How do checklists and risk of bias tools support research?

 The EQUATOR (Enhancing the QUAlity and Transparency of health Research) network is a global initiative focused on improving the quality and transparency of research reports. It offers a wide range of resources to support researchers, such as checklists and risk of bias tools.

Why are they needed?

Checklists are key to ensuring the quality of research reports, minimizing the risk of omitting important information. Risk of bias tools help identify potential weaknesses in studies, allowing them to be examined more critically.

These tools are particularly useful for those working in clinical, epidemiological, and basic science research. They can help researchers avoid methodological errors that could affect the credibility of results.

The EQUATOR website provides a number of guides and checklists, including PRISMA (for reporting meta-analyses) and CONSORT (for reporting clinical trials). Each of these resources is designed to support transparency and reliability in scientific reporting.

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Randomized controlled trials (RCTs)

Randomized controlled trials (RCTs) are considered the “gold standard” in medical research. They involve randomly assigning participants to study groups, such as an experimental group (receiving a new treatment) and a control group (receiving a placebo or standard treatment). This allows for an objective assessment of the effectiveness and safety of medical interventions. RCTs are a key element of evidence-based medicine (EBM) and are used not only in medicine but also in other fields such as psychology and social sciences.

RCTs can be very useful in management, especially in the area of ​​evidence-based decision-making. They can be used to test different management strategies, organizational policies, or training methods. For example, if a company wants to implement a new way of motivating employees, it can conduct an RCT to see if it actually produces better results compared to current practices. Such studies help minimize the risk of decision-making errors and also help understand which interventions are most effective.

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Evolution of Systematic Literature Reviews - from static to dynamic – a critical analysis

 Zeszyty Naukowe Akademii Górnośląskiej - No. 19

How are systematic literature reviews adapting to turbulent times and changing the face of EBMnt? Through a critical literature review, we explored how traditional, static reviews, usually conducted as one-time projects, are transforming into dynamic, continuously updated “living literature reviews.”

Key findings: – Traditional, static reviews, conducted as one-time reports, are gradually evolving as technology advances and the number of scientific publications increases. – Dynamic approaches, based on the concept of living systematic reviews, meet the contemporary needs of updating reviews in real time.

Added value: Traditional reviews, once sufficient as one-time reports, are losing their relevance in the face of the growing number of new studies. Our research findings indicate the need to develop more flexible and dynamic methods for conducting literature reviews that can keep up with continuous innovation.

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Learning Curve and DART in the Evidence-Based Management (EBMnt) paradigm

Contemporary organizational management not only seeks operational efficiency but also relies on scientific research and empirical evidence to support decision-making. Within the Evidence-Based Management (EBMnt) paradigm, concepts such as the learning curve and the DART (Dynamic Assessment of Real-Time Learning) approach are analyzed in the context of process and work method effectiveness. Below, I will discuss these two concepts in the context of EBMnt, considering their role in improving organizational performance.

Learning Curve in EBMnt

Learning curve is a concept that illustrates how work efficiency (e.g., productivity, task completion time) changes as experience is gained in a given area. Simply put, the more we perform a certain activity, the more proficient we become, thus reducing the time needed to complete it and improving the quality of results.

In the Evidence-Based Management (EBMnt) paradigm, learning curve analysis is the basis for making decisions about resource allocation, training planning, process improvement, or identifying areas that may require greater support. The key element in this approach is the use of empirical data and scientific evidence that indicate how the learning curve differs depending on the organizational context, type of work, or industry specifics.

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Empirical evidence in this area may indicate that, for example, in manufacturing companies, the learning curve is strongly related to automation and standardization processes, while in the creative industry it may be more complex, and the time to perfect skills is more difficult to measure in a linear way. EBMnt allows for monitoring and analyzing these changes over time, which enables process optimization.

DART (Dynamic Assessment of Real-Time Learning) in EBMnt

DART is an approach that uses dynamic assessment of progress in real-time, in the context of organizational learning. Instead of analyzing only changes in efficiency over the long term, DART focuses on continuously collecting data that allows for an assessment of how an organization (or its members) absorb new knowledge and how it affects their results in the short term.

Within EBMnt, the DART approach becomes particularly important as it enables organizations not only to respond to problems in real-time but also to monitor which specific strategies or training interventions bring measurable effects in a short time. By using real-time evidence, organizations can more quickly identify effective learning and adaptation techniques that lead to better results.

DART in the context of EBMnt allows for quick testing of different management strategies and assessment of their impact on the learning process. An example could be analyzing the effectiveness of training or employee development programs at both individual and team levels. Through an evidence-based approach, organizations can make adjustments to development strategies in a more flexible way that is tailored to current needs.

Combining Learning Curve and DART in the EBMnt Paradigm

Both approaches – learning curve and DART – in the context of EBMnt can be mutually complementary. The learning curve shows the long-term trend of efficiency as experience is gained, while DART focuses on current assessment and adaptation, based on data collected in real-time. Combining these two elements within the EBMnt paradigm enables organizations not only to better forecast but also to quickly respond to changes and optimize learning processes.

In practice, organizations can use the learning curve for long-term planning of training, competency development, or process design. At the same time, the application of DART allows for ongoing monitoring of the effectiveness of these activities and making adjustments when data indicate that a given process or intervention is not yielding the expected results. From the EBMnt perspective, both these tools should be supported by empirical evidence that allows for an objective assessment of the effectiveness of actions and making decisions based on them.

Summary

In the context of Evidence-Based Management (EBMnt), both the learning curve and DART are powerful analytical tools that allow organizations to improve efficiency through the use of scientific evidence and data. The learning curve provides a broad view of long-term development, while DART allows for quick and ongoing adjustment of management strategies in real-time. As a result, organizations can not only optimize their processes but also dynamically adapt to changing market conditions and employee needs.

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How to balance impact and quality of research in the REF?

Here’s what to consider

The REF (Research Excellence Framework) is a research assessment framework that simultaneously considers the quality of research and its impact on society, the economy and other areas. To effectively balance these two aspects, it is crucial to understand the criteria and expectations for both.

1. Understand the assessment criteria

The first step is to understand how the quality and impact of research are assessed in the REF. Research quality is assessed by expert panels on four elements: originality, significance, reliability and contribution to the field. Research impact is assessed through case studies that show how research has impacted the economy, society, culture, environment, health or public policy. The REF documentation provides detailed definitions, examples and standards for each of these elements. It is worth familiarising yourself with the relevant guidelines for your discipline and unit of assessment.

2. Focus on research quality

To ensure high-quality research, attention should be paid to its originality, significance, and methodological integrity. In practice, this means designing studies that not only provide new, valuable information, but are also well-documented and conducted according to rigorous research standards.

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3. Build impact case studies

To demonstrate the impact of your research, it is important to develop case studies that show how the research has had an impact. An example would be a study related to sustainability in business, where the research may have an impact on changes in corporate policies regarding sustainable resource use. It should be clearly shown how the research has influenced changes in practices and decisions outside of academia.

For example, if your research explores the EBMnt model, highlight how implementing evidence into decision-making processes increases organizational effectiveness and helps adapt to changing market conditions.

4. Combine quality and impact

The key to success is the integration of quality and impact. Good research is research that not only contributes new, valuable information to its field, but also has a practical, visible impact on the world outside the university. Engage in interdisciplinary collaborations that increase the chance of broad impact while maintaining high research standards.

5. A culture of reflection and continuous improvement

It is also important to build a culture of reflection among researchers, enabling them to share experiences, discuss challenges and successes, and promote continuous improvement in their research. Regular training and resources for researchers can help them communicate their research more effectively and better align it with the requirements of the REF.

Summary

Balancing the impact and quality of research in the REF requires a deliberate strategy. Understanding the evaluation criteria, investing in high-quality research, and being able to communicate its impact on the wider community are key elements of success in the research evaluation process.

It's worth watching

https://impact.ref.ac.uk/casestudies/Search1.aspx

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Non-Destructive Testing (NDT) and EBMnt – Conclusions and Synergy Prospects

1. Introduction to Nondestructive Testing (NDT) and EBMnt
Nondestructive testing (NDT) is a set of methods used to examine materials and structures, allowing the assessment of their technical condition without causing any damage. NDT is widely used in industries such as aerospace, energy, automotive, and construction, where critical components of machines, devices, and structures must be regularly checked for damage or defects that could threaten safety or functionality.

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On the other hand, EBMnt (Evidence-Based Management) is a management approach based on empirical evidence that uses data and analysis to make decisions within an organization. In the field of NDT, this approach is becoming increasingly important because it allows the systematic incorporation of NDT test data and results into decision-making processes, which in turn enables more precise and fact-based decisions regarding the technical condition, maintenance, or replacement of components.

2. NDT in the Context of EBMnt
In the context of EBMnt, NDT plays the role of providing hard evidence about the condition of materials and equipment, enabling a comprehensive analysis of their state. NDT tests generate data that is used to make decisions regarding further usage, maintenance, or replacement of components. In traditional approaches, these decisions might be based on subjective assessments or intuitive judgments, but through EBMnt, this process becomes more objective, transparent, and evidence-based.

NDT tests offer a wide range of methods, such as:

• Ultrasonic Testing (UT)
• Radiographic Testing (RT)
• Magnetic Particle Testing (MT)
• Eddy Current Testing (ET)
• Thermography (IR)
• Visual Testing (VT)

Each of these methods provides detailed data on the material's structure, its integrity, and any hidden defects. It is worth noting that in the use of EBMnt, NDT test results must be properly analyzed, documented, and used in the decision-making process, which enhances operational efficiency.

3. Key Findings from the Integration of NDT and EBMnt

• Objectivity of Decisions – the integration of NDT with EBMnt allows decisions to be made based on accurate test data, eliminating subjectivity in assessing the technical condition of machines and structures. This enables managers to make more precise decisions about the need for maintenance or replacement of parts, minimizing the risk of errors.
• Risk Minimization – regular NDT tests allow for early detection of damage that may lead to failures. Combined with the EBMnt approach, decisions regarding the need for repair or replacement are based on empirical data, reducing the risk of sudden failures and related production downtime.
• Cost Optimization – using NDT tests helps optimize maintenance costs. Maintenance and repair activities can be precisely scheduled when most needed, preventing unnecessary replacement costs for components that are in good condition, as well as repair costs after failures that could have been anticipated.
• Operational Efficiency – NDT allows for continuous monitoring of equipment and material conditions without disrupting their operation. This ensures the continuity of production and operations in enterprises while maintaining safety and reliability.

4. Synergy Perspective

Integrating NDT methods with the EBMnt approach brings numerous benefits in risk management, cost optimization, and improving operational safety.

Here are some key areas of synergy between these two approaches:

• Improved Quality of Decision-Making Processes – NDT provides detailed, measurable data about the technical condition of equipment and materials, which, combined with EBMnt analysis, allows for better decisions in maintenance, repair, and technical resource management.
• Continuous Improvement of Production Processes – according to the EBMnt principle, processes within an organization are continuously improved based on analysis and data. NDT tests allow for constant monitoring of material and product quality, enabling quick responses to irregularities and the implementation of effective corrective actions.
• Optimization of Maintenance Planning – by combining NDT results with EBMnt analysis, better planning of maintenance activities is possible. Based on the evidence from tests, organizations can move to an approach based on the actual condition of equipment, leading to efficient planning of repairs, part replacements, and other maintenance activities.

5. Conclusions
The integration of NDT with EBMnt enables organizations to manage technical resources more precisely and consciously. NDT research provides data that, when combined with the EBMnt approach, can lead to better operational decision-making, risk minimization, and reduced maintenance costs. NDT becomes a key tool in this context, providing organizations with evidence of the actual technical condition of their resources, enabling the implementation of effective, fact-based decisions that contribute to increased efficiency, reliability, and operational safety.

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Determinants of the application of the Evidence – Based Management concept for explaining ontological entities in management and quality sciences

„Kwartalnik Nauk o Przedsiębiorstwie“ 4 (2024), p. 115–135. https://doi.org/10.33119/KNoP.2024.74.4.8

We are pleased to invite you to explore our latest scientific article, which delves into the key determinants of studying ontological entities in quality management sciences through the lens of the Evidence Based Practice concept. This article provides an in-depth critical analysis of previous studies, highlighting the cognitive gap and emphasizing the significance of systematic literature reviews, meta-analyses, and other methodologies in advancing our understanding of ontological entities within management and quality sciences.

Summary: The article aims to identify the primary determinants of studying ontological entities in quality management sciences using the Evidence Based Practice concept. The research draws on critical analyses of prior studies to elucidate the current status and knowledge gaps in this field. Our findings demonstrate the effective application of research methodologies such as systematic literature reviews and meta-analyses, adapted from the medical industry, in the management and quality sciences. These methodologies provide a broader scientific understanding compared to traditional literature reviews. Moreover, the article addresses the challenges posed by managers' limited knowledge of ontological entities and the ontological significance of their decisions in business. The increasing availability of large sets of scientific data enhances the understanding and utilization of evidence-based management practices.

We believe that this article will offer valuable insights and contribute to the ongoing discourse in management and quality sciences. We look forward to your feedback and engagement with our research.

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A unique research approach

The Scientific Confraternity of Evidence Researchers (SCoER) implements a unique research approach in management sciences. The team consisting of Adam Jabłoński, Marek Jabłoński, Daniel Dulęba, Mariusz Glenszczyk and Piotr Janulek focuses its activities on the evolutionary combination of methodologies developed in evidence-based medicine (Evidence-Based Medicine) with management sciences.

The foundation of the team's work is the belief that rigorous research methods used in medicine and pharmacy can significantly improve the quality of the decision-making process in managing organizations. The Confraternity undertakes pioneering attempts to adapt meta-analyses and systematic literature reviews — methods commonly used in medical research — to the context of enterprise management. This is a particularly innovative approach, because so far management sciences have been based mainly on other research methods, often less methodologically rigorous.

The team develops the concept of management based on systematic evidence (Systematic Evidence-Based Management), which may constitute the seed of a new subdiscipline in management sciences. This can be provisionally called "Systematic Evidence-Based Management" or "Medical Standards Management".

Within this concept, researchers propose using the hierarchy of scientific evidence, characteristic of medicine, in the process of making managerial decisions. This approach aims to increase the objectivity and effectiveness of management actions by basing them on systematically verified scientific evidence.

The Confraternity introduces the methodology of meta-analysis to management sciences, which allows for obtaining quantitative, precise and integrated conclusions from various studies. This is particularly important in the context of the growing complexity of management problems and the need to make decisions based on reliable data.

The team's research is characterized by a holistic approach to science, combining the methodological rigor of medical sciences with practical aspects of managing organizations. This interdisciplinary perspective allows for the creation of new solutions in the field of scientific research and management practice.

The Confraternity's activities can contribute to significant changes in the way research is conducted in management sciences, introducing higher methodological standards and new research tools. In a practical perspective, the team's work can lead to the development of more effective and objective management strategies based on systematically verified scientific evidence.

The team believes that introducing rigorous methodological standards to management sciences is necessary in the face of the growing complexity of modern organizations and their environment. By adapting proven methods from the field of medicine, the Confraternity aims to improve the quality of research in management sciences and increase their practical usefulness.

This pioneering activity of the Scientific Confraternity of Evidence Researchers fills a significant gap in the literature on the subject, creating new standards for conducting scientific research in the field of management and quality. It can also provide a basis for the development of a new paradigm in management sciences, combining best practices from various fields of science.

Evolutionary Integration of Evidence-Based Medicine (EBM) Methodology with Management Sciences

The integration of methodologies developed in evidence-based medicine (EBM) with management sciences requires consideration of various information sources that together form a coherent and comprehensive foundation for decision-making. One of the key elements is scientific research, which provides reliable evidence of the effectiveness of methods and strategies. Rigorous analyses, such as meta-analyses and systematic literature reviews, serve as a solid reference point, helping to avoid subjectivity and reliance on unverified assumptions.

Another important source of information is grey literature, which includes reports, working papers, presentations, and other materials not considered traditional academic publications. Although often less formal, grey literature provides practical context and data that can be crucial for analyzing management realities, especially in situations where scientific publications are lacking.

Equally important are organizational data, which come from internal resources of the organization, such as operational analyses, performance indicators, and employee satisfaction survey results. This data allows decisions to be tailored to the unique conditions and challenges faced by a given organization, enhancing the effectiveness of implemented strategies.

Expert opinions should also be taken into account as a valuable source of practical knowledge and intuitive insights. Experts with experience in a specific field can provide guidance that may be difficult to find in literature or numerical data, particularly in the context of innovation or crisis management.

Stakeholder interests also play a crucial role in the decision-making process. Evidence-based management requires understanding and considering the needs, expectations, and preferences of individuals and groups involved in the organization's activities. This allows the development of strategies that are not only effective but also accepted and supported by stakeholders.

The final, but equally important, aspect is cultural factors. Every organization operates within a specific cultural context, which influences decision-making, communication, and the implementation of changes. Considering cultural aspects allows strategies to be better tailored to local conditions and minimizes the risk of resistance to change.

The integration of these six information collectors creates a foundation for evidence-based management, which, much like in medicine, enables decision-making that is highly effective and contextually appropriate.

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