NBA seasons (2012-2015).   Data represented 627 players (height = 200.7 ± 8.9 cm, mass = 100.6 ± 12.1 kg, NBA experience = 4.8 ± 4.2 years, pre-NBA experience = 3.2 ± 1.9 years), 73 209 games, and 1663 injury events.   An injury event was defined as a player missing or leaving a game due to injury. Logistic multilevel regression was used to predict injuries from time-lagged fatigue and game load with between-subjects differences explained by demographic variables.   The odds of injury increased by 2.87% ( P < .001) for each 96 minutes played and decreased by 15.96% ( P < .001) for each day of rest. Increases in game load increased injury odds by 8.23% ( P < .001) for every additional 3 rebounds and 9.87% ( P < .001) for every additional 3 field-goal attempts. When fatigue and game load were held constant, injury odds increased by 3.03% ( P = .04) for each year of NBA experience and 10.59% ( P = .02) for a 6-cm decrease in height. I observed variability in the intercepts ( P < .001) and the slopes for minutes, rest, field-goal attempts, and rebounds (all P < .001).   Injuries were associated with greater fatigue and game load, more years of NBA experience, and being shorter than average. Both baseline injury risk and the magnitude of the load-injury and fatigue-injury associations varied across individuals. Researchers should explore the nature of these relationships.

Multi-level modeling is currently regaining attention in the database and software engineering community with different emerging proposals and implementations. One driver behind this trend is the need to reduce model complexity, a crucial aspect in a time of analytics in Big Data that deal with complex heterogeneous data structures. So far no standard exists for multi-level modeling. Therefore, different formalization approaches have been proposed to address multi-level modeling and verification in different frameworks and tools. In this article, we present an approach that integrates the formalization, implementation, querying, and verification of multi-level models. The approach has been evaluated in an open-source F-Logic implementation and applied in a large-scale data interoperability project in the oil and gas industry. The outcomes show that the framework is adaptable to industry standards, reduces the complexity of specifications, and supports the verification of standards from a software engineering point of view.

This article examines the extent to which culturally endorsed transformational leadership theories (CLTs) and the sustainability of society, both considered societal level institutional indicators, impact the emergence of social entrepreneurship. Using 107,738 individual-level responses from 27 countries for the year 2009 obtained from the Global Entrepreneurship Monitor (GEM) survey, and supplementing with country-level data obtained from Global Leadership and Organizational Behavior Effectiveness (GLOBE) and Sustainability Society Foundation (SSF), our findings from multilevel analysis show that transformational CLTs and sustainability conditions of society positively influence the likelihood of individuals becoming social entrepreneurs. Further, the effectiveness of transformational CLTs matters more for social entrepreneurship when the sustainability of society is low, which suggests the interaction between cultural leadership styles and societal sustainability. This article contributes to comparative entrepreneurship research by introducing strong cultural antecedents of social entrepreneurship in transformational CLTs and societal sustainability. We discuss various implications and limitations of our study, and we suggest directions for future research.

The need for real-time collaborative solutions in model-driven engineering has been increasing over the past years. Conflict-free replicated data types (CRDT) provide scalable and robust replication mechanisms that align well with the requirements of real-time collaborative environments. In this paper, we propose a real-time collaborative multi-level modeling framework to support advanced modeling scenarios, built on a collection of custom CRDT, specifically tailored for the needs of modeling environments. We demonstrate the benefits of the framework through an illustrative modeling case and compare it with other state-of-the-art modeling frameworks.

Entrepreneurship and innovation create a positive impact on the economy and society. Globally, governments invest resources to support new ventures and facilitate innovation. In this study, we examine this phenomenon by studying the pathway that goes from individual entrepreneur to innovation impact. We measure the effect of entrepreneurial motives on different types of innovations, with a particular focus on its amplification by formal and informal institutional conditions. Specifically, we use multi-level models to analyze annual data of 29 countries for 2006 to 2018. We find that opportunity-driven entrepreneurs are associated with higher levels of radical innovation, breakthrough innovation, and disruptive innovation. Better tax policies and less bureaucracy amplify this positive effect on radical innovation and breakthrough innovation. For necessity-driven entrepreneurs, the regulatory quality amplifies the positive effect on radical innovation and disruptive innovation, while monetary resources dampen this effect. Our findings show that the differences in innovation impact can be explained by differences in entrepreneurial motives and their specific interactions with formal and informal institutional conditions.Plain English SummaryThe rise of new business ventures has a positive impact on innovation and on the economy. This positive impact comes in different forms and shapes, such as business ventures introducing new products in new markets, creating new products for existing customers, or repositioning existing products for new consumers. Governments also play an important role through their policies and rules, shaping the conditions that influence the entrepreneurial process. In this study, we argue that entrepreneurs are driven by opportunity or necessity. The main implication for policy is that the two types of entrepreneurs react differently to governmental policies and conditions, and therefore also behave differently when it comes to introducing or creating new products in new or existing markets.

PurposeTemporal leadership is a new construct that predicts team outcomes. This study examines the mediating role of shared temporal cognitions and the moderating role of time pressure in the relationship between temporal leadership and project success within sustainable construction projects.Design/methodology/approachThe multi-source and multi-wave data were collected via self-administered questionnaires from teams working on sustainable construction projects. The direct and mediating hypotheses were tested using multi-level structural equation modelling, while moderated mediation hypotheses were examined by applying the bootstrap method using SPSS Process Macro.FindingsThe results showed that temporal leadership enables project success via shared temporal cognitions. Temporal leadership is most beneficial for facilitating project success via shared temporal cognitions when teams experience high time pressure.Originality/valueThis is the first study examining shared temporal cognitions as a mediator of the relationship between temporal leadership and project success. Also, this is the first study that considered time pressure as a boundary condition that influences the relationships between temporal leadership, shared temporal cognitions and project success within sustainable construction projects. The study provides valuable advice to project managers and project-based construction organizations about using and managing time within projects.