Methodological Challenges Research Articles

Proton pump inhibitors and the risk of gastric cancer: a systematic review, evidence synthesis and life course epidemiology perspective

ObjectivesSince proton pump inhibitors (PPI) have been introduced, many concerns were raised regarding potential gastric carcinogenicity. We aim to summarise and weigh the epidemiological evidence and address possible causality.DesignSystematic literature...

A Decade of Industrial Relations Research in Sub‐Saharan Africa: A Systematic Review and Future Research Agenda

ABSTRACTThis review paper examines the state of IR research in SSA and establishes a future research agenda—a context that has had limited coverage in global IR literature. Following the Preferred Reporting Items method for conducting Systematic reviews and Meta‐Analyses, we reviewed 52 IR studies published in 43 journals between 2013 and 2023 that covered 16 countries in SSA. Analysis of the studies revealed five core areas of IR research: union militancy, new form of union activism and democratisation, union fragmentation, tripartism and externally driven neoliberalism. Further analysis identified four emerging themes: colonial and historical legacies, overrepresentation of the South African context, and theoretical and methodological challenges linked to researching IR in the region. Despite regional variations between Francophone, Lusophone and Anglophone Africa, our review contends that regardless of the country‐specific orientation and history, IR in SSA is entangled with colonisation, decolonisation, institutional democratisation, economic informality and precarious labour politics. These findings have significant implications for IR research and policy, particularly on the advancement of decent work and regeneration of trade unions in SSA.

Harnessing neuroimaging-guided transcranial magnetic stimulation for precision therapy in substance use disorders.

Substance use disorders (SUDs) are a critical public health challenge characterized by high relapse rates, with existing treatments often proving inadequate. The focus of this review is to provide an update on the current state of transcranial magnetic stimulation (TMS) as a therapeutic intervention for SUDs and discuss neuroimaging-guided TMS practices. This review explores the neurobiology underlying SUDs, emphasizing the roles of the prefrontal cortex, striatal circuits, and dopaminergic pathways, and examines the theory that TMS modulates neurocircuitry to impact addiction-related behaviors. We discuss TMS procedural aspects and provide a comparative analysis of TMS protocols, focusing on repetitive, deep, single-pulse, paired-pulse, and a more recent approach, theta burst stimulation. We review recent randomized clinical trials (RCTs) to demonstrate reductions in cravings and use across SUDs as well as highlight the need for standardized protocols. We emphasize the power of combining neuroimaging techniques to show functional connectivity changes in the brain and identify potential biomarkers predictive of SUD treatment response, an unexplored area of discussion. With these topics, this review highlights the potential of TMS as a versatile and effective therapeutic modality for SUDs, especially when combined with neuroimaging. Key findings emphasize the necessity for future research to address methodological challenges, such as standardizing protocols and optimizing stimulation parameters. The integration of neuroimaging provides insights into functional connectivity changes, enabling enhanced precision and individualized treatment strategies. By validating TMS approaches and incorporating multimodal techniques, this field can advance toward a more robust clinical utility in addressing the complex neurocircuitry of addiction-related behaviors underlying SUDs.

Toward multiscalar measures of inequality in archaeology

The Gini coefficient is a statistical measure commonly used to characterize distributions of socioeconomic quantities. Archaeologists and social scientists have recently adopted this method to analyze ancient inequality by targeting specific proxy variables (e.g., residential unit size, burial data, etc.). Variations in the Gini are then examined in relation to key factors such as time, geography, and subsistence. Yet, Gini coefficients could be obtained across different scales of aggregation, from small neighborhoods within a larger settlement to an ensemble of multiple settlements that are part of the same polity. These different scales of aggregation represent considerable methodological and theoretical challenges, as larger scales might, for example, imply greater social and economic variation within groups and thus affect the Gini coefficients. Furthermore, these issues can also be exacerbated by the idiosyncrasies and limitations of historical and archaeological datasets. This paper discusses the potential and challenges of measuring Gini coefficients at and above the scale of individual archaeological sites, contrasting different approaches and discussing how each can reveal insights into different patterns of past wealth inequality, addressing methodological, empirical, and theoretical implications arising from the multiscalar nature of human interactions.

Study and instrument quality in perception-based L2 pronunciation research

Abstract This methodological synthesis surveys study and instrument quality in L2 pronunciation research by scrutinizing methodological practices in designing and employing scales and rubrics that measure accentedness, comprehensibility, and intelligibility. A comprehensive coding scheme was developed, and searches were conducted in several databases. A total of 380 articles (409 samples) that employed 576 target instruments and appeared in peer-reviewed journals from 1977 to 2023 were synthesized. Results demonstrated, among other findings, strengths in reporting several listener and speaker characteristics. Areas in need of improvement include (a) more thorough evaluation and reporting of interrater reliability and instrument validity and (b) greater adherence to methodological transparency and open science practices. We conclude by discussing the implications of these findings for researchers and researcher trainers; by raising awareness of methodological and ethical challenges in psychometric research on L2 speech perception; and by providing recommendations for advancing the quality of instruments in this domain.

Exploring the feasibility of using school food purchase data as a method to assess dietary intakes in secondary school-aged pupils.

To explore the information available in school food purchase data and to ascertain the potential to assess pupils' dietary intakes. The proportion of purchased food and drink items that were linked to (i) an Intake24 food group, and (ii) a nutrient code from the UK NDNS Databank, was calculated. Pupil-level food purchase data covering the whole school day was obtained. Each item purchased was linked to an Intake24 food group and an NDNS Nutrient Databank code. Depending on the level of detail provided, items may have been assigned both a food group and a nutrient code, a food group only or neither for items which did not contain enough information about the type of food or drink purchased. Five secondary schools in northeast England. Secondary school pupils aged 11-16 years. The data captured 119,125 purchases made by 3466 pupils. 92% of item descriptions were assigned a food group, this equated to 82% of total purchases. 70% were assigned an NDNS Databank nutrient code, which accounted for 60% of total purchases. 8% of items had insufficient information and did not have a food group or a nutrient code assigned. The methodological challenges of collecting dietary data from pupils in the secondary school setting are significant. Purchase data offers an alternative, objective approach to collecting information on school food choices, across the school day and for a large sample of pupils. With further development the potential to use purchase data to assess intakes could be achieved.

Health economic evaluation of trans-tibial prosthetic suspension systems: a protocol for a pilot using an observational study and synthetic cohort

BackgroundHealth Economic Evaluations (HEEs) provide the necessary evidence of cost–benefit to inform policy and investment decisions. No HEEs have quantified the cost–benefit of passive suction (PS) vs vacuum assisted suction (VAS) suspension for trans-tibial prosthesis users. There are methodological challenges to conducting HEE in prosthetics given the benefit measures are not focused on the things most important to prosthesis users and funders, and the required time horizons are lengthy. To address these challenges, we propose a pilot study using two PROMIS instruments to measure benefits and trial the use of a Synthetic Cohort Method, to quantify the cost-effectiveness and cost-utility of PS and VAS suspension for people living with trans-tibial amputation.MethodsAn observational study will measure the costs and benefits of PS and VAS suspension for trans-tibial prosthesis users using a Synthetic Cohort Method, a technique used in epidemiological modelling of life-time risks. Each intervention will include 3 sub-groups, representing prosthesis users in the first, second, or third year of the intervention since fitting. A prosthetic payor perspective will be taken, with data collected over a 1-year period and synthesised to reflect the costs and benefits over a 3-year time horizon. Benefits will be measured using two PROMIS instruments reported to best measure the benefits most important to prosthesis users and funders. Costs will be calculated from actual billable costs to the funder. Costs and benefits will be discounted at 4%. Cost-effectiveness and cost-utility will be calculated using the incremental costs and incremental benefits, with results presented as incremental cost-effectiveness and incremental cost-utility ratios. Bootstrapping will be undertaken to assess uncertainty, and discounting will be analysed through a one-way sensitivity analysis.DiscussionThis pilot will make a novel contribution by trailing the use of a Synthetic Cohort Method to reduce the lengthy time horizons required in prosthetic HEE. The HEE will use a two-pronged approach whereby cost-utility and cost-effectiveness are simultaneously evaluated using the PROMIS instruments to inform a wide range of policy and investment decisions. Additionally, this pilot will be the first HEE of suction suspension systems for people with transtibial amputation and will therefore make an important contribution to the prosthetic evidence base.

Criminal Violence, the State, and Society

The study of criminal violence has received increasing attention in political science over the past 15 years, as organized criminal groups have grown and diversified worldwide, unleashing unprecedented waves of violence. This article presents a critical assessment of the current state of political science scholarship on criminal violence. It discusses the sources and dynamics of organized criminal violence, emphasizing the reconceptualization of state–criminal group relationships in the literature, shifts in illegal markets, and the political incentives fueling criminal wars. It also examines how states and societies respond to criminal violence. State responses include punitive approaches, institutional reform, and community-based interventions, while societal responses can be examined through the lenses of exit (e.g., migration, disengagement), voice (e.g., political participation, collective resistance), and loyalty (i.e., compliance with state authorities or criminal groups). The article also addresses conceptual and methodological challenges, policy implications, and ethical considerations inherent in this field of study and identifies promising pathways for future research.

Intelligent Design Method for Thermal Conductivity Topology Based on a Deep Generative Network

Heat dissipation performance is critical to the design of high-end equipment, such as integrated chips and high-precision machine tools. Owing to the advantages of artificial intelligence in solving complex tasks involving a large number of variables, researchers have exploited deep learning to expedite the optimization of material properties, such as the heat dissipation of solid isotropic materials with penalization (SIMP). However, because the approach is limited by discrete datasets and labeled training forms, ensuring the continuous adaptation of the condition domain and maintaining the stability of the design structure remain major challenges in the current intelligent design methodology for thermally conductive structures. In this study, we propose an innovative intelligent design framework integrating Conditional Deep Convolutional Generative Adversarial Networks (CDCGAN) with SIMP, capable of creating topology structures that meet prescribed thermal conduction performance. This proposed design strategy significantly reduces the computational time required to solve symmetric and random heat sink problems compared with existing design approaches and is approximately 98% faster than standard SIMP methods and 55.5% faster than conventional deep-learning-based methods. In addition, we benchmarked the design performance of the proposed framework against theoretical structural designs via experimental measurements. We observed a 50.1% reduction in the average temperature and a 28.2% reduction in the highest temperature in our designed topology compared with those theoretical structure designs.

Enzymes Drive Glutathione Shunt to Explain Oxidative State Using an In-Parallel Multi-Omic Method

The glutathione shunt is one of the most important contributors to the cellular redox state, with implications across cancer, chronic diseases, diseases of ageing, and autoimmune diseases, including inflammatory bowel disease (IBD). Traditionally, the redox state is gauged by the ratio of the surrogate metabolites GSH and GSSG. However, this presents methodological challenges and offers a constrained illustration of metabolites without a systems-level understanding of redox dynamics, failing to elucidate variations across an entire biochemical network. Targeted proteomics can fill this void. Here, we describe an in-parallel metabolomic and proteomic targeted method to encompass measurements directly related to the shunt. Samples are simultaneously prepared to extract the substrate building blocks, cysteine, cystine, methionine, glutamic acid, and kynurenine; and the proteins, SLC7A11 (xCT), Glutamate Cysteine Ligase (GSH1), Glutathione Synthetase (GSH2), Glutathione Peroxidase (GPx), and Glutathione Reductase (GSHR) for targeted mass spectrometry. We demonstrate the method by targeted analysis of proteins in plasma, serum, nasal swab, and saliva and apply the multi-omic method to assess changes in the glutathione shunt in the serum of patients diagnosed with IBD. This allows for a broader narrative to establish context at which the glutathione shunt is operating.

A hierarchical analysis of perceived team personality traits in sport

IntroductionThis study aims to revisit and enhance the foundational concept of perceived team personality by addressing critical conceptual and methodological challenges in previous brand personality studies. While prior studies have identified team personality dimensions and developed measurement scales, ongoing ambiguities in applying the general brand personality conceptualization to sport teams remain. In addition, the approach's limited generalizability, inadequate methods for selecting descriptors, and biases in team (brand) selection pose significant challenges to make a valid and reliable team personality scale in sports.MethodsTo overcome these limitations, this study employs a lexical approach from personality psychology, which posits that fundamental personality dimensions emerge naturally from the adjectives people use to describe themselves and others. By analyzing a set of 99 sport-specific personality descriptors based on the lexical approach, this study explores hierarchical solutions ranging from one to six factors to determine whether perceived team personality dimensions align with established human personality models, such as the Big Five and HEXACO frameworks.ResultsFindings reveal that the five- and six-factor models exhibit strong conceptual alignment with these established human personality structures, demonstrating the efficacy of the lexical approach in capturing sport team personality.DiscussionThis research strengthens the theoretical and methodological foundation for assessing team personality in sport by providing a solid framework that better aligns with consumer perceptions. These insights may contribute to a more precise and contextually relevant understanding of team personality, offering implications for sport brand management, consumer engagement strategies, and long-term team positioning.

Value of Artificial Intelligence for Enhancing Suspicion of Cardiac Amyloidosis Using Electrocardiography and Echocardiography: A Narrative Review.

Nonspecific symptoms and other diagnostic challenges lead to underdiagnosis of cardiac amyloidosis (CA). Artificial intelligence (AI) could help address these challenges, but a summary of the performance of these tools is lacking. This narrative review of published literature describes the performance of AI tools that use data from ECGs and echocardiography to improve identification of CA and challenges that hinder adoption of these tools. Thirteen studies met inclusion criteria with sample sizes ranging from 50 to 2451 patients. Four studies used ECG data, 8 used echocardiography data, and 1 used both. The CA gold standard was typically defined as a CA diagnosis in an institutional or other database but the requirements for these diagnoses were heterogenous across studies, and many did not distinguish among CA subtypes. AI model development varied considerably, and only 4 studies included external validation. The ability of models to predict CA ranged from 0.71 to 1.00, sensitivity ranged from 16% to 100%, and specificity from 75% to 100%. Only 1 study reported model performance across strata of sex, age, race, and CA type. Persistent challenges to AI adoption include usability, cost, value added, electronic health record/information technology interoperability, patient-related factors, regulation, and privacy and liability. Published studies on AI for improved identification of CA show favorable performance measures but numerous methodologic and other challenges must be addressed before these tools are more widely adopted.

DATA-ASSIMILATION OF TRANSITIONAL AND SEPARATED TURBOMACHINERY FLOWS WITH PHYSICS-INFORMED NEURAL NETWORKS

Abstract Despite the demonstrated utility of Reynolds-averaged Navier Stokes (RANS) calculations for many industrially-relevant problems, the method yields unsatisfactory representations of many flows of engineering interest, such as non-equilibrium turbulence, massive flow separation, coherent unsteadiness, and secondary flow features. Due to the Reynolds-averaging process, a turbulence model is required to close the RANS equations, and the simple physical approximations used in many turbulence models can cause erroneous results when applied to the flows that feature strong pressure gradients, sudden changes in mean-strain-rate, surface curvature, and turbulence anisotropy. Physics-informed neural networks (PINNs) offer a way to model aerodynamic problems without explicitly requiring a turbulence closure. The network can use sparse training data and the unclosed RANS equations to reconstruct the flow without a turbulence model. In this work, PINNs are applied to two problems of relevance in the turbomachinery community. Firstly, we consider a variable area channel known as the periodic hills, which features a shear layer, a separation bubble, as well as favourable and adverse-pressure-gradients. Secondly, a PINN is applied to the T106C low-pressure turbine blade featuring the additional challenges of transition and laminar separation. We demonstrate that PINNs are capable of capturing sensitive features such as separation length and kinetic wake loss. This paper undertakes a considered and pragmatic assessment of the state of PINNs when applied to complex high Reynolds number flows of industrial interest, and this work demonstrates the benefits and challenges of this data-assimilation methodology.

Sub‐Sampling at the Researcher's Peril: New Insights Into Sampling Strategy to Avoid Invalid Findings

ABSTRACTMany researchers currently make scientific claims about a general population that differs in material dimensions from the subsample utilized in the analysis, without fully describing their sample characteristics. It is essential to fully disclose relevant facets of the sample, to enable future stakeholders to make appropriate adjustments: we argue that all publications are valuable independently of the sampling strategy, however; their usefulness will dramatically increase when the authors include all conceivable sample characteristics. By employing a Big‐Data set of over 3,300,000 workers (including 300,000 foreign workers) over 10 years, we illustrate how focusing on narrow subsets of a target group can lead to very different conclusions. We address methodological and ethical challenges for the HRM research field providing recommendations on how to avoid the possibility of flawed validity results and how to make the study more relevant, impactful and ethically robust. For practitioners, we highlight how managers can draw learning from academic studies by appreciating differences in subgroups' outcomes that incorporate “context,” which eventually can inform strategic management and managerial decisions.

Between Methodological Agility and Participant Protection: Opinion Research and Scientific Ethics in Light of FAIR Principles and Brazilian Legal Frameworks

Objective: This study analyzes the relationship between public opinion research and scientific ethics, focusing on the role of Research Ethics Committees (RECs), seeking to examine how ethical protocols, regulated by the Resolutions of the National Health Council (466/2012; 510/2016) and Law 14.874/2024, ensure participant protection and methodological quality. Theoretical Framework: Contemporary scientific research requires the integration of ethical and methodological principles, especially in research involving human subjects. FAIR (Findable, Accessible, Interoperable, Reusable) data management and ethical self-regulation are crucial to ensure transparency and reproducibility, as discussed by Gil (2017), Creswell (2021), and Santos and Nascimento (2018). Method: A documentary research and critical literature review were conducted, including analysis of legal norms (Resolutions 466/2012, 510/2016, and Law 14.874/2024), theoretical references (Gil, 2017; Creswell, 2021), and empirical studies on public opinion research (Weber; Pérsigo, 2019). The methodology followed three steps: (1) mapping formal ethical requirements; (2) critical analysis of operational models in research exempt from REC evaluation; and (3) evaluation of the operationalization of protocols via Plataforma Brasil. Results and Discussion: The results show that RECs act as mediators between scientific rigor and ethical safeguards, and that Plataforma Brasil optimizes procedural transparency in the evaluation of complex projects. However, public opinion research exempt from formal evaluation requires ethical self-regulation aligned with FAIR technical-ethical practices for rigorous anonymization and transparent microdata management. The research also pointed out geographical inequalities in the distribution of qualified RECs, which limits the contextual adjustment of national regulations. Research Implications: Public opinion research illustrates the contemporary challenges in balancing methodological agility and participant protection. The integration of FAIR principles and ethical self-regulation are essential to ensure the integrity of research, especially in digital contexts with large volumes of data. Originality/Value: This study highlights the need for continuous alignment between technical innovation and humanistic principles in Brazilian scientific production. The analysis of public opinion research from the perspective of FAIR principles and Brazilian legal frameworks offers an original perspective on the ethical and methodological challenges faced by researchers.

Extracellular Vesicles in Aging and Age-Related Diseases. How Important Are They?

Extracellular vesicles (EVs), lipid bilayer-bound particles secreted by cells, have attracted significant research attention for their roles in aging-related disorders, including cardiovascular disease, metabolic dysfunction, cancer, and neurodegeneration. Research shows that EV cargo and function are influenced by factors including age, disease state, exercise, nutrition and sleep, and they may modulate various aging-associated processes such as stem cell renewal, nutrient sensing, cell senescence, mitochondrial function, and insulin resistance. This perspective highlights, for a general audience, a selection of studies of EVs in aging and age-related diseases, and their diverse roles in organ crosstalk. While current evidence indicates that EVs play multiple roles in aging, there are numerous challenges including methodological challenges and limitations, heterogeneous reports of EV cargo, limited reproducibility, and apparent context-dependent effects of EVs and their cargo. Together, this limits the interpretation of these studies. This is proposed that EVs may act as fine-tuners of cellular communication within the broader aging-associated secretome and the importance of standardizing methods are emphasized. Last, future directions for EV research are suggested.

Theorizing competition: An interdisciplinary framework

This paper introduces a framework to facilitate an interdisciplinary analysis of “competition.” While such an interdisciplinary analysis can be justified by referencing the various fields of social and economic life in which “competition” is important, three challenges are found to aggravate such endeavor: First, a challenge of scope, which refers to the generality of the concept of “competition” and the fact that contributions differ with regard to the competing actors, the objects of competition, as well as the institutional specificities of competition; second, a challenge of methodology, which exists because different scientific disciplines study competition with distinct methods and epistemological orientations, and, third, a challenge of normativity, which refers to the fact that debates about competition have been closely linked to controversial political debates. To mitigate these challenges, and to explicate the often implicit meta-theoretical assumptions in the scope, methodological, and normative dimension, this paper introduces a meta-theoretical framework. Its usefulness is illustrated via a comparative description of selected contributions from the social sciences and humanities. Despite its limited scope, it yields some preliminary conjectures that may inspire future research: first, there are sufficient common elements across different concepts of competition that justify an interdisciplinary approach to study competition; second, apart from differences between disciplines, there are remarkable differences within disciplines that are at least of similar importance. Finally, there are important interdependencies between the meta-theoretical dimensions considered in the framework.

Self-regulated Learning in the Digitally Enhanced Science Classroom: Toward an Early Warning System

Recent research underscores the importance of inquiry learning for effective science education. Inquiry learning involves self-regulated learning (SRL), for example when students conduct investigations. Teachers face challenges in orchestrating and tracking student learning in such instruction; making it hard to adequately support students. Using AI methods such as machine learning (ML), the data that is generated when students interact in technology-enhanced classrooms can be used to track their learning and subsequently to inform teachers so that they can better support student learning. This study implemented digital workbooks in an inquiry-based physics unit, collecting cognitive, metacognitive, and affective data from 214 students. Using ML methods, an early warning system was developed to predict students’ learning outcomes. Explainable ML methods were used to unpack these predictions and analyses were conducted for potential biases. Results indicate that an integration of cognitive, metacognitive, and affective data can predict students’ productivity with an accuracy ranging from 60 to 100% as the unit progresses. Initially, affective and metacognitive variables dominate predictions, with cognitive variables becoming more significant later. Using only affective and metacognitive data, predictive accuracies ranged from 60 to 80% throughout. Bias was found to be highly dependent on the ML methods being used. The study highlights the potential of digital student workbooks to support SRL in inquiry-based science education, guiding future research and development to enhance instructional feedback and teacher insights into student engagement. Further, the study sheds new light on the data needed and the methodological challenges when using ML methods to investigate SRL processes in classrooms.

Detecting Introspective Errors in Consciousness Science

Detecting introspective errors about consciousness presents challenges that are widely supposed to be difficult, if not impossible, to overcome. This is a problem for consciousness science because many central questions turn on when and to what extent we should trust subjects’ introspective reports. This has led some authors to suggest that we should abandon introspection as a source of evidence when constructing a science of consciousness. Others have concluded that central questions in consciousness science cannot be answered via empirical investigation. I argue that on closer inspection, the challenges associated with detecting introspective errors can be overcome. I demonstrate how natural kind reasoning—the iterative application of inference to the best explanation to home in on and leverage regularities in nature—can allow us to detect introspective errors even in difficult cases such as judgments about mental imagery, and I conclude that worries about intractable methodological challenges in consciousness science are misguided.

Deliberative Theory for Re‐Focusing the Concept, Measurement, and Analysis of Substantive Reasons in Political Discussions

ABSTRACTBased on theories of deliberative democracy, this article explores the substantive content of reasons as a conceptual tool. Although most studies focus on procedural dimensions, we argue that identifying and mapping substantive reasons contributes to advancing empirical investigation in a theoretically informed and normatively relevant way. More precisely, using a typology of reasons contributes to: (i) systematically tracking agreements and disagreements on controversial political issues and better understanding the complexities of political judgments, in a more nuanced and quantifiable description; (ii) deal with some contemporary methodological challenges to understand reciprocity and reflexivity; and (iii) explore disrespectful and undemocratic commitments in argumentation. In this context, we draw implications for broadening research at a macro‐level or systemic analysis. To elucidate the practical feasibility of the proposed measure, we offer examples of operationalization in different research designs and ways of engaging in rich and historically based explanations.