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Criterion measures are foundational to an effective selection and classification process and valid enlistment aptitude standards. The Department of Defense, when possible, considers eligibility standards based on empirical evidence of the relationship between recruit attributes and applicable performance to be best practice. Ensuring use and incorporation of appropriate criteria is critical to this process. However, this process is often complex and costly. Numerous policy related issues must be considered. This paper provides commentary on each of the technical papers included in this issue from a policy and operational perspective. [ABSTRACT FROM AUTHOR]

We introduce the Theory and Methods Special Issue on Precision Medicine and Individualized Policy Discovery. The issue consists of four discussion papers, grouped into two pairs, and sixteen regular research papers that cover many important lines of research on data-driven decision making. We hope that the many provocative and original ideas presented herein will inspire further work and development in precision medicine and personalization. [ABSTRACT FROM AUTHOR]

Criterion identification and measurement is often an afterthought in criterion-related validation research. Yet it is essential in determining what predictor measures to use in operational settings. Accordingly, this special issue discusses recent advances in addressing the "criterion problem" in U.S. military enlisted personnel selection and classification research. In this introductory paper, broad issues regarding criterion identification and measurement in the military and previous research on this topic are reviewed and subsequent papers, which address specific criterion issues and describe an unprecedented joint-service criterion project, are introduced. [ABSTRACT FROM AUTHOR]

One of the key aspects of digitalization brought by Industry 4.0 is driven by the Internet of Things. Perhaps, the most interesting component of digital transformation is Augmented Reality. Augmented reality is merging the digital and physical worlds in the same experience. This new technology is accepted and applications are initiated in various areas including manufacturing. The main usage area in manufacturing is planning, execution, and verification of the assembly and maintenance operations. Likewise, augmented reality is used in the training of inexperienced people and to guide them by remote expert support. However, the augmented reality application selection, especially for non-technology savvy manufacturing organizations is challenging. Detailed and time-consuming analyses are required to understand the key features to compare and select the most suitable augmented reality application. In this paper, the most critical nine augmented reality software selection criteria are identified. To help organizations to apply such criteria and make a selection that is most suitable for the enterprise, a multi criteria decision-making approach is suggested. The suggested method is based on a fuzzy spherical number. The complex proportional assessment method is used to calculate the rankings. Therefore, the suggested method is called SF-COPRAS. Moreover, the detailed definition of augmented reality features and terminologies is explained. Several augmented reality use-case scenarios are discussed for manufacturing organizations on their Industry 4.0 initiatives. This paper aims to guide decision makers on their augmented reality software selection journey. The offered framework aims to save time for investigating augmented reality solution features systematically and objectively. [ABSTRACT FROM AUTHOR]

One of the most prevalent problems in linear programming as one of the convenient models in the field of operation research environment is Data Envelopment Analysis (DEA), which supports the efficiency of Decision-Making Units (DMUs). Usually, accurate data are common; however, in the real world, we are facing an inaccurate situation. In this paper, a new model for assessing DMUs in a fuzzy environment is presented; we consider the inverse DEA model with the variable return to scale with fuzzy numbers for fluctuating data. A case study is given to illustrate its performance. [ABSTRACT FROM AUTHOR]

Socio-hydrology has expanded and been effective in exposing the hydrological community to ideas and approaches from other scientific disciplines, and social sciences in particular. Yet it still has much to explore regarding how to capture human agency and how to combine different methods and disciplinary views from both the hydrological and the social sciences to develop knowledge. A useful starting ground is noting that the complexity of human–water relations is due to interactions not only across spatial and temporal scales but also across different organizational levels of social systems. This calls for consideration of another analytical scale, the human organizational scale, and interdisciplinarity in study methods. Based on the papers published in this journal's Special Issue Advancing Socio-hydrology over 2019–2022, this paper illuminates how the understanding of coupled human–water systems can be strengthened by capturing the multi-level nature of human decision making and by applying an interdisciplinary multi-method approach. [ABSTRACT FROM AUTHOR]

Vaccine hesitancy is an ongoing barrier to achieve sufficient COVID-19 vaccination coverage. Although there are many studies globally of vaccine hesitancy based on large survey samples, there are fewer in-depth qualitative studies that explore vaccine hesitancy and acceptance as a spectrum of decision-making. In this paper, we begin to describe vaccination decision-making among 58 adults living in remote Alaska based on three waves of online surveys and follow-up semi-structured interviews conducted between November 2020 and November 2021. The survey question of intention was not a predictor of adoption for about one third of the interviewees who were unvaccinated when they took the survey (n=12, 35%). Over half of all interviewees (n=37, 64%) had vaccine-related concerns, including 25 vaccinated individuals (representing 57% of vaccinated interviewees). Most interviewees reported that they learned about COVID-19 vaccines through interpersonal interactions (n=30, 52%) and/or a variety of media sources (n=29, 50%). The major facilitators of acceptance were trust in the information source (n=20, 48% of the 42 who responded), and learning from the experiences of family, friends, and the broader community (n=12, 29%). Further, trust and having a sense of agency appears to be important to interviewee decision-making, regardless of vaccination status and intention. [ABSTRACT FROM AUTHOR]

Graduate medical education (GME) and Clinical Competency Committees (CCC) have been evolving to monitor trainee progression using competency-based medical education principles and outcomes, though evidence suggests CCCs fall short of this goal. Challenges include that evaluation data are often incomplete, insufficient, poorly aligned with performance, conflicting or of unknown quality, and CCCs struggle to organize, analyze, visualize, and integrate data elements across sources, collection methods, contexts, and time-periods, which makes advancement decisions difficult. Learning analytics have significant potential to improve competence committee decision making, yet their use is not yet commonplace. Learning analytics (LA) is the interpretation of multiple data sources gathered on trainees to assess academic progress, predict future performance, and identify potential issues to be addressed with feedback and individualized learning plans. What distinguishes LA from other educational approaches is systematic data collection and advanced digital interpretation and visualization to inform educational systems. These data are necessary to: 1) fully understand educational contexts and guide improvements; 2) advance proficiency among stakeholders to make ethical and accurate summative decisions; and 3) clearly communicate methods, findings, and actionable recommendations for a range of educational stakeholders. The ACGME released the third edition CCC Guidebook for Programs in 2020 and the 2021 Milestones 2.0 supplement of the Journal of Graduate Medical Education (JGME Supplement) presented important papers that describe evaluation and implementation features of effective CCCs. Principles of LA underpin national GME outcomes data and training across specialties; however, little guidance currently exists on how GME programs can use LA to improve the CCC process. Here we outline recommendations for implementing learning analytics for supporting decision making on trainee progress in two areas: 1) Data Quality and Decision Making, and 2) Educator Development. [ABSTRACT FROM AUTHOR]

Qualitative research provides an in-depth understanding of lived experiences. However, these experiences can be hard to apprehend by using just one method of data analysis. A good example is the experience of resilience. In this paper, the authors describe the chain of the decision-making process in the research of the construct of "resilience". s The authors justify the implications of a multi-method, pluralistic approach, and show how the triangulation of two or more qualitative methods and integration of several qualitative data analysis methods can improve a deeper understanding of the resilience among people with chronic pain. By combining the thematic analysis, narrative analysis, and critical incident technique, lived experiences can be seen from different perspectives.Therefore, the thematic analysis describes the content and answers to "what" regarding resilience, the narrative analysis describes the dynamics of resilience, and answers to "how", while the critical incident technique clarifies the most significant experience and the answers to "why" changes happen. This integrative approach could be used in the analysis of other psychological constructs and can serve as an example of how the rigour of qualitative research could be provided. [ABSTRACT FROM AUTHOR]

In order to improve the effect of precision intelligent agricultural decision support system, this paper combines big data technology to carry out precision mining of agricultural data, and combines decision tree algorithm to carry out data classification processing. Moreover, this paper obtains the most effective agricultural decision reference data through data mining, combines the agricultural decision support process to set the functional modules of the decision system, and analyzes the implementation process of each functional module. In addition, this paper studies the theoretical basis and key technologies of the agricultural production structure optimisation decision support system, and builds a precision and intelligent agricultural decision support system based on big data analysis. The system mainly performs accurate processing of agricultural data and makes effective predictions, and then makes scientific decision results. Finally, this paper verifies the structure of the model in this paper combined with experimental analysis. From the comparison of experiments, it can be seen that the precision and intelligent agricultural decision support system constructed in this paper has significant effects. [ABSTRACT FROM AUTHOR]

Although collaborative decision-making with multiple stakeholders in supply chain has become an important research issue in recent years, current research on the mechanism/process, methods, and performance assessment of collaborative decision-making still lags behind the practical needs in supply chain management. This paper reviews the existing literature in the past decade on collaborative supply chain from the perspective of decision-making in four stages with a causal framework. This paper reveals a general retrospect of the main subjects developed and investigated with related applications of cases, which includes both theoretical assumptions and practical data. The findings indicate that most articles in this field are based on a direct relationship between collaboration and performance improvement but lack of a mechanism between them. Additionally, dynamic collaboration with members in the whole supply chain both horizontally and vertically has not been conducted by previous studies. This paper provides a new conceptual framework and future research directions for further study in supply chain collaboration. [ABSTRACT FROM AUTHOR]

Saudi Arabia's Vision 2030 aims to reform health care across the Kingdom, with health technology assessment being adopted as one tool promising to improve the efficiency with which resources are used. An understanding of the opportunity costs of reimbursement decisions is key to fulfilling this promise and can be used to inform a cost-effectiveness threshold. This paper is the first to provide a range of estimates of this using existing evidence extrapolated to the context of Saudi Arabia. We use four approaches to estimate the marginal cost per unit of health produced by the healthcare system; drawing from existing evidence provided by a cross-country analysis, two alternative estimates from the UK context, and based on extrapolating a UK estimate using evidence on the income elasticity of the value of health. Consequences of estimation error are explored. Based on the four approaches, we find a range of SAR 42,046 per QALY gained (48% of GDP per capita) to SAR 215,120 per QALY gained (246% of GDP per capita). Calculated potential central estimates from the average of estimated health gains based on each source gives a range of SAR 50,000–75,000. The results are in line with estimates from the emerging literature from across the world. A cost-effectiveness threshold reflecting health opportunity costs can aid decision-making. Applying a cost-effectiveness threshold based on the range SAR 50,000 to 75,000 per QALY gained would ensure that resource allocation decisions in healthcare can in be informed in a way that accounts for health opportunity costs. A limitation is that it is not based on a within-country study for Saudi Arabia, which represents a promising line of future work. Healthcare in Saudi Arabia is undergoing wide-ranging reform through Saudi Arabia's Vision 2030. One aim of these reforms is to ensure that money spent on healthcare generates the most improvement in population health possible. To do this requires understanding the trade-offs that exist: funding one pharmaceutical drug means that same money is not available to fund another pharmaceutical drug. This is relevant whether the new drug would be funded from within the existing budget for healthcare or from an expansion of it. If the drugs apply to the same patient population and have the same price, the question is simply, "which one generates more health?" In reality, we need to compare pharmaceutical drugs for different diseases, patient populations, and at a range of potential prices to understand whether the drug in question would generate more health per riyal spent than what is currently funded by the healthcare system. This paper provides the first estimates of the amount of health, measured in terms of quality adjusted life years (QALYs), generated by the Saudi Arabian healthcare system. We find that the healthcare system generates health at a rate of one QALY produced for every 50,000–75,000 riyals spent (58–86% of GDP per capita). Using the range we estimate to inform cost-effectiveness threshold can aid decision-making. [ABSTRACT FROM AUTHOR]

Due to the workload and lack of a critical mass of trained operational researchers within their ranks, health systems and programmes may not be able to dedicate sufficient time to conducting operational research (OR). Hence, they may need the technical support of operational researchers from research/academic organisations. Additionally, there is a knowledge gap regarding implementing differentiated tuberculosis (TB) care in programme settings. In this 'how we did it' paper, we share our experience of implementing a differentiated TB care model along with an inbuilt OR component in Tamil Nadu, a southern state in India. This was a health system initiative through a collaboration of the State TB cell with the Indian Council of Medical Research institutes and the World Health Organisation country office in India. The learnings are in the form of eleven tips: four broad principles (OR on priority areas and make it a health system initiative, implement simple and holistic ideas, embed OR within routine programme settings, aim for long-term engagement), four related to strategic planning (big team of investigators, joint leadership, decentralised decision-making, working in advance) and three about implementation planning (conducting pilots, smart use of e-tools and operational research publications at frequent intervals). These may act as a guide for other Indian states, high TB burden countries that want to implement differentiated care, and for operational researchers in providing technical assistance for strengthening implementation and conducting OR in health systems and programmes (TB or other health programmes). Following these tips may increase the chances of i) an enriching engagement, ii) policy/practice change, and iii) sustainable implementation. [ABSTRACT FROM AUTHOR]

Supply chain uncertainty has become an important area of research, and it is crucial for many firms, and especially so for global firms. Decision makers find it difficult to make decisions due to lack of transparency in the supply chain and the impact of possible risk events. This paper aims to approach this topic by developing a conceptual model to assist logistics and supply chain managers to improve supply chain effectiveness by analysing risk events. To this end we use a case study based on a set of interviews with agents of a German firm and some of its suppliers from India, China and Europe, which generates insights uncaptured in previous research in the area. The main findings answer questions such as: 1. How to build an agile supply chain strategy with rapid planning and integrated execution in different stages?; 2. How to identify and avoid risk events as they increase the supply chain uncertainty and are multiplied when interrelated risk events occur simultaneously; 3. Can supply chain uncertainty be reduced by determining the degree of flexibility required to mitigate risk effects, reducing supply chain uncertainty and increasing the firm's dynamic capabilities?. [ABSTRACT FROM AUTHOR]

In the classification calculation, the data are sometimes not unique and there are different values and probabilities. Then, it is meaningful to develop the appropriate methods to make classification decision. To solve this issue, this paper proposes the machine learning methods based on a probabilistic decision tree (DT) under the multi-valued preference environment and the probabilistic multi-valued preference environment respectively for the different classification aims. First, this paper develops a data pre-processing method to deal with the weight and quantity matching under the multi-valued preference environment. In this method, we use the least common multiple and weight assignments to balance the probability of each preference. Then, based on the training data, this paper introduces the entropy method to further optimize the DT model under the multi-valued preference environment. After that, the corresponding calculation rules and probability classifications are given. In addition, considering the different numbers and probabilities of the preferences, this paper also uses the entropy method to develop the DT model under the probabilistic multi-valued preference environment. Furthermore, the calculation rules and probability classifications are similarly derived. At last, we demonstrate the feasibility of the machine learning methods and the DT models under the above two preference environments based on the illustrated examples. [ABSTRACT FROM AUTHOR]

This paper investigates agri-food value chain transparency and the role of institutional quality on agri-food processing firms' performance. The research collected primary data via survey to analyse 320 agri-food processing firms. This study empirically established that value chain transparency and institutional quality significantly improve agri-food processing firms' performance. Also, institutional quality positively moderates the relationship between value chain transparency and the performance of agri-food processing firms significantly. The empirical results in this paper imply that stakeholders in the existing and incoming agri-food processing firms should prioritise transparency and institutional quality in making managerial decisions to achieve higher performance in their value chain. The study's uniqueness is its contribution to value chain literature and also towards the development of the agri-food value chain sector to enhance performance in a developing economy. [ABSTRACT FROM AUTHOR]

For the past decade, collaboration has been the preferred method of devising land and water policy in Aotearoa New Zealand to achieve agreed outcomes. However, the use of collaboration in policymaking is at a crossroads, as some argue it is unrealistic to expect stakeholders and tangata whenua with competing interests to work in partnership on contentious policy issues. To help clarify the future direction of collaboration, this paper provides authorising agencies (those with responsibility for the policy problems) with prompts to reflect ex ante on the advantages and disadvantages of pursuing collaboration. Based on a retrospective review of collaborations in New Zealand's land and water policymaking, and a two-day symposium with collaboration practitioners, we identify situations that are potentially less suited to collaboration owing to factors that are challenging to overcome through process design. Four issues that affect the success of collaborations are identified: the nature of the policy problem, the personal and collective qualities of those involved, power relationships, and the collaborative process. Finally, we draw together practical lessons from history, the symposium, and the literature to develop a list of questions for authorising agencies to use to reflect on the advantages and disadvantages of pursuing collaboration. [ABSTRACT FROM AUTHOR]

The paper explores factors of importance for young people's choice of upper secondary education and for their future life, from a student perspective. The paper builds on data from a questionnaire study, answered by 1,414 students in grade 9 (age 15–16) in Sweden, prior to their choice of upper secondary school. In addition, data from earlier studies, have been included. Results show that the majority plan to transit to further levels of education. There is, however, a group of students who opt to move directly into work. In relation to making an educational choice, the factors that dominate the decision-making processes of students are related to; interest and ability, wellbeing and fun, but also plans after upper secondary school. Views and choices among friends and adults outside the family, is of less importance. With regard to perceptions of a future life, what dominates students' thinking relates to their views of a secure and comfortable life. Comparisons between boys and girls and between students with parents born outside Sweden and students with native-born parents, show similarities as well as differences. Drawing on our results, we argue that career learning needs to be flexible and adapted to the students' different situations and needs. [ABSTRACT FROM AUTHOR]

In any organization, the capability, knowledge and skills play a significant role in success of an employee. For instant, in hospital management, the performance evaluation of an employee is mainly based on qualitative in nature. Evaluation of certain factors such as uncertainty, vagueness and imprecision is based on the decision making ability of the evaluator. The performance evaluation for promotion, incentives, bonuses, growth and development of any employee should be recognized in effective and efficiency manner with appropriate ratings. Therefore, this paper aims to design and implement a multi criteria performance evaluation for hospital employees to get promotion, incentives, bonus, growth and development. In this paper, we propose an Interval Valued Fuzzy Weighted Distance Algorithm (IVFWDA) for performance evaluation. The expected interval and the actual interval of the work achievement, management skill, personal quality, care, safety and risk management features are extracted by Interval Valued Fuzzy Soft Matrix (IVFSM) and it is effectively done using the proposed algorithm. An example is presented as a case study to illustrate the proposed algorithm. Implementation of the proposed algorithm in Maple is also discussed with sample computations. [ABSTRACT FROM AUTHOR]

COVID-19 has triggered an unprecedented health crisis, crippling economic activity around the world. The aim of this paper is to analyse European countries' vulnerability to the associated consequences. The analysis will focus on three areas that a priori are expected to be most severely affected by the pandemic – health, society and work – examining the possible relationship with countries' wealth. The multicriteria decision-making Technique for Order Preference by Similarity to the Ideal Solution (TOPSIS) will be used to generate a ranking of countries based on criteria that define each of these three areas. The findings will provide authorities with quantitative information to guide their aid policies. The results show that Eastern European countries should direct their resources towards addressing health-related and social issues. Conversely, those that have higher GDP per capita and that have been hardest hit by coronavirus will have to make changes to their labour systems in order to minimize the fallout. [ABSTRACT FROM AUTHOR]

Macroeconomic predicting is a research hotspot in the field of predicting. The accuracy of predicting often directly affects the rationality of decision-making, especially for defense expenditure predicting. This paper studies the residual correction method of prediction model based on time series. Firstly, based on the grey nonlinear Verhulst prediction model, Fourier series is introduced in this paper to correct the residual sequence once and establish a residual correction model. On this basis, this paper also introduces Markov related concepts, creatively introduces the two-dimensional residual data into Markov state transition matrix, classifies it by K-means clustering analysis, and calculates its parameters by PSO algorithm to realize the secondary accurate correction of residual. Finally, a PSO-Markov residual correction method based on Verhulst-Fourier model is proposed. Tested by examples, this method effectively improves the prediction accuracy of the model, and the prediction is more reliable and accurate. [ABSTRACT FROM AUTHOR]

In this article, firstly, we describe picture fuzzy N-soft sets (PFN-SSs) as a generalization of picture fuzzy sets (PFSs) and N-soft sets (N-SS) by observing that one of the essential concept of neutral grade is missing in intuitionistic fuzzy N-SS (IFN-SS) theory. The concept of neutrality grade can be observed in the situation when we encounter human views including more answers of type: yes, abstain, no, refusal. For instance, in election the election commission or election council issues voting papers for the candidate. The voting outcomes are categorized into 4 groups with the number of papers namely, vote for, abstain, vote against, and refusal voting. Further, We define the fundamental properties of PFN-SS and introduce M-subset, F-subset, compliment, intersections, unions, of PFN-SS and give their examples. Secondly, we define an algorithm to cope with PFN-SS data which is more generalized then the algorithm defined for IFN-SS. To show the advantage and usefulness of the defined technique, we give two examples from real life by utilizing PFN-SS data. The result shows in the comparison that our initiated method is more general and suitable than the IFN-SS, fuzzy N-SS (FN-SS), and N-SS. [ABSTRACT FROM AUTHOR]

Formal decision-making methods can be used to help improve the overall sustainability of industries and organisations. Recently, there has been a great proliferation of works aggregating sustainability criteria by using diverse multiple criteria decision-making (MCDM) techniques. A number of review papers summarising these techniques have been published. During the past few years, new approaches for hybrid MCDM (HMCDM) methods have been developed, but they have not yet been completely reviewed. This article aims to fill this gap and to summarise publications related to the application of HMCDM. The current study is limited solely to papers available in the Thomson Reuters Web of Science Core Collection database. The main findings report that HMCDM methods have been increasingly applied for supporting decisions in different domains of sustainability. The most frequently used methods emphasise the advantages of hybrid approaches over individual methods, and we conclude that they can assist decision-makers in handling information such as stakeholders’ preferences, interconnected or contradictory criteria, and uncertain environments. The main contribution of this work is identifying hybrid approaches as improvements for decision-making related to sustainability issues, while also promoting future application of the approaches. [ABSTRACT FROM PUBLISHER]

Climate change is an important driver of migration, but little research exists on whether migrant communities in the U.S. identify climate change-related factors as reasons for migrating. In 2021, we conducted a multidisciplinary, collaborative project to better understand the nexus of climate change and immigrant health in the Atlanta area. This paper presents one arm of this collaboration that explored both the role of climate change in decisions to immigrate to Georgia and the ways that climate change intersects with other possible drivers of migration. First generation migrants from Latin America were recruited primarily through CPACS Cosmo Health Center and were invited to participate in an intake survey and an in-depth interview. Results were analyzed using descriptive statistics and thematic analysis. Findings suggest that while participants may not have explicitly identified climate change as a primary reason for migration, in both surveys and in-depth interviews, participants reported multiple and intersecting social, economic, political, and environmental factors that are directly or indirectly influenced by climate change and that are involved in their decisions to migrate. The narratives that emerged from in-depth interviews further contextualised survey data and elucidated the complex nexus of climate change, migration, and health. [ABSTRACT FROM AUTHOR]

The project 'Quality Decision-making by women and providers' (QUALI-DEC) combines four non-clinical interventions to promote informed decision-making surrounding mode of birth, improve women's birth experiences, and reduce caesarean sections among low-risk women. QUALI-DEC is currently being implemented in 32 healthcare facilities across Argentina, Burkina Faso, Thailand, and Viet Nam. In this paper, we detail implementation processes and the planned process evaluation, which aims to assess how and for whom QUALI-DEC worked, the mechanisms of change and their interactions with context and setting; adaptations to intervention and implementation strategies, feasibility of scaling-up, and cost-effectiveness of the intervention. We developed a project theory of change illustrating how QUALI-DEC might lead to impact. The theory of change, together with on the ground observations of implementation processes, guided the process evaluation strategy including what research questions and perspectives to prioritise. Main data sources will include: 1) regular monitoring visits in healthcare facilities, 2) quantitative process and output indicators, 3) a before and after cross-sectional survey among post-partum women, 4) qualitative interviews with all opinion leaders, and 5) qualitative interviews with postpartum women and health workers in two healthcare facilities per country, as part of a case study approach. We foresee that the QUALI-DEC process evaluation will generate valuable information that will improve interpretation of the effectiveness evaluation. At the policy level, we anticipate that important lessons and methodological insights will be drawn, with application to other settings and stakeholders looking to implement complex interventions aiming to improve maternal and newborn health and wellbeing. Trial registration: ISRCTN67214403. [ABSTRACT FROM AUTHOR]

Evidence-based decision making often relies on meta-analyzing multiple studies, which enables more precise estimation and investigation of generalizability. Integrative analysis of multiple heterogeneous studies is, however, highly challenging in the ultra high-dimensional setting. The challenge is even more pronounced when the individual-level data cannot be shared across studies, known as DataSHIELD contraint. Under sparse regression models that are assumed to be similar yet not identical across studies, we propose in this paper a novel integrative estimation procedure for data-Shielding High-dimensional Integrative Regression (SHIR). SHIR protects individual data through summary-statistics-based integrating procedure, accommodates between-study heterogeneity in both the covariate distribution and model parameters, and attains consistent variable selection. Theoretically, SHIR is statistically more efficient than the existing distributed approaches that integrate debiased LASSO estimators from the local sites. Furthermore, the estimation error incurred by aggregating derived data is negligible compared to the statistical minimax rate and SHIR is shown to be asymptotically equivalent in estimation to the ideal estimator obtained by sharing all data. The finite-sample performance of our method is studied and compared with existing approaches via extensive simulation settings. We further illustrate the utility of SHIR to derive phenotyping algorithms for coronary artery disease using electronic health records data from multiple chronic disease cohorts. [ABSTRACT FROM AUTHOR]

This paper presents a machine-learning-based method for evaluating the internal value of talent in any organization and for evaluating the salary criteria. The study assumes the design and development of a salary predictor, based on artificial intelligence technologies, to help determine the internal value of employees and guarantee internal equity in the organization. The aim of the study is to achieve internal equity, which is a critical element a that directly affects employees' motivation. We implemented and validated the method with 130 employees and more than 70 talent acquisition cases with a Basque technology research organization during the years 2021 and 2022. The proposed method is based on statistical data assessment and machine-learning-based regression. We found that while most organizations have established variables for job evaluation as well as salary increments for staff according to their contribution to the organization, only a few employ tools to support equitable internal compensation. This study presents a successful real case of artificial intelligence applications where machine learning techniques help managers make the most equitable and least biased salary decisions possible, based on data. [ABSTRACT FROM AUTHOR]

The aim of this paper is to decide on heavy metal levels based on ecological parameters by effectively eliminating common disadvantages such as high cost and serious time-consuming laboratory procedures via an effective artificial intelligence approach. Therefore, this study is hinged on an artificial intelligence technique, ANN, because of its low cost and high accuracy in overcoming the mentioned limitations and obstacles in the determination process of the amounts of elements. The ANNs have thus been employed to determine essential heavy metals, such as Fe, Mn, and Zn depending on Ca, K, and Mg concentrations of soil samples obtained from different altitudes in Mount Ida. To the best knowledge of the authors, this is the first study in the literature in which altitude was considered as a parameter in the prediction of nutrient heavy metals. The computed relative errors are significantly low for each of the considered elements (Fe, Mn, and Zn); and are found to be between 1.0-4.1%, 1.0-4.2%, 1.5-7.1%, respectively, for the training, testing, and holdout data. The findings indicate that the relative errors could still be decreased further by assuming the altitude as a factor variable. [ABSTRACT FROM AUTHOR]

Shared decision making (SDM) has been acknowledged in the last decade. Literature has shown that when physicians are engaged in SDM and form a relationship with their patients, there is higher patient satisfaction of care. Moreover, SDM has been reported to improve patient outcomes and clinical measures. Despite this clear benefit of implementing SDM into clinical practice, there is little evidence for including SDM learning into preclinical medical education. We integrated an exercise for second year medical students to practice the steps of shared decision making. In this paper, the quantitative and qualitative results from a survey of medical students following the SDM learning exercise will be discussed. Students were more educated regarding SDM after this exercise and were motivated to use it in their future clinical careers. They also expressed overall positive attitudes towards SDM tools such as decision aids. Feedback to improve this SDM learning experience included the use of standardized patients, and to expand such education to the clinical environment training. This research provides a model of SDM practice integration into medical education. Similar programs can be beneficial for the development of SDM and other interpersonal skills. [ABSTRACT FROM AUTHOR]

This is a conceptual paper, which contributes to the debate on the measure of intergenerational sacrifice and advances a post-growth theoretical framework that becomes intelligible within a wider space of deliberation, at once economic, social, and moral. We explore the three-pronged argument in two parts. First, we examine the implications of the socio-economic mechanism of the transition of the production mode from extractive to generative, which is envisaged to replace the market automatic response by allocative efficiency. A second part tackles the moral reason underlying decision-making. Drawing on the lessons of the post-growth debate, we propose and defend a working definition of the moral reason of intergenerational justice that should also underpin our rationalizing about other issues of similar importance regarding the obligations we owe to future people, such as passing on languages or containing epidemics. [ABSTRACT FROM AUTHOR]

The significance of Institutional Effectiveness is pivotal to the functioning of an academic institution. The mushrooming of private institutions in the Indian higher education space necessitates exploring its antecedents to ensure quality higher education is imparted by the institution. The purpose of this research endeavor is to explore the impact of Academic Leadership on Institutional Effectiveness with a mediating role of Campus Culture and Faculty Involvement in Decision Making. The study employed a cross-sectional research design and implemented a purposive sampling technique to collet primary data from 80 permanent faculties and 249 students of private engineering colleges located in the Karnataka state of India. Data was collected through a structured questionnaire and analyzed using Partial Least Square-Structural Equation Modeling. Hypothesis shows academic leadership and campus culture has high influence on institutional effectiveness. Faculty in decision making and campus culture partially mediates between academic leadership and institutional effectiveness. The results of FIMIX-PLS and PLS-MGA analysis shows the similarity in the results of total effect and path relationships. This paper provides theoretical foundations and empirical findings on conceptualizing the antecedents of institutional effectiveness. The outcomes of this research serve as significant input to policy makers and higher education institutions to facilitate enhancement of institutional effectiveness. [ABSTRACT FROM AUTHOR]

In real decision-making problems, decision makers (DMs) usually select the most potential project from several ones. However, they unconsciously show different confidence levels in decision-making process because they come from various backgrounds and have different experiences, etc., which affects the decision results. Moreover, the probabilistic linguistic term set, which not only includes the linguistic expressions used by DMs in their daily life but also contains the probability for each linguistic term, can well portray the real perceptions of DMs for the projects. Furthermore, large-scale consensus has gradually been a popular way to effectively solve complex decision-making problems. To sum up, in this paper, we are dedicated to constructing a large-scale consensus model considering the confidence levels of DMs under probabilistic linguistic circumstance. Firstly, the endo-confidence is defined and measured by DM's probabilistic linguistic information. Then, the DMs are clustered according to the similarities of both evaluation information and the endo-confidence levels. Both evaluation of the non-consensus cluster and evaluation integrated by the clusters with higher endo-confidence level than this non-consensus cluster are used as the reference to adjust its evaluation information. Then, a case study and the comparative analysis are carried out. Finally, some conclusions and future work are given. [ABSTRACT FROM AUTHOR]

Addressing the multi-criteria group decision making problem with interval attribute values and attribute weights, this paper proposes a decision method based on attribute distribution information. The selection of green suppliers is taken as an example for decision analysis. First, in the case of group decision-making, the quantitative values of the evaluation attributes of green suppliers are imputed by decision-makers, and the relevant distributions are constructed for each attribute. Next, combined with the ranges of attribute values, the random interval values are used to describe the information represented by each attribute to overcome the loss caused by the aggregation of individual expert information into group information. We then propose the distributed interval weighted arithmetic average (DIWAA) operator and corresponding operation rules, which realizes the fusion of qualitative data and quantitative judgment. Thus, the proposed approach allows ensuring reasonable results of the multi-criteria analysis. We also construct a ranking method for alternatives based on distributed interval comprehensive scores. Finally, we verify the feasibility and effectiveness of the proposed method for the task of green supplier selection through numerical experiments. [ABSTRACT FROM AUTHOR]

Agent-based modelling is a powerful tool when simulating human systems, yet when human behaviour cannot be described by simple rules or maximizing one's own profit, we quickly reach the limits of this methodology. Machine learning has the potential to bridge this gap by providing a link between what people observe and how they act in order to reach their goal. In this paper we use a framework for agent-based modelling that utilizes human values like fairness, conformity and altruism. Using this framework we simulate a public goods game and compare to experimental results. We can report good agreement between simulation and experiment and furthermore find that the presented framework outperforms strict reinforcement learning. Both the framework and the utility function are generic enough that they can be used for arbitrary systems, which makes this method a promising candidate for a foundation of a universal agent-based model. [ABSTRACT FROM AUTHOR]

During recent years, we have seen an increase in the complexity of school leadership as knowledge work. The new forms of knowledge available to school leadership, such as performance data and survey-based data, raise the expectation that leaders make relevant use of these knowledge resources. Theory and policy claim that new knowledge is of instrumental use in leadership decision making, yet there are important limitations to this strand of efficiency thinking. The paper analyzes leaders' use of organizational assessment data for the purposes of developing their leadership practice. The paper constructs this empirical object as a composite of general quality standards and specific knowledge about each school. Accountability practices are taken to be specific to particular schools and professional organizations. The paper explores how school leadership practices can be researched as an interplay between quality standards emerging from abstracted knowledge ressources such as theory and strategy, and local knowledge work. It may be difficult to balance these forms of knowledge work, and that may explain why leaders often find it hard to use new knowledge resources. The task of making sense of the flow of knowledge and new standards becomes a complex, comprehensive and time-consuming part of practical leadership. [ABSTRACT FROM AUTHOR]

India’s experience with the National Rural Health Mission (NRHM) is notable on account of nationally formalising – at scale – community action in service delivery,monitoring, and planning of health services. A study was undertaken to document and create a historical record of NRHM’s ‘communitization’ processes. The oral history method of the Witness Seminar was adopted and two virtual seminars with five and nine participants, respectively, were conducted, and supplemented with 4 in depth interviews. Analysis of transcripts was done using ATLAS.ti 22 with the broad themes of emergence, evolution, and evaluation and impact of ‘communitization’ under NRHM. This paper engages with the theme of ‘emergence’ and adopts the Multiple Streams Framework (MSF) conceptualised by John Kingdon for analysis. Key findings include the pioneering role of boundary spanning decision makers and the Jan Swasthya Abhiyan (JSA) in advocacy and design of ‘communitization’ structures, and the legacy of rights based social mobilizations and state-civil society partnerships in health during the 1990s influencing the ethos underlying ‘communitization’. Democracy, leadership from the civil society in policy design and implementation, and state-civil society partnerships are linked to the positive results witnessed as part of ‘communitization’ in NRHM. [ABSTRACT FROM AUTHOR]

Pharmaceutical sector corruption undermines patient access to medicines by diverting public funds for private gain and exacerbating health inequities. This paper presents an analysis of UN Convention Against Corruption (UNCAC) compliance in seven countries and examines how full UNCAC adoption may reduce corruption risks within four key pharmaceutical decision-making points: product approval, formulary selection, procurement, and dispensing. Countries were selected based on their participation in the Medicines Transparency Alliance and the WHO Good Governance for Medicines Programme. Each country’s domestic anti-corruption laws and policies were catalogued and analysed to evaluate their implementation of select UNCAC Articles relevant to the pharmaceutical sector. Countries displayed high compliance with UNCAC provisions on procurement and the recognition of most public sector corruption offences. However, several countries do not penalise private sector bribery or provide statutory protection to whistleblowers or witnesses in corruption proceedings, suggesting that private sector pharmaceutical dispensing may be a decision-making point particularly vulnerable to corruption. Fully implementing the UNCAC is a meaningful first step that countries can take reduce pharmaceutical sector corruption. However, without broader commitment to cultures of transparency and institutional integrity, corruption legislation alone is likely insufficient to ensure long-term, sustainable pharmaceutical sector good governance. [ABSTRACT FROM AUTHOR]

Background: There is a shortage of organs for transplantation in the UK. However, whilst 82% of the population consider donating their organs, only 35% of people have joined the NHS Organ Donor Register. Although the Human Tissue Acts (2004, 2006) and Human Transplantation (Wales) Act (2013) state that the wishes of the deceased cannot be vetoed, it is unlikely that healthcare teams will continue with the retrieval process without the family's agreement to proceed. Aim: To understand what influences the decision of families to donate in order to guide clinical practice, education, training and increase donation rates to 80% in line with the NHS Blood and Transplant - Taking Organ Donation to 2020 strategy. Method: A literature review of published research. Results: Seven papers met the inclusion criteria. Several significant factors were identified that influence family decisions, including prior knowledge of the deceased's wishes (e.g. carrying a donor card), presence at the time of the donor's injury, understanding of brain stem death testing, 'personal realisation' of death and hospital related factors (e.g. information, communication and care). These were organised to form the acronym DONATE that serves as a useful mnemonic to guide the requester prior to discussing organ donation. Conclusions: Rates of donation of organ donation may increase through understanding family decision-making. [ABSTRACT FROM AUTHOR]

Electric vehicles arouse interest since they not only contribute economies of countries in the context of dependency to oil but also support to more livable and sustainable urban areas. The location selection of electric vehicle charging stations is one of the most vital topics in order to enhance the use of electric vehicles. In this sense, the aim of this paper is to propose an approach that integrates Geographic Information System (GIS) techniques and Multi-Criteria Decision Making (MCDM) methods for finding suitable locations of the electric vehicle charging stations. In this regard, the Analytic Hierarchy Process (AHP) and the Fuzzy Analytic Hierarchy Process (FAHP) methods are used to calculate the weights of criteria. While the two different weights for each criterion are obtained by means of AHP in terms of environmental impact and accessibility, another weight for each criterion is obtained as a means of applying the FAHP. The intersection of three different suitability indexes is determined so as to achieve a holistic, credible result. The Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method is used to rank the alternative locations. The results show that the proposed approach offers a notable solution to be selected suitable charging station locations. Moreover, policymakers and administrators could benefit from these results in order to make efficient decisions for forward planning and strategies. [ABSTRACT FROM AUTHOR]

This paper addresses the question of how to cultivate organisational development to support organisations towards a sustainable future. Seeking to identify, systematise and elucidate the process of redevelopment in an Austrian small/medium enterprise by means of Language-Information-Reality, a multidimensional system of semantics, it is argued that sustainable organisational development can be explained by the enactment and management of four well-selected knowledge components: expertise, competence and capabilities in their operational influence upon organisational action, as well as explanatory meta-theoretical reflection. This paper contributes to the theory on organisational development by demonstrating the value of knowledge sharing by individual employees from different domains of expertise, and acknowledges the research leading to the paradigm of sense-making in organisations. [ABSTRACT FROM AUTHOR]

Background: Data-based decision-making in education often focuses on the use of summative assessment data in order to bring about improvements in student achievement. However, many other sources of evidence are available across a wide range of indicators. There is potential for school leaders, teachers and students to use these diverse sources more fully to support their work on a range of school improvement goals. Purpose and sources of evidence: To explore data-based decision-making for school improvement, this theoretical paper discusses recent research and literature from different areas of data use in education. These areas include the use of formative assessment data, educational research study findings and 'big data'. In particular, the discussion focuses on how school leaders and teachers can use different sources of data to improve the quality of education. Main argument: Based on the literature reviewed, an iterative model of data use for school improvement is described, consisting of defining goals for data use, collecting different types of data or evidence (e.g. formal data, informal data, research evidence and 'big data'), sense-making, taking improvement actions and evaluation. Drawing on the literature, research insights are discussed for each of these components, as well as identification of the research gaps that still exist. It is noted that the process of data use does not happen in isolation: data use is influenced by system, organisation and team/individual level factors. Conclusions: When it comes to using data to improve the quality of teaching and learning, it is evident that some of the most important enablers and barriers include data literacy and leadership. However, what is less well understood is how we can promote the enablers and remove the barriers to unlock, more fully, the potential of data use. Only then can data use lead to sustainable school improvement. [ABSTRACT FROM AUTHOR]

This paper presents the parametric design optimisation of the bellows multi-response convolution stresses using multiple attribute decision making (MADM) technique. Grey Relational Analysis (GRA) in conjunction with Principal Components Analysis (PCA) has been employed to find a single response grade (PCA-GRG). Experimental stress analysis was carried out using L25 Taguchi orthogonal array (OA) by considering the design parameters, namely, ply thickness (tp), convolution height (H),convolution pitch (q), and bellows pitch diameter (dp). Analysis of variance (ANOVA) has been implemented to identify the most influenced significant design parameters and optimal design level settings using PCA-GRG. Confirmation test was conducted which proves the improvement in GRG by 0.042 (25.30%) over the initial setting of the factors. Further, multiple regression mathematical models were developed to find the best alternate optimal solutions and verified it by comparing with experimental GRG and Taguchi predictions. Alternate optimal solution signifies the improvement in PCA-GRG over the initial parameter setting from 13.04% to 32.6%, which directs the extensive reduction in the stresses. This verification proves the potential of the integrated PCA-GRG approach for the multi-response parametric optimisation of the convolution stresses. [ABSTRACT FROM AUTHOR]

Ertsen discusses the representation of reality and uncertainty in our paper, raising three critical points. In response to the first, we agree that discussion of different interpretations of the concept of uncertainty is important when developing perceptual models - making different uncertainty interpretations explicit was a key motivation behind our method. Secondly, we do not, as Ertsen suggests, deny anyone who is not a "certified" scientist to have relevant knowledge. The elicitation of diverse views by discussing perceptual models is a basis for open discussion and decision making. Thirdly, Ertsen suggests that it is not useful to treat socio-hydrological systems as if they exist. We argue that we act as "pragmatic realists" in most practical applications by treating socio-hydrological systems as an external reality that can be known. But the uncertainty that arises from our knowledge limitations needs to be recognized, as it may impact on practical decision making and associated costs. [ABSTRACT FROM AUTHOR]

The oil industry in Thailand primarily relies on truck transportation due to the versatility of trucks. Different truck sizes can easily access many customer locations better than any other transportation mode. However, although trucks have many advantages, the transportation cost per unit is very expensive compared to railway, air, waterway, and pipe transportation modes. Previous data indicated that shipping costs were approximately 703.995 USD/month/fleet and the transportation utilization was 83.145%. Therefore, efficiencies should be identified to improve business operations. This paper proposes an oil transportation plan with transshipment points by adopting Network Flow Models (NFM) to develop a Generalized Network Flow Model (GNFM). The developed GNFM was applied to solve three different problems and compared to the existing method. The objectives of the proposed model were to minimize total cost and maximize truck efficiency. Multi-Criteria Decision Analysis (MCDA) was considered to set the optimal decision criteria for managing the oil transportation network with the transshipment sites. The proposed model with the Cluster Model (CT) provided optimal results, with a total cost of 663.539 USD/month/fleet and a truck efficiency of 88.029%. [ABSTRACT FROM AUTHOR]

The study presents analytical data-based multi-criteria approach of critical success factors of infrastructure construction projects analyzed in the Ethiopian con- struction industry. This multi-criteria technique helps to improve the decision cap- abilities and ultimate performance of construction processes in various low-income countries of the East African region. The aim of this paper is to establish a logical relationship and interdependencies of success-related factors for enhancing decision making for various project teams and identify priorities while taking into account all known construction organizational constraints. A structured hierarchical matrix was developed based on a pre-identified success-related factors, and initially evaluated by experienced professionals as part of a content validation of the survey. Different professionals working in various construction organizations in Ethiopia were invited to participate in the questionnaire survey. All the required data analysis, including sen- sitivity performance, was conducted through Expert Choice© 11. Further, Kendall’s coefficient of concordance was conducted to examine and compare multiple expert responses. Based on the findings, the top success-related factors that affect decision making in construction projects are Adequate Goals/Objectives, Consultant’s Competency, Prior Experience of Consulting Firms, Consulting Firm’s Willingness and Cooperation, and Financial Standing of Contractor. The results are based on their global priority weights in the hierarchical model. The findings highlighted that there is disagreement between the major stakeholders involved in the construction process. The contribution of the study is introducing a bench-marking multi-criteria decision analysis technique to enhance decision making in the Ethiopian infrastructure sector. [ABSTRACT FROM AUTHOR]

To fill the gap in health research capacity-building efforts, we created the 'Virtual Library' (VL) – a web-based repository of context-relevant resources for health researchers in low- and middle-income countries (LMICs). This paper describes the participatory process used to systematically develop the VL, and describes how our interprofessional team – representing both an LMIC (Nepal) and a high-income country (HIC) (USA, US) – engaged in shared meaning-making. A team of researchers and clinicians representing a range of subdisciplines from Nepal and the US created a replicable search strategy and standardized Resource Screening Guide (RSG) to systematically assess resources to be included within the VL. Descriptive methods were used to summarize findings from the RSG and lessons learned from the collaborative process. Collectively, 14 team members reviewed 564 potential resources (mean = 40, SD = 22.7). Mean RSG score was 7.02/10 (SD = 2). More than 76% of resources met each of the four quality criteria (relevant; reputable, accessible; understandable). Within the published VL, 298 resources were included, organized by 15 topics and 45 sub-topics. Of these, 223 resources were evaluated by the RSG; 75 were identified by team member expertise. The collaborative process involved regular meetings, iterative document revisions, and peer review. Resource quality was better than expected, perhaps because best practices/principles related to health research are universally relevant, regardless of context. While the RSG was essential to systematize our search and ensure reproducibility, team member expertise was valuable. Pairing team members during peer-review led to bi-directional knowledge sharing and was particularly successful. This work reflects a highly collaborative global partnership and offers a model for future health research capacity-building efforts. We invite engagement with the Virtual Library <> as one supportive pillar of infrastructure to develop individual and institutional research capacity. [ABSTRACT FROM AUTHOR]

Previous literature reports that low male partner support is a barrier to women's adherence and retention in HIV care programs. This qualitative study explored the relationships between partners to understand what is meant by male partner support in adherence of HIV-positive women in four healthcare facilities in Southern Malawi. We conducted 8 semi-structured focus group discussions (FGDs) with 73 participants (40 men and 33 women) and 10 in-depth interviews (IDIs) between August 2018 to December 2019. Participants were HIV-positive patients, healthcare workers (HCWs), expert patients (EPs), and couples attending the clinic. All data were digitally recorded, transcribed verbatim, and analysed using a gender-responsive grounded theory approach. This study confirms previous literature, which suggests male partner support is expressed by providing access to transport to the clinic and accompaniment to appointments. However, we found that men can also control access to resources and decision-making. Support is more complex than previous literature reported and, in some cases, gender norms significantly limit women's capacity to engage in care independently of male support since women need male partner permission to access the resources to attend clinics. This paper suggests that restrictive male-partner gender norms limit women's power to engage in care. Most importantly, the gender analysis reveals that what previous literature describes as male partner support can sometimes hide male partner control in permitting access to resources to attend health facilities. For this reason, policies enhancing male support should consider the gender power relationship between partners to avoid reinforcing gender inequality. [ABSTRACT FROM AUTHOR]

In South Africa, community participation has been embraced through the development of progressive policies to address past inequities. However, limited information is available to understand community involvement in priority setting, planning and decision-making in the development and implementation of public services. This narrative review aims to provide evidence on forms, extents, contexts and dynamics of community participation in primary health care (PHC) and water governance in South Africa and draw cross-cutting lessons. This paper focuses on health and water governance structures, such as health committees, Catchment Management Agencies (CMA), Water User Associations (WUAs), Irrigation Boards (IBs) and Community Management Forums (CMFs). Articles were sourced from Medline (Ovid), EMBASE, Google Scholar, Web of Science, WHO Global Health Library, Global Health and Science Citation Index between 1994 and 2020 reporting on community participation in health and water governance in South Africa. Databases were searched using key terms to identify relevant research articles and grey literature. Twenty-one articles were included and analysed thematically. There is limited evidence on how health committees are functioning in all provinces in South Africa. Existing evidence shows that health committees are not functioning effectively due to lack of clarity on roles, autonomy, power, support, and capacity. There was slow progress in establishment of water governance structures, although these are autonomous and have mechanisms for democratic control, unlike health committees. Participation in CMAs/WUAs/IBs/CMFs is also not effective due to manipulation of spaces by elites, lack of capacity of previously disadvantaged individuals, inadequate incentives, and low commitment to the process by stakeholders. Power and authority in decision-making, resources and accountability are key for effective community participation of marginalized people. Practical guidance is urgently required on how mandated participatory governance structures can be sustained and linked to wider governance systems to improve service delivery. [ABSTRACT FROM AUTHOR]

Evaporation is important basic information for irrigation decision making in water resources management. Developing countries usually use a small pan to observe surface evaporation, but the relationship between evaporation in different small pans is not sufficiently clear. In this paper, we use an extreme learning machine (ELM) model to predict and convert E20 (diameter 0.20 m) and E601 (diameter 0.62 m) pan data for 38 meteorological stations in southern China. Firstly, we obtained the best combination of meteorological parameters for forecasting E20 and E601, respectively, and we also found that the accuracy of the model can be significantly improved by adding pan data. Secondly, we found that during the conversion between E20 and E601, the model performance when using E601 data to predict the E20 evaporation is better than that when using E20 data to predict the E601 evaporation. Finally, the geographical factors were analysed, and the model performance was found to be relatively poor in the coastal area and the North–South junction. [ABSTRACT FROM AUTHOR]

Adolescence is a stage when significant changes in moral development take place. Sometimes, a discrepancy between adolescents' thoughts, emotions and actions may result in socially undesirable outcomes. The question is not whether emotions produce moral behaviour, but how awareness of emotions may influence moral action. In this paper, the interaction among cognition, emotions, and action is considered from two theoretical perspectives: cognitive-developmental and social domain theory. We argue that with increased awareness of cognition and emotion, actions may be influenced, and propose meta-moral cognition process as an active mediator of the cognition and emotions involved in making moral decisions. The meta-moral cognition strategies are also provided for classroom practices with the intention to support adolescents' meta-moral cognitive development. [ABSTRACT FROM AUTHOR]