Background: The present study investigates the suitability of various treatment outcome indicators to evaluate performance of mental health institutions that provide care to patients with severe mental illness. Several categorical approaches are compared to a reference indicator (continuous outcome) using pretest-posttest data of the Health of Nation Outcome Scales (HoNOS). Methods: Data from 10 institutions and 3189 patients were used, comprising outcomes of the first year of treatment by teams providing long-term care. Results: Findings revealed differences between continuous indicators (standardized pre-post difference score ES and ΔT) and categorical indicators (SEM, JTRCI, JTCS, JTRCI&CS, JTrevised) on their ranking of institutions, as well as substantial differences among categorical indicators; the outcome according to the traditional JT approach was most concordant with the continuous outcome indicators. Conclusions: For research comparing group averages, a continuous outcome indicator such as ES or ΔT is preferred, as this best preserves information from the original variable. Categorical outcomes can be used to illustrate what is accomplished in clinical terms. For categorical outcome, the classical Jacobson-Truax approach is preferred over the more complex method of Parabiaghi et al. with eight outcome categories. The latter may be valuable in clinical practice as it allows for a more detailed characterization of individual patients.
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The importance of leadership is increasingly recognized in relation to digital transformation. Therefore, middle management and top management must have the competencies required to lead such a transformation. The purpose of this paper is to investigate the relationship between the digital leader competencies as set out by the European e-competence framework (e-CF) and the digital transformation of organizations. Also, the relationship between digital leadership competency (DLC) and IT capability is examined. An empirical investigation is presented based on a sample of 433 respondents, analyzed using PLS-SEM. The results strongly support our hypotheses. DLC has a strong impact on organizational digital transformation. A post-hoc analysis showed this is predominantly the case for the e-CF competencies of business plan development, architecture design, and innovating while business change management and governance do not seem to affect organizational digital transformation. This is the first empirical study to conceptualize, operationalize and validate the concept of DLC, based on the e-competence framework, and its impact on digital transformation. These findings have significant implications for researchers and practitioners working on the transformation toward a digital organization.
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The paper summarizes two models for engineering education, as discussed in earlier papers. The first model (Corporate Curriculum) aims to bring Industry into the school, while the second model (I3) intends to bring the school into Industry. The contribution of the presented models to the Bologna Declaration and to the Renaissance Engineer idea are discussed.
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The swift enhancement of technology has affected the business environment as higher education alone no longer plays a definitive role in the employment process. To meet the emerging requirements of employers, individuals, specifically students, need to gain more entrepreneurial tendencies. The aim of this study is to investigate the factors affecting the entrepreneurial intention (EI) of university students. In order to do so, five constructs (EI, individual entrepreneurial orientation (IEO), self-efficacy, environmental support, and knowledge sharing) and their items taken from existing literature were used within the proposed model, and the constructed hypotheses were evaluated using structural equation modelling (SEM). Based on the model, a survey was distributed to 332 students of various universities.Self-efficacy and IEO are expected to be the prime factors affecting EI, whereas environmental support and knowledge sharing are expected to have more of an indirect effect on EI. Overall, this study will help establish the influencers of EI among university students.
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From Pubmed: " BACKGROUND: Antigen-specific immunotherapy (AIT) is a promising therapeutic approach for both cow's milk allergy (CMA) and peanut allergy (PNA), but needs optimization in terms of efficacy and safety. AIM: Compare oral immunotherapy (OIT) and subcutaneous immunotherapy (SCIT) in murine models for CMA and PNA and determine the dose of allergen needed to effectively modify parameters of allergy. METHODS: Female C3H/HeOuJ mice were sensitized intragastrically (i.g.) to whey or peanut extract with cholera toxin. Mice were treated orally (5 times/week) or subcutaneously (3 times/week) for three consecutive weeks. Hereafter, the acute allergic skin response, anaphylactic shock symptoms and body temperature were measured upon intradermal (i.d.) and intraperitoneal (i.p.) challenge, and mast cell degranulation was measured upon i.g. challenge. Allergen-specific IgE, IgG1 and IgG2a were measured in serum at different time points. Single cell suspensions derived from lymph organs were stimulated with allergen to induce cytokine production and T cell phenotypes were assessed using flow cytometry. RESULTS: Both OIT and SCIT decreased clinically related signs upon challenge in the CMA and PNA model. Interestingly, a rise in allergen-specific IgE was observed during immunotherapy, hereafter, treated mice were protected against the increase in IgE caused by allergen challenge. Allergen-specific IgG1 and IgG2a increased due to both types of AIT. In the CMA model, SCIT and OIT reduced the percentage of activated Th2 cells and increased the percentage of activated Th1 cells in the spleen. OIT increased the percentage of regulatory T cells (Tregs) and activated Th2 cells in the MLN. Th2 cytokines IL-5, IL-13 and IL-10 were reduced after OIT, but not after SCIT. In the PNA model, no differences were observed in percentages of T cell subsets. SCIT induced Th2 cytokines IL-5 and IL-10, whereas OIT had no effect. CONCLUSION: We have shown clinical protection against allergic manifestations after OIT and SCIT in a CMA and PNA model. Although similar allergen-specific antibody patterns were observed, differences in T cell and cytokine responses were shown. Whether these findings are related to a different mechanism of AIT in CMA and PNA needs to be elucidated."
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Big data analytics received much attention in the last decade and is viewed as one of the next most important strategic resources for organizations. Yet, the role of employees' data literacy seems to be neglected in current literature. The aim of this study is twofold: (1) it develops data literacy as an organization competency by identifying its dimensions and measurement, and (2) it examines the relationship between data literacy and governmental performance (internal and external). Using data from a survey of 120 Dutch governmental agencies, the proposed model was tested using PLS-SEM. The results empirically support the suggested theoretical framework and corresponding measurement instrument. The results partially support the relationship of data literacy with performance as a significant effect of data literacy on internal performance. However, counter-intuitively, this significant effect is not found in relation to external performance.
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Although governments are investing heavily in big data analytics, reports show mixed results in terms of performance. Whilst big data analytics capability provided a valuable lens in business and seems useful for the public sector, there is little knowledge of its relationship with governmental performance. This study aims to explain how big data analytics capability led to governmental performance. Using a survey research methodology, an integrated conceptual model is proposed highlighting a comprehensive set of big data analytics resources influencing governmental performance. The conceptual model was developed based on prior literature. Using a PLS-SEM approach, the results strongly support the posited hypotheses. Big data analytics capability has a strong impact on governmental efficiency, effectiveness, and fairness. The findings of this paper confirmed the imperative role of big data analytics capability in governmental performance in the public sector, which earlier studies found in the private sector. This study also validated measures of governmental performance.
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This article presents findings from an empirical study on the relations between the variables comprising learning mechanisms in small collaborative groups. Variables comprising the central learning mechanisms component were task related interactions, knowledge elaborations, and subjective estimation of knowledge acquisition. Student related determinants of the learning processes were dysfunctional behavior, and work group management. Tutor related determinants were tutor facilitative behavior (facilitation, questioning, and mini-lecturing), and tutor work group management. The selected variables were structured into a path model. The first question concerned the way the three variables comprising the learning mechanisms component are related to each other. We have contrasted mediation vs. addition hypothesis. The second question concerned the validity of the complete small group functioning model. In order to answer these questions we have constructed a questionnaire and administered it to 89 1st year medical students. Concerning the first question, the data were in accordance with the mediation hypothesis. Specific analyses showed that only the collaborative sequence type of task related interactions had positive effects on both knowledge elaborations and knowledge acquisition. In contrast the transfer of knowledge type of interactions had negative effects on the same variables. Test of the validity of the complete model showed a satisfactory model fit. Briefly, our findings suggest that a large proportion of collaborative sequences in the group, together with a low proportion of dysfunctional behavior and highly facilitative behavior of the tutor will lead to a high proportion of knowledge elaborations. Knowledge elaborations positively affect knowledge acquisition which is an important determinant of the student satisfaction and motivation.
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Entrepreneurs are likely to be victims of ransomware. Previous studies have found that entrepreneurs tend to adopt few preventive measures, thereby increasing their chances of victimization. Due to a lack of research, however, not much is known about why entrepreneurs lack self-protective behaviors and how they can be encouraged to change said behaviors. Therefore, the purpose of this study is to explain, by means of an extended model of the Protection Motivation Theory (PMT), the motivation for entrepreneurs using protective measures against ransomware in the future. The data for our study were collected thanks to a questionnaire that was answered by 1,020 Dutch entrepreneurs with up to 250 employees. Our Structural Equation Modelling (SEM) analysis revealed that entrepreneurs are more likely to take preventive measures against ransomware if they perceive the risk of ransomware as severe (perceived severity), if they perceive their company as being vulnerable (perceived vulnerability), if they are concerned about the risks (affective response), and if they think that the people and companies around them expect them to apply preventive measures (subjective norms). However, if entrepreneurs think that they are capable of handling the risk (self-efficacy) and are convinced that their adopted preventive measures are effective (response efficacy), they are less likely to take preventive measures. Furthermore, for entrepreneurs that outsource IT security, the significant effect of perceived vulnerability and subjective norms disappears. The likelihood of entrepreneurs protecting their business against ransomware is thus influenced by a complex interplay of various motivational factors and is partly dependent on the business’ characteristics. Based on these findings, we will discuss security professionals’ prospects for increasing the cyber resilience of entrepreneurs, thus preventing cybercrime victimization.
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Objectives The aim of this study was to examine the reciprocal association between work–family conflict and depressive complaints over time. Methods Cross-lagged structural equation modeling (SEM) was used and three-wave follow-up data from the Maastricht Cohort Study with six years of follow-up [2416 men and 585 women at T1 (2008)]. Work–family conflict was operationalized by distinguishing both work–home interference and home–work interference, as assessed with two subscales of the Survey Work–Home Interference Nijmegen. Depressive complaints were assessed with a subscale of the Hospital Anxiety and Depression scale. Results The results showed a positive cross-lagged relation between home–work interference and depressive complaints. The results of the χ 2 difference test indicated that the model with cross-lagged reciprocal relationships resulted in a significantly better fit to the data compared to the causal (Δχ 2 (2)=9.89, P=0.001), reversed causation model (Δχ 2 (2)=9.25, P=0.01), and the starting model (Δχ 2 (4)=16.34, P=0.002). For work–home interference and depressive complaints, the starting model with no cross-lagged associations over time had the best fit to the empirical data. Conclusions The findings suggest a reciprocal association between home–work interference and depressive complaints since the concepts appear to affect each other mutually across time. This highlights the importance of targeting modifiable risk factors in the etiology of both home–work interference and depressive complaints when designing preventive measures since the two concepts may potentiate each other over time.
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