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Discriminant validity assessment has become a generally accepted prerequisite for analyzing relationships between latent variables. For variance-based structural equation modeling, such as partial least squares, the Fornell-Larcker criterion and the examination of cross-loadings are the dominant approaches for evaluating discriminant validity. By means of a simulation study, we show that these approaches do not reliably detect the lack of discriminant validity in common research situations. We therefore propose an alternative approach, based on the multitrait-multimethod matrix, to assess discriminant validity: the heterotrait-monotrait ratio of correlations. We demonstrate its superior performance by means of a Monte Carlo simulation study, in which we compare the new approach to the Fornell-Larcker criterion and the assessment of (partial) cross-loadings. Finally, we provide guidelines on how to handle discriminant validity issues in variance-based structural equation modeling.

Background The medication process is complex and error-prone. To avoid medication errors, a medication order should fulfil certain criteria, such as good readability and comprehensiveness. In this context, a computerized physician order entry (CPOE) system can be helpful. This study aims to investigate the distinct effects on the quality of prescription documentation of a CPOE system implemented on general wards in a large tertiary care hospital. Methods In a retrospective analysis, the prescriptions of two groups of 160 patients each were evaluated, with data collected before and after the introduction of a CPOE system. According to nationally available recommendations on prescription documentation, it was assessed whether each prescription fulfilled the established 20 criteria for a safe, complete, and actionable prescription. The resulting fulfilment scores (prescription- Fscores ) were compared between the pre-implementation and the post-implementation group and a multivariable analysis was performed to identify the effects of further covariates, i.e., the prescription category, the ward, and the number of concurrently prescribed drugs. Additionally, the fulfilment of the 20 criteria was assessed at an individual criterion-level (denoted criteria- Fscores ). Results The overall mean prescription- Fscore increased from 57.4% ± 12.0% (n = 1850 prescriptions) before to 89.8% ± 7.2% (n = 1592 prescriptions) after the implementation ( p  < 0.001). At the level of individual criteria, criteria- Fscores significantly improved in most criteria (n = 14), with 6 criteria reaching a total score of 100% after CPOE implementation. Four criteria showed no statistically significant difference and in two criteria, criteria- Fscores deteriorated significantly. A multivariable analysis confirmed the large impact of the CPOE implementation on prescription- Fscores which was consistent when adjusting for the confounding potential of further covariates. Conclusions While the quality of prescription documentation generally increases with implementation of a CPOE system, certain criteria are difficult to fulfil even with the help of a CPOE system. This highlights the need to accompany a CPOE implementation with a thorough evaluation that can provide important information on possible improvements of the software, training needs of prescribers, or the necessity of modifying the underlying clinical processes.

Recognizing evaluation results as a crucial source of information to support RD&I management, this article introduces ‘AgroRadarEval’, an interactive tool aimed at fulfilling theoretical, conceptual, and practical gaps concerning the systematization of the use of evaluation results in agricultural RD&I. Aligned with the principles of Responsible Research and Innovation (RRI) and Responsible Research Assessment (RRA), AgroRadarEval aims to support leaders and managers of RD&I in reflecting on the strengths and weaknesses of organizational capacities, culture, collaborations, processes, and communications that underlie the use of evaluation results in agricultural RD&I. AgroRadarEval is built along three support pillars: Evaluation Capacity Building, Impact-Oriented Evaluation Culture, and Reflective Learning, and is operationalized through eight interconnected dimensions: 1. participation and collaboration, 2. skills development, 3. promotion of an evaluation culture, 4. continuous feedback and adaptation, 5. integration with strategic planning, 6. monitoring, 7. influences of the external environment, and 8. communication. This study describes the development of the tool, its characteristics, illustrating its application in an agricultural RD&I organization. The study is targeted at leaders and managers of agricultural RD&I, evaluators, and researchers interested in research evaluation and enhancing the impact of RD&I.

Recognizing evaluation results as a crucial source of information to support RD&I management, this article introduces 'AgroRadarEval’, an interactive tool aimed at fulfilling theoretical, conceptual, and practical gaps concerning the systematization of the use of evaluation results in agricultural RD&I. Aligned with the principles of Responsible Research and Innovation (RRI) and Responsible Research Assessment (RRA), AgroRadarEval aims to support leaders and managers of RD&I in reflecting on the strengths and weaknesses of organizational capacities, culture, collaborations, processes, and communications that underlie the use of evaluation results in agricultural RD&I. AgroRadarEval is built along three support pillars: Evaluation Capacity Building, Impact-Oriented Evaluation Culture, and Reflective Learning, and is operationalized through eight interconnected dimensions: 1. participation and collaboration, 2. skills development, 3. promotion of an evaluation culture, 4. continuous feedback and adaptation, 5. integration with strategic planning, 6. monitoring, 7. influences of the external environment, and 8. communication. This study describes the development of the tool, its characteristics, illustrating its application in an agricultural RD&I organization. The study is targeted at leaders and managers of agricultural RD&I, evaluators, and researchers interested in research evaluation and enhancing the impact of RD&I.

Purpose Continuous curvilinear capsulorhexis (CCC), as a prerequisite for successful cataract surgery, is one of the most important and difficult steps in phacoemulsification. In clinical practice, the size and circularity of the capsular tear and eccentricity with the lens are often employed as indicators to evaluate the effect of CCC. Methods We present a neural network-based model to improve the efficiency and accuracy of evaluation for capsulorhexis results. The capsulorhexis results evaluation model consists of the detection network based on U-Net and the nonlinear fitter built from fully connected layers. The detection network is responsible for detecting the positions of the round capsular tear and lens margin, and the nonlinear fitter is utilized to fit the outputs of the detection network and to compute the capsulorhexis results evaluation indicators. We evaluate the proposed model on an artificial eye phantom and compare its performance with the medical evaluation method. Results The experimental results show that the average detection error of the proposed evaluation model is within 0.04 mm. Compared with the medical method (the average detection error is 0.28 mm), the detection accuracy of the proposed evaluation model is more accurate and stable. Conclusion We propose a neural network-based capsulorhexis results evaluation model to improve the accuracy of evaluation for capsulorhexis results. The results of the evaluation experiments show that the proposed results evaluation model evaluates of the effect of capsulorhexis better than the medical evaluation method.

Background Increasing prevalence of childhood obesity and its related consequences emphasises the importance of developing and evaluating interventions aimed at prevention. The importance of process evaluation in health intervention research is increasingly recognised, assessing implementation and participant response, and how these may relate to intervention success or failure. A comprehensive process evaluation was designed and undertaken for the West Midlands ActiVe lifestyle and healthy Eating in School children (WAVES) study that tested the effectiveness of an obesity prevention programme for children aged 6-7 years, delivered in 24 UK schools. The four intervention components were: additional daily school-time physical activity (PA); cooking workshops for children and parents; Villa Vitality (VV), a 6-week healthy lifestyle promotion programme run by a local football club; and signposting to local PA opportunities. Methods Data relating to six dimensions (Fidelity, Reach, Recruitment, Quality, Participant Responsiveness, Context) were collected via questionnaires, logbooks, direct observations, focus groups and interviews. Multiple data collection methods allowed for data triangulation and validation of methods, comparing research observations with teacher records. The 6-stage WAVES study model ((i) Data collection, (ii) Collation, (iii) Tabulation, (iv) Score allocation and discussion, (v) Consultation, (vi) Final score allocation) was developed to guide the collection, assimilation and analysis of process evaluation data. Two researchers independently allocated school scores on a 5-point Likert scale for each process evaluation dimension. Researchers then discussed school score allocations and reached a consensus. Schools were ranked by total score, and grouped to reflect low, medium or high intervention implementation. Results The intervention was predominantly well-implemented and well-received by teachers, parents and children. The PA component was identified as the most challenging, VV the least. Median implementation score across schools was 56/75 (IQR, 51.0 - 60.8). Agreement between teacher logbooks and researcher observations was generally high, the main discrepancies occurred in session duration reporting where in some cases teachers’ estimations tended to be higher than researchers’. Conclusions The WAVES study model provides a rigorous and replicable approach to undertaking and analysing a multi-component process evaluation. Challenges to implementing school-based obesity prevention interventions have been identified which can be used to inform future trials. Trial registration ISRCTN97000586 . 19 May 2010.

This article describes how as internet technology continues to change and improve lives and societies worldwide, effective global information management becomes increasingly critical, and effective Internet information retrieval systems become more and more significant in providing Internet users worldwide with accurate and complete information. Search engine evaluation is an important research field as search engines directly determine the quality of information users' Internet searches. Relevance-decrease pattern/model plays an important role in search engine result evaluation. This research studies effective measurement of search results through investigating relevance-decrease patterns of search results from two popular search engines: Google and Bing. The findings can be applied to relevance-evaluation of search results from other information retrieval systems such as OPAC, can help make search engine evaluations more accurate and sound, and can provide global information management personnel with valuable insights.

Based on the connotation and process of innovation-driven development, we have developed a comprehensive evaluation system containing 20 indicators in five aspects, including innovation factors, innovation subjects, innovation environments, innovation outputs, and development performance, to measure the levels of innovation-driven development in Sichuan province. Selecting 21 cities and prefectures in Sichuan province as research objects, we evaluated and measured the innovation-driven development levels of each city and prefecture using the entropy weight method (EWM). According to the evaluation results, the 21 cities and prefectures were divided into four categories depending on their levels of innovation-driven development: advanced-level, high-level, medium-level, and low-level. The results show that there are obvious spatial differences in terms of innovation-driven development levels among cities and prefectures in Sichuan province. Specifically, Chengdu, Mianyang, Panzhihua, and Deyang cities rank among the top four cities because of their advanced and high levels of innovation-driven development, while other cities and prefectures are at the medium and low levels. We also analyzed the innovation-driven development policies and practices of cities and prefectures in Sichuan province, to provide guidance for implementing innovation-driven development strategies in the cities and prefectures in the future.

County-level highway, as a transport carrier to expand the external economic exchanges of rural areas, plays an important role as a hub in the rural road network system, and it is an important infrastructure for the country to promote the construction of new urbanization. In order to ensure the sustainable development of county-level highway, according to the relevant policies in China, the research on the investment effect evaluation of county-level highway projects has been regarded as a focus in the “14th Five-Year Plan”. Therefore, evaluating the investment benefits of the in-service county-level highways by identifying key evaluation factors has become an important research topic for new urbanization development. Since there is a lack of a comprehensive and reasonable evaluation index system. Therefore, this paper has taken the policy applicability and maximization of comprehensive benefits as the research goal, and started from five criteria, including new urbanization development ability, road network construction ability, economic benefits, social benefits and environmental benefits. Later, 14 second-level factors including industrial development ability, coverage of road network structure and national economic evaluation conclusion, etc., as well as a total of 58 third-level factors including the growth rate of the number of townships service industries and service industries, the increase of highway transport tools and regional urbanization growth rate, etc. were screened and constructed as the investment effect evaluation system. Subsequently, according to the characteristics of gray fuzzy of county-level highway indexes, this paper established a hybrid model applicable to the multi-attribute investment effect evaluation of new urbanization county-level highway, which used the game combination weighting method (GCWM), to combine the entropy power method and hierarchical analysis method to assign weights and reasonably calculate the comprehensive weights of indexes, and on this basis, it combined grey Euclid relational analysis method (GERAM) and the attribute theory to rank the correlation degrees of evaluation objects and further classify the investment effect level of the project. Finally, the hybrid model was applied to the county-level highway under the new urbanization construction demonstration counties to derive the optimal solution, as well as the optimization paths and measures for other auxiliary solutions. The results of this study provide an effective theoretical basis for the evaluation of the investment yield of rural transportation infrastructure with the support of policies, and provide a reliable practical method for the developing countries to achieve the maximum comprehensive benefits of the county-level highway investment programs with limited funds.