Multiple Perspectives Research Articles

Artificial Intelligence & the Capacity for Discrimination: The Imperative Need for Frameworks, Diverse Teams & Human Accountability

The increasing integration of Artificial Intelligence (AI) in various industries has led to concerns about how these systems can perpetuate discrimination, particularly in fields like employment, healthcare, and public policy. Multiple academic and business perspectives on AI discrimination, focusing on the need for global policy coordination and ethical oversight to mitigate biased outcomes, ask for our technical innovators to create contingencies that will better protect humanity’s experience with AI’s ever-expanding reach. Central to the key constructs such as biased datasets, algorithmic transparency, and the global governance of AI systems can function as a harmful drawback to these systems. Without adequate data governance and transparency, AI systems can perpetuate discrimination. AI's ability to discriminate stems primarily from biased data and the opacity of machine learning models, necessitating proactive research and policy implementation on a global scale. These frameworks must transcend the limitations of the experiences or perspectives of their programmers to ensure that AI innovations are ethically sound and that their use in global organizations adheres to principles of fairness and accountability. This synthesis will explore how these articles advocate for comprehensive, continuous monitoring of AI systems and policies that address both local and international concerns, offering a roadmap for organizations to innovate responsibly while mitigating the risks of AI-driven discrimination.

A multi-stakeholder engagement framework for material-building-city synergy through circular transformation

Scholars, industrial stakeholders, and governmental institutions are developing the circular economy paradigm. However, the emergence of multiple perspectives has challenged its implementation. As the industry that is the biggest contributor to the negative impacts on the environment, the construction industry stakeholders are paving the way for more sustainable as well as circular and regenerative construction by considering all actors in the system. Yet, the construction industry has a complex supply chain that requires clear strategies and stakeholder engagement across materials, buildings, and cities for efficient flows in the supply chain. Nonetheless, there is a need for improvement in the engagement of construction stakeholders for circular transformation. Therefore, this study aims to develop a multi-stakeholder engagement framework through circular transformation to guide the decision-makers for circular city governance. It has identified critical success factors by considering the construction stakeholders. The framework includes strategies at the micro (material), meso (building), and macro (city) scales to strengthen the material-building-city synergy. It's a significant step toward advancing circular city governance by bridging the gap between theoretical understanding and practical implementation and establishing a robust engagement for material-building-city synergy. The study employs a systematic literature review to extract strategies and natural language processing to analyze the strategies by topic modeling and defines critical success factors for multi-stakeholder engagement at multiscale. The outcome introduces the REVERT framework, bridging resource, envisagement, validation, entity, regulation, and technology, to facilitate a seamless transition by material-building-city synergy advancing circular city governance.

Clinical Data Flow in Botswana Clinics: Protocol for a Mixed-Methods Assessment.

Botswana has made significant investments in its health care information infrastructure, including vertical programs for child health and nutrition, HIV care, and tuberculosis. However, effectively integrating the more than 18 systems in place for data collection and reporting has proved to be challenging. The Botswana Health Data Collaborative Roadmap Strategy (2020-24) states that "there exists parallel reporting systems and data is not integrated into the mainstream reports at the national level," seconded by the Botswana National eLearning strategy (2020), which states that "there is inadequate information flow at all levels, proliferation of systems, reporting tools are not synthesized; hence too many systems are not communicating." The objectives of this study are to (1) create a visual representation of how data are processed and the inputs and outputs through each health care system level; (2) understand how frontline workers perceive health care data sharing across existing platforms and the impact of data on health care service delivery. The setting included a varied range of 30 health care facilities across Botswana, aiming to capture insights from multiple perspectives into data flow and system integration challenges. The study design combined qualitative and quantitative methodologies, informed by the rapid assessment process and the technology assessment model for resource limited settings. The study used a participatory research approach to ensure comprehensive stakeholder engagement from its inception. Survey instruments were designed to capture the intricacies of data processing, sharing, and integration among health care workers. A purposive sampling strategy was used to ensure a wide representation of participants across different health care roles and settings. Data collection used both digital surveys and in-depth interviews. Preliminary themes for analysis include perceptions of the value of health care data and experiences in data collection and sharing. Ethical approvals were comprehensively obtained, reflecting the commitment to uphold research integrity and participant welfare throughout the study. The study recruited almost 44 health care facilities, spanning a variety of health care facilities. Of the 44 recruited facilities, 27 responded to the surveys and participated in the interviews. A total of 75% (112/150) of health care professionals participating came from clinics, 20% (30/150) from hospitals, and 5% (8/150) from health posts and mobile clinics. As of October 10, 2023, the study had collected over 200 quantitative surveys and conducted 90 semistructured interviews. This study has so far shown enthusiastic engagement from the health care community, underscoring the relevance and necessity of this study's objectives. We believe the methodology, centered around extensive community engagement, is pivotal in capturing a nuanced understanding of the health care data ecosystem. The focus will now shift to the analysis phase of the study, with the aim of developing comprehensive recommendations for improving data flow within Botswana's health care system. DERR1-10.2196/52411.

Synergistic adsorption mechanism of novel combined collector in flotation separation of fayalite from magnetite: Experiments and MD simulations

As an iron-bearing silicate mineral, fayalite has similar surface properties to magnetite, which makes it challenging to efficiently separate fayalite from magnetite by flotation. In this study, magnetite and fayalite were separated using novel combined collector polyoxyethylene alkyl amine (PAE) and terpinol (AT) through reverse flotation. Combined with a variety of characterization analysis and molecular dynamics (MD) simulation, the synergistic adsorption mechanism of the combined collector on mineral surface was revealed from multiple perspectives. The optimum scheme and pH response range of the combined collector were determined by micro-flotation test. Compared with PAE alone, the difference value of flotation recovery between fayalite and magnetite using combined collector was increased by 10.16%, and the dosage was reduced by 20%. Additionally, an effectively adsorption of combined collector onto fayalite surface to improve its surface hydrophobicity. Fourier transform infrared (FTIR), X-ray photoelectron spectroscopy (XPS) and MD simulations results showed synergistic adsorption was produced onto fayalite surface using combined collector mainly through non-polar interaction, hydrogen bond association and bridge adsorption. Furthermore, the spatial distribution and diffusion behavior of water molecules provide microscopic evidence for the enhancement of surface hydrophobicity of fayalite by combined collector.

Using virtual fieldwork to support interprofessional and community-based learning: students from four faculties investigate life on a remote island in Japan

IntroductionThe Mishima Islands in Kagoshima Prefecture, Japan, are remote islands designated as medically underserved. Supporting the people living in such remote islands requires a multifaceted examination of the livelihoods of the residents, including their health, work, and living environment.MethodsStudents from the faculties of nursing, veterinary science, agriculture, and engineering participated in a 3-day virtual fieldwork session held synchronously from the campus of Kagoshima University and on a remote island in the Mishima Mura island group in Kagoshima Prefecture, Japan. Coursework and post-course surveys were analyzed to determine student engagement and learning. In particular, the analysis considered learning in relation to interprofessional learning and community-based learning.Results and discussionStudents expressed being able to conduct fieldwork online, collaborate with students from multiple faculties and graduate schools, and apply their expertise. These outcomes were reflected in the five-point Likert scale evaluation responses, highlighting a sense of accomplishment and satisfaction. Virtual fieldwork can contribute to student learning by introducing new concepts and allowing them to consider development issues in remote areas from multiple perspectives.

Digital sexual literacy: the potential for online spaces to support sexual literacy among young people

IntroductionHealth literacy is defined as the capacity, resources and insight to seek information, ask critical questions and be an advocate for one’s health. Sexual literacy is framed in similar terms, relating to the capacity to navigate emotionally and physically safe sexual encounters and to hold a critical and assertive stance on relationship dynamics, gender and consent. In this paper, we present findings from a qualitative study that explored how young people’s use of the internet develops sexual literacy.MethodsIn-depth interviews were conducted with 22 young people (aged 18–25). Interviews explored the ways in which young people participate in online spaces to learn about sex, relationships, bodies and other aspects of sexual health. Data were analysed using qualitative, inductive thematic coding techniques.ResultsYoung people described learning about sexual health online as a process of exploring ideas, topics or experiences (going down ‘rabbit holes’). It was rarely the case that young people searched for specific information. Young people described engaging with multiple sources of content and multiple people online to learn about relationships or sex. This was often personal. Young people wanted to understand their own experiences and hear stories from people with similar experiences. Engaging with multiple sources, stories and people enabled critical reflection and peer-based learning.DiscussionBuilding sexual health literacy is not simply about providing young people with sexual health information. Rather, it is about supporting young people to develop critical insight and capacity to reflect deeply on experiences and issues that affect their sexual relationships. The online environment supports sexual literacy as it enables critical inquiry, access to multiple perspectives, peer connection and personal reflection.

Undervisningsutveckling genom analyser av elevers lärande av handlingskompetens gällande antibiotikaresistens

The aim of this study is to investigate how teaching about antibiotic resistance and action competence can be designed and modelled based on analyses of student learning. Analyses are made of students’ privileging when asked to discuss complex tasks related to antibiotic resistance where knowledge from multiple disciplines and perspectives is relevant to use and where different courses of action are possible. Teaching were performed in two cycles with a didactic analysis and remodeling of teaching in between. The results make visible how teaching can be modelled by making space for subject knowledge as well as values and priorities. With a sharper focus in the modified teaching, students could debate the content that the teaching was aimed at. We discuss the challenges of finding content that is relevant and engaging for students and that also draws attention to multiple perspectives and potential conflicts of interest on the issue.

Recent Advances in Photocatalytic Nitrogen Fixation Based on Two‐Dimensional Materials

AbstractUtilizing solar energy for photocatalytic nitrogen fixation has been extensively regarded as a promising avenue towards renewable nitrogen energy production. 2D materials, renowned for their unique structures and properties, have sparked remarkable advancements in the realm of energy and environment sciences. In this review, we delve into the recent advancements pertaining to 2D materials for solar‐driven photocatalytic nitrogen fixation. This encompasses a diverse array of materials, including metal carbides/nitrides (MXenes), transition metal dichalcogenides (TMDs or TMDCs), graphitized carbon nitride (g‐C3N4), metal‐organic frameworks (MOFs), covalent organic frameworks (COFs), and covalent triazine‐based frameworks (CTFs). We offer a fundamental understanding of the functionalities exhibited by various 2D materials in enhancing photocatalytic nitrogen fixation activity. The photocatalytic properties of these 2D materials are meticulously examined from multiple perspectives, including visible light absorption capabilities, charge transfer within the energy band, and reaction mechanisms. Finally, we present an in‐depth discussion on the existing challenges and prospects surrounding the application of 2D materials in photocatalytic nitrogen fixation.

Automatic Exchange of Financial Information from the Perspective of Tax Fairness and Transparency

<p>Tax fairness and transparency concepts have gained increasing prominence in the global tax and financial regulatory landscape in recent years. Automatic Exchange of Financial Information (AEOI) has proven to be an effective tool for implementing these principles. This executive summary briefly overviews the key role AEOI plays in promoting tax fairness and transparency. It explores the mechanisms and framework behind AEOI and highlights its impact on curbing tax evasion and improving the fair sharing of the tax burden between individuals and businesses. Additionally, this summary highlights the implications of AEOI for financial institutions, governments, and taxpayers, and highlights the challenges and benefits associated with its implementation. By considering AEOI from multiple perspectives, this summary aims to contribute to the ongoing international tax discussion and facilitate further research on the multiple dimensions of tax fairness and transparency in the global financial ecosystem.<em></em></p>

Exploring Children’s Journeys into the Youth Justice System from Multiple Perspectives: An Interpretative Phenomenological Analysis

In order to contribute to research into youth crime prevention, the current study aimed to explore the experiences of children in conflict with the law prior to receiving their first caution or conviction. Participants were recruited at a Youth Justice Service and included five children working with the service, their five case managers, and two of their parents/guardians. Data was collected using semi-structured interviews and timelines were used to support the children in sharing their experiences. Data was analysed using Interpretative Phenomenological Analysis, which focuses on understanding how individuals experience the world and how they make sense of those experiences. Six group experiential themes were identified within the analysis. These consider the events leading up to offending and whether it could have been predicted, the extent to which the children felt supported, the effect of school practices, the impact of peer influences, difficulties with managing emotions, and the children’s readiness for change. Each theme is discussed and recommendations for practice and future research are considered. The importance of placing children’s experiences at the forefront of research is highlighted throughout.

Risk Factors for Disease Aggravation in Older Patients With Non-communicable Diseases: Interpretive Structural and Hierarchical Holographic Modelling.

This study aimed to analyse and determine the risk factors of disease aggravation faced by older patients with non-communicable diseases (NCDs) and their interwoven correlations. We employed a descriptive and cross-sectional study, which followed the STROBE guidelines for reporting. We conducted a semi-structured in-depth interview with 26 older patients with NCDs from a hospital in Qingdao, China between July and August 2022 on the basis of the literature review. Then, we analysed data using the directed content analysis and determined risk factors through a focus group discussion and the Delphi consultation. Afterward, we combined interpretive structural modelling and hierarchical holographic modelling to construct a hierarchical structure model and drew relationship framework diagrams to exhibit diversified risk factors and complex interwoven correlations. We identified 30 risk factors from individual, interpersonal, organisational, community and policy levels. The hierarchical structure model constructed by interpretive structural modelling demonstrated a four-layer structure, and the individual and interpersonal levels were at the highest layer. The relationship framework diagrams demonstrated the identification process of risk factors and interwoven correlations at individual and organisational levels. Risk factors causing disease aggravation amongst older patients with NCDs are diverse. To delay the progression of NCDs, we should comprehensively explore risk factors, interpret the root causes and effects of risks from multiple perspectives and consider the interaction amongst multi-level risk factors to develop precise measures related to risk control. Controlling risk factors is an effective measure to postpone disease aggravation. Through this study, we provide a scientific and comprehensive basis for clinical risk screening so that healthcare providers can sense potential risk factors for disease aggravation in older patients' surroundings and formulate targeted nursing measures according to the risk factors faced by different patients. Patients participated in interviews to supplement risk factors included in our study. Experts provided opinions on the inclusion, exclusion and modification of risk factors.

The role and mechanism of CRISPLD2 in skin fibrosis of systemic sclerosis.

Systemic sclerosis (SSc) is an autoimmune connective tissue disease involving multiple organs. The most common clinical symptom of SSc is progressive fibrosis of the skin, and the pathologically manifestations of skin were activation and proliferation of fibroblasts and continuous proliferation of extracellular matrix. Transforming growth factor β (TGFβ) can promote the proliferation and activation of fibroblasts, causing excessive deposition of collagen and structural proteins. Therefore, exploring the specific mechanism of TGFβ-related pathway on fibrosis is of great significance for improving skin fibrosis in SSc. Genes related to TGFβ pathway were screened through bioinformatics analysis, and SSc phenotypes were verified in vivo and vitro. The relevant molecular mechanisms were preliminarily discussed in combination with transcriptome sequencing. Human cysteine-rich secreted protein LCCL domain protein 2 (CRISPLD2) was found increased reactivity in TGFβ induced fibroblasts, and the expression of ACTA2 (ɑ-SMA) decreased significantly in TGFβ-mediated fibroblasts with up-regulation of CRISPLD2. CRISPLD2 was found increased reactivity in TGFβ induced fibroblasts, and we further confirmed that CRISPLD2 can participate in TGFβ induced fibroblast fibrosis from multiple perspectives and levels in negative feedback regulation, and investigated the mechanism of CRISPLD2 in fibrosis.

Automated Recognition of Snow-Covered and Icy Road Surfaces Based on T-Net of Mount Tianshan

The Tianshan Expressway plays a crucial role in China’s “Belt and Road” strategy, yet the extreme climate of the Tianshan Mountains poses significant traffic safety risks, hindering local economic development. Efficient detection of hazardous road surface conditions (RSCs) is vital to address these challenges. The complexity and variability of RSCs in the region, exacerbated by harsh weather, make traditional surveillance methods inadequate for real-time monitoring. To overcome these limitations, a vision-based artificial intelligence approach is urgently needed to ensure effective, real-time detection of dangerous RSCs in the Tianshan road network. This paper analyzes the primary structures and architectures of mainstream neural networks and explores their performance for RSC recognition through a comprehensive set of experiments, filling a research gap. Additionally, T-Net, specifically designed for the Tianshan Expressway engineering project, is built upon the optimal architecture identified in this study. Leveraging the split-transform-merge structure paradigm and asymmetric convolution, the model excels in capturing detailed information by learning features across multiple dimensions and perspectives. Furthermore, the integration of channel, spatial, and multi-head attention modules enhances the weighting of key features, making the T-Net particularly effective in recognizing the characteristics of snow-covered and icy road surfaces. All models presented in this paper were trained on a custom RSC dataset, compiled from various sources. Experimental results indicate that the T-Net outperforms fourteen once state-of-the-art (SOTA) models and three models specifically designed for RSC recognition, with 97.44% accuracy and 9.79% loss on the validation set.

Evaluation and the mechanism of ShengXian and JinShuiLiuJun decoction in the treatment of silicotic fibrosis: An integrated network pharmacology, life omics, and experimental validation study

BackgroundSilicosis is a systemic disease characterized by extensive fibrosis due to prolonged exposure to silica dust, with rising incidence rates significantly impacting global public health. ShengXian and JinShuiLiuJun Decoction (SXD) is a Chinese medicinal preparation containing a variety of medicinal plants. It has shown notable clinical efficacy in treating silicotic fibrosis in China. However, the precise mechanisms underlying its therapeutic effects remain unclear. This study integrates network pharmacology, multi-omics analysis, and experimental validation to investigate the potential mechanisms by which SXD treats silicotic fibrosis. ObjectiveThe study aims to investigate the therapeutic efficacy of SXD in treating silicotic fibrosis and to elucidate its underlying molecular mechanisms. MethodsHPLC-Q-TOF-MS was used to identify the active components of SXD, and combined with network pharmacology, metabolomics, and transcriptomics, the mechanism of SXD in treating silicotic fibrosis was explored from multiple perspectives. The therapeutic effect of SXD was assessed through HE staining, Masson staining, Micro CT imaging, pulmonary function tests, and hydroxyproline content in lung tissue. Finally, network pharmacology and multi-omics findings were validated using molecular docking. CETSA, immunofluorescence, SPR, and Western blotting were used to analyze key factors in the NF-κB pathway at the animal, cellular, and molecular levels. ResultsSXD treatment improved lung function in silicosis rats, reduced inflammatory cell infiltration, collagen deposition, fibrosis and other pathological changes, and inhibited the protein expression of TNF-α, IL-17A, and IL-1β, and NF-κB in lung tissue. HPLC-Q-TOF-MS combined with network pharmacology identified key compounds such as Liquiritigenin, 3-Methoxynobiletin, Isomangiferin, Hesperidin, shogaol, and Ligustroflavone, which likely exert therapeutic effects through the TNF, IL-17, NF-κB, and TGF-β signaling pathways. Transcriptomics and metabolomics results revealed that SXD up-regulated the expression of NF-κB pathway-related genes (NFKBIA, NFKBIZ) and key regulators of the retinol metabolism pathway, while down-regulating pro-inflammatory genes (IL1B, IL17A, IL6). Experimental findings confirmed that SXD suppressed the expression of NF-κB pathway-related proteins and upstream activators TNF-α, IL-17A, and IL-1β, as well as their receptors, in both lung tissue and cellular models. Additionally, SXD-containing serum had a direct, non-toxic effect on MRC-5 cells, effectively inhibiting collagen expression and TGF-β secretion. SXD also had a positive effect on collagen production and extracellular matrix (ECM) aggregation in fibroblasts. Molecular dynamics studies showed that SXD directly binds to NF-κB and IκB. ConclusionSXD exerts therapeutic effects on silicotic fibrosis by inhibiting NF-κB signaling transduction mediated by TNF-α, IL-17A, and IL-1β, and suppressing fibroblast activation.

Microfinance and Women's Economic Empowerment in Rural India

Self-help groups (SHGs) have played a crucial role in empowering women in India. They have provided a platform for women to come together, share experiences, and support each other in improving their social and economic conditions. This review traverses through the history of microfinance initiatives in India through SHGs, analyses the theoretical framework of SHGs, and integrates multiple perspectives to understand their formation, functioning, and impact on the role of microfinance initiatives on women's economic status and empowerment in rural India. The existing models of microcredit and microfinance are compared, along with the analysis of dynamic risk default and an overview of their mathematical models.

RobustMap: Visual Exploration of DNN Adversarial Robustness in Generative Latent Space.

The paper presents a novel approach to visualizing adversarial robustness (called robustness below) of deep neural networks (DNNs). Traditional tests only return a value reflecting a DNN's overall robustness across a fixed number of test samples. Unlike them, we use test samples to train a generative model (GM) and render a DNN's robustness distribution over infinite generated samples within the GM's latent space. The approach extends test samples, enabling users to obtain new test samples to improve feature coverage constantly. Moreover, the distribution provides more information about a DNN's robustness, enabling users to understand a DNN's robustness comprehensively. We propose three methods to resolve the challenges of realizing the approach. Specifically, we (1) map a GM's high-dimensional latent space onto a plane with less information loss for visualization, (2) design a network to predict a DNN's robustness on massive samples to speed up the distribution rendering, and (3) develop a system to supports users to explore the distribution from multiple perspectives. Subjective and objective experiment results prove the usability and effectiveness of the approach.

A long-term power system state calculation method using sequential power and holomorphic embedding

Traditional Power Flow (PF) calculations struggle to support the data analysis requirements as the power system develops. Therefore, this paper introduces a non-sequential and non-iterative PF calculation method to enhance the computational speed and analytical efficiency of power system state variables over long time scales. Firstly, a Power Sequential Division (PSD) method is proposed, representing the power curve using hierarchical energy with temporal characteristics. Next, based on the Fast and Flexible Holomorphic Embedding (FFHE) method, the paper explores the incremental properties of Voltage Power Series Coefficients (VPSC) and proposes an Incremental Holomorphic Embedding (IHE) method. Finally, by combining PSD and IHE, this study introduces a Power Sequential Flow (PSPF) method for rapid computation of power system state variables over long time scales. Unlike traditional PF methods, PSPF method shifts away from time-series calculations and instead analyzes the distribution of power system state variables over long time scales from an energy perspective. Various test cases are set up to analyze the performance characteristics of IHE and PSPF from multiple perspectives. The results indicate that the proposed methods significantly outperform traditional PF methods in terms of computational speed, adaptability to test cases, and analytical efficiency.

REVEALS: an open-source multi-camera GUI for rodent behavior acquisition.

Deciphering the rich repertoire of mouse behavior is crucial for understanding the functions of both the healthy and diseased brain. However, the current landscape lacks effective, affordable, and accessible methods for acquiring such data, especially when employing multiple cameras simultaneously. We have developed REVEALS (Rodent Behavior Multi-Camera Laboratory Acquisition), a graphical user interface for acquiring rodent behavioral data via commonly used USB3 cameras. REVEALS allows for user-friendly control of recording from one or multiple cameras simultaneously while streamlining the data acquisition process, enabling researchers to collect and analyze large datasets efficiently. We release this software package as a stand-alone, open-source framework for researchers to use and modify according to their needs. We describe the details of the graphical user interface implementation, including the camera control software and the video recording functionality. We validate results demonstrating the graphical user interface's stability, reliability, and accuracy for capturing rodent behavior using DeepLabCut in various behavioral tasks. REVEALS can be incorporated into existing DeepLabCut, MoSeq, or other custom pipelines to analyze complex behavior. In summary, REVEALS offers an interface for collecting behavioral data from single or multiple perspectives, which, when combined with deep learning algorithms, enables the scientific community to identify and characterize complex behavioral phenotypes.

Efficient separation of diisopropyl ether and ethanol using green solvents: Thermodynamic analysis and process simulation

Ionic liquids (ILs) and deep eutectic solvents (DESs) were first proposed as green solvents to study and compare their azeotropic separation effects in diisopropyl ether (DIPE) and ethanol (EA). The COSMO-RS model was used to screen ILs/DESs, and the feasibility of the extractants were comprehensively analyzed in terms of viscosity, thermal stability, and toxicity. Experiments were carried out on selected ILs and DESs, and the results showed that the separation effect of ILs was significantly stronger than that of DESs. Quantum chemical calculations and molecular dynamics simulations were combined to comprehensively analyze and compare the microscopic mechanisms of extraction separation. Based on the experimental data, a DIPE-EA extraction-coupled separation process was designed. For the first time, life cycle analysis was used to comprehensively evaluate the impact of this process on human health and the environment from multiple perspectives, providing theoretical guidance for the separation of alcohol ethers in the industry.

"Each person's experience, each person's needs": How therapists open space for multiple perspectives during reflecting team family therapy supervision.

Tom Andersen's reflecting team approach invited family members to hear the dialogues between professionals in response to their stories. This study aimed to explore intersubjective reflexivity during reflecting team group supervision, by observing resonances between group members. Twelve hours of reflecting team group supervision was transcribed and analyzed using Systemic Functional Linguistics. Hallidayan transitivity analysis of selected sections of interaction focused on mental processes, and Martin and White's appraisal framework was used to explore the "engagement" between different voices at play in the conversations. We found the use of physical metaphor to express reflective experiences, multi-voiced expressions to expand perspectives and possibilities, and dialogical patterns of relating between group therapy participants. Understanding dialogism from a linguistic perspective may offer insights into how reflective dialogues work and support understanding of fidelity for approaches which engage reflecting teams.