Exploratory Framework Research Articles

From stakeholders to protagonists: an exploratory framework for cultivating prosocial capacities for development

The world in 2024 faces numerous interlinked crises, including climate change and water shortages, rising geopolitical tensions, and a new awareness of the risks of pandemics. These crises reverse decades of incremental development progress and humanity’s aspirations embodied in the 2030 Agenda for Sustainable Development, necessitating a more active and collaborative participation of development stakeholders. The magnitude of challenges points to the need for transformational approaches to releasing the potential of stakeholders, which requires building on and extending beyond current best practices in participation and capacity strengthening. What is most needed today is a balanced assessment of the complexity of human nature and a vision that recognizes the prosocial potential of people to harmonize the pursuit of personal interests with a willingness to contribute to social and collective development goals. Prosociality is a capacity that all stakeholders can strengthen—from individuals to institutions to communities (including different forms of social groupings). These stakeholders can become empowered as active protagonists of development, with the potential to work synergistically to achieve the Sustainable Development Goals (SDGs). In order to work with these protagonists, it is important to view them systematically in terms of key characteristics such as their antecedent knowledge, values and culture, stance, agency, roles, relationships, and learning.

Identifying Clusters of Curatively-Treated Esophageal Cancer Patients Using Patient-Reported Outcome Measures at Three Months from Discharge: A Secondary Analysis from a Longitudinal Single Center Study.

This study aims to identify meaningful clusters based on Patient-Reported Outcome Measures (PROMs) in curatively-treated esophageal cancer patients at three months post-discharge. This secondary analysis of a longitudinal single-center study included 46 esophageal cancer patients who underwent curative surgery. Patients were selected based on their completion of PROMs surveys at three months post-discharge, were aged 18 years or older, and had undergone surgical resection (esophagectomy) with or without neoadjuvant chemotherapy and/or radiotherapy. The analysis utilized t-distributed Stochastic Neighbor Embedding (t-SNE) for dimensionality reduction and hierarchical clustering to analyze PROMs data collected three months post-discharge. Clustering was performed on physical, emotional, cognitive, and social functioning variables, symptom burden, and health literacy. Three distinct clusters were identified: Cluster 1 (n = 24) with higher functioning and moderate symptoms, Cluster 2 (n = 14) with moderate functioning, higher symptoms, and lower health literacy, and Cluster 3 (n = 8) with the highest functioning, lowest symptoms, and highest health literacy. Significant differences between squamous cell carcinoma and adenocarcinoma subtypes were observed across several PROMs domains, including critical health literacy, general health status/quality of life, nausea and vomiting, and insomnia. These clusters provide an exploratory framework for tailoring post-operative interventions to enhance patient recovery, which necessitates further confirmatory investigations, including outcomes such as complications and mortality, in the analysis. This study fills a research gap by demonstrating the utility of PROMs in identifying distinct recovery patterns in esophageal cancer patients post-surgery. The findings support the use of PROMs to guide personalized post-operative care, potentially improving patient outcomes and quality of life. Further research is needed to validate these findings in larger, diverse populations.

An Exploratory Community-Engaged Framework to Operationalize Building Bridges between Public Library Education and Praxis

This article explores an exploratory/emerging framework collaboratively developed by a public librarian and library and information science (LIS) educator to operationalize community-engaged programming through critical autoethnographic reflections from Williams (first author) doctoral journey. The authors propose a novel framework, emphasizing elements crucial to a community-embedded approach, and employ a critical autoethnographic methodology to provide a nuanced understanding focused on generating community impact. The findings apply elements of a community-engaged framework in relation to both authors’ contributions with regard to the performance of their roles as a public librarian (and doctoral student) and LIS educator respectively. The discussion critically addresses the emerging challenges and presents opportunities with potential implications for LIS education.

Exploratory parallel hybrid sampling framework for imbalanced data classification

Current engineering application scenarios often face the challenge of imbalanced data, hybrid sampling is an effective method to deal with the imbalanced data classification issue, which can avoid the issues of overfitting and mistakenly deleting useful majority samples when using oversampling approach and undersampling approach alone. However, at present most of the hybrid sampling approaches are implemented serially, and the implementation of oversampling and undersampling approaches alone will cause mutual interference and influence between them. This study proposes a parallel hybrid sampling framework based on the idea of parallel engineering and theoretically analyzes its superiority. The experimental results show that when applied to five classification algorithms with three performance evaluation metrics,the proposed framework outperforms the two mainstream hybrid sampling frameworks. Moreover, the proposed framework can effectively reduce the time consumption of hybrid sampling process.

Clustering computer mouse tracking data with informed hierarchical shrinkage partition priors.

Mouse-tracking data, which record computer mouse trajectories while participants perform an experimental task, provide valuable insights into subjects' underlying cognitive processes. Neuroscientists are interested in clustering the subjects' responses during computer mouse-tracking tasks to reveal patterns of individual decision-making behaviors and identify population subgroups with similar neurobehavioral responses. These data can be combined with neuroimaging data to provide additional information for personalized interventions. In this article, we develop a novel hierarchical shrinkage partition (HSP) prior for clustering summary statistics derived from the trajectories of mouse-tracking data. The HSP model defines a subjects' cluster as a set of subjects that gives rise to more similar (rather than identical) nested partitions of the conditions. The proposed model can incorporate prior information about the partitioning of either subjects or conditions to facilitate clustering, and it allows for deviations of the nested partitions within each subject group. These features distinguish the HSP model from other bi-clustering methods that typically create identical nested partitions of conditions within a subject group. Furthermore, it differs from existing nested clustering methods, which define clusters based on common parameters in the sampling model and identify subject groups by different distributions. We illustrate the unique features of the HSP model on a mouse tracking dataset from a pilot study and in simulation studies. Our results show the ability and effectiveness of the proposed exploratory framework in clustering and revealing possible different behavioral patterns across subject groups.

Exploring the Impact of a Structured Educational Approach on Peristomal Skin Complications: An Interim Analysis.

This study, employing an interim analysis, investigates the effects of the Dermamecum protocol, a structured educational and tailored approach that stratifies ostomy patients into risk paths (green, yellow, red) based on pre-operative and post-operative characteristics. The green path indicates a low risk of peristomal skin complications (PSCs), focusing on sustaining healthy behaviours and basic stoma care. The yellow path represents a moderate risk, emphasizing the need for patients to self-monitor and recognize early signs of complications. The red path corresponds to high risk, requiring stringent monitoring and immediate access to healthcare support. The study aims to reduce PSCs and improve patient outcomes. Methods include the stratification of 226 patients, with significant differences in gender distribution, BMI categories, and stoma types across the paths. Results show an occurrence rate of PSCs of 5.9% in all risk paths (5.7% green path, 4.7% yellow path, and 7.9% red path, p = 0.685), significantly lower than the median rate of 35% reported in the literature. Multiple correspondence analysis validated the stratification, with distinct clusters for each path. Poisson regression models in the exploratory framework of an interim analysis identified male gender as the only significant predictor of PSCs, indicating the need for gender-specific interventions. The findings suggest that the Dermamecum protocol effectively reduces early PSCs, providing a foundation for further research.

Barriers for bariatric surgery in provincial New Zealand: a qualitative analysis.

Bariatric surgery is the most effective treatment for people with obesity. It has been shown that there's is a complex psychosocial overlay in the pathophysiology and treatment, which requires specific consideration when delivering care. There is a significant drop out rate for patients accepted on to bariatric programmes in New Zealand, resulting in failure to progress to surgical intervention. We conducted individual, semi-structured interviews with patients who were accepted onto the bariatric surgery programme but did not complete the programme, or receive an operation between 2015 and 2020. Grounded theory methodology was used to create an exploratory framework to identify and describe the themes encountered. An iterative process of thematic analysis and comparison between participants experiences was used to consolidate the shared key barriers. This study aims to explore patients experiences of a bariatric surgery programme to understand barriers and enablers to complete a bariatric programme and receive an operation. Adding to previous qualitative work investigating patients experience of bariatric surgery programmes in New Zealand. Five themes of barriers that patients face to receiving bariatric surgery were identified. These were preoperative weight loss requirement, experiencing the social stigma of obesity, communication, socioeconomic and geographic barriers, and community support. These five themes often co-exist in patients experiences and combine, to cause patients to disengage with the bariatric service. Many factors contribute to eligible patients not receiving bariatric surgery once accepted onto the programme. Specified weight loss goals was the most significant barrier. Community support and online resources were significant enablers. This study should inform changes to bariatric programmes in New Zealand.

School Funding and Equity in Australia: Critical Moments in the Context of Text Production Phase of the Education Policy Cycle

Education policy has long been analyzed as a cycle where various actors influence different stages. However, few such studies have focused on identifying and interrogating the specific moments that shape an education policy’s overall equity trajectory. This article uses Bowe, Ball, and Gold’s policy cycle as an exploratory theoretical framework, focusing on the historic Review of Funding for Schooling (2011) in Australia, which evaluated the nation’s school funding policy. The authors concentrate on the Context of Text Production phase of its policymaking process, and consider the implications for equity. From this work, the authors highlight two “critical moments” that they argue significantly influenced how the appointed panel approached equity. Their theorizing of a “critical moment” offers valuable insights for researchers and stakeholders seeking to understand or influence education policy.

Modeling Group Discourse with Epistemic Network Analysis: Unpacking Connections, Perspectives, and Individual Contributions

AbstractOperationalizing and modeling group talk has proved to be a consistent challenge in educational research. In this paper, we suggest that epistemic network analysis (ENA) could provide unique insights concerning group talk. Specifically, we use ENA to model the talk orientations put forward in the Exploratory Talk framework (Cumulative, Disputational, Exploratory). Participants (n = 60, 67% female, 33% male) were undergraduate students in an Introduction to Psychology course who took part in three 90-min collaborative online tasks. We coded student discourse according to a set of basic communicative acts reflective of the Exploratory Talk framework. Then, using ENA, we identified different groups’ patterns of discourse at the group and individual level. Presenting the epistemic networks of four purposefully chosen groups, this paper offers three key contributions to modeling and conceptualizing group dialogue: (1) illustrating how ENA could offer new ways to analyze group talk by focusing on the frequency of co-occurrence of connections between a basic set of communicate acts rather than the different communicative acts used; (2) refining the theoretical conceptualization of Exploratory Talk by distinguishing two sub-variations—other-oriented vs. self-oriented Exploratory Talk—that differ according to the depth of engagement with other perspectives; (3) examining how ENA allows unpacking diverging dynamics of individual contributions to group discourse, focusing on the role of individuals that function as “instigators” or “connectors.”

An exploratory framework to identify dust on photovoltaic panels in offshore floating solar power stations

Offshore floating solar power stations represent a new frontier in energy development. These stations maximize solar energy use, reduce land consumption and promote algae growth. However, moist marine air makes airborne particles like sand adhere more easily to PV panels, reducing photoelectric conversion efficiency and causing overheating and potential damage. Additionally, drones and fixed cameras face challenges due to complex maritime factors and complicating image analysis. To address these issues, this paper proposes an exploratory framework for identifying dust regions on photovoltaic panels specifically for offshore floating solar power stations. The framework aims to address how to accurately and reliably identify dust accumulation on PV panels, even in images with complex background interference. The framework is designed to be compatible with existing image recognition models and incorporates specialized enhancement modules to further improve recognition accuracy and efficiency. In this study, we utilized a dust feature enhancement module based on the HLS color space, combined with Mask R-CNN, to demonstrate the framework's feasibility and effectiveness. Experimental results show that, compared to a single Mask R-CNN, the framework significantly reduces misidentification and omission of dust areas, boosting the confidence level and effectively meeting the operational needs of offshore floating solar power stations.

Can digital transformation or metamorphosis alleviate staff shortages?

PurposeThis study investigates the roles of technological development, artificial intelligence (AI), and robots in the catering business and their effects on the labor force and staff shortages.Design/methodology/approachThe impact of AI, robots, and technological advancements on the labor force is outlined. Secondary sources and the results of interviews with two highly experienced experts were used to identify the transformation or metamorphosis that affects the staff situation during catering operations. This is an opinion paper that explains why it contains many of the authors’ observations and experiences.FindingsTechnological equipment and AI have been used frequently in catering operations, and robots are not grooved. In the near future, these tools could be part of a solution for the staff shortage because, with their help, less-skilled workers could be doing some of the job in the catering business.Research limitations/implicationsThe study is written from a limited perspective because of its qualitative nature and the fact that it is shaped by expert opinions and the author's views. It is expected that conducting the same study with a larger number of experts operating in various fields on similar topics in the future will lead to more effective results.Practical implicationsThis research character is an opinion paper and qualitative. This research aims to explain the use of artificial intelligence, technological advancements, and robots and to explore their impacts on the labor force based on the opinions of experts and the author. The data obtained are expected to be useful for future labor force planning in catering businesses.Social implicationsThis study provides an exploratory framework regarding staff shortages in catering businesses and the integration of technology, AI and robots.Originality/valueBased on a literature review, only a limited number of studies have been conducted on the use of technology, artificial intelligence, and robots in kitchens. None of these studies investigated their impact on the labor force. Therefore, this study is both original and valuable.

Digesting Psychology: Metadiscourse as a Recontextualising Tool in the Digital Communication of Disciplinary Research

Abstract Metadiscourse was initially conceived as a tool to explore mainly academic, monologic, written texts. When applied to the study of digital discourse, metadiscourse may reveal itself as a rather frustrating exploratory framework, firstly because of the problematised interaction between writer and reader in an online context where audiences are not easily identifiable and, secondly, because of the multimodal character of digital texts. In the present study, the role played by interactive metadiscoursal resources in online communication has been explored. For such purposes, the focus has been placed on a corpus of digital research digests published on the website of the British Psychological Society, the most important representative body of the profession in the United Kingdom. The study revolves around the function of interactive metadiscourse as a recontextualising tool to facilitate the understanding of complex disciplinary knowledge for potentially less expert readers. Several interactive resources (code glosses, evidentials, transition markers, and frame markers) are identified, which strategically contribute to the mediation of specialised information and which also take advantage of the affordances offered by digital platforms.

From Tweets to Energy Trends (TwEn): An exploratory framework for machine learning-based forecasting of urban-scale energy behavior leveraging social media data

Understanding energy behavior is crucial in addressing climate change, yet the accuracy of energy predictions is often limited by reliance on oversimplified occupancy data. This study develops an exploratory framework, from Tweets to Energy Trends (TwEn), leveraging machine learning and geo-tagged social media data to investigate the social dynamics of urban energy behavior. TwEn explores the relationship between social media interactions, specifically the frequency of tweets using data from the X Platform, and energy use patterns on an hourly basis. Employing various machine learning models, including artificial neural networks (ANN), decision tree (DTREE), random forest (RDF), and gradient boosting machine (GBM), the study evaluates their efficiency in both static and time-series forecasting of energy use trends and investigates the capability of social media data in predicting urban energy patterns. In addition, the study carries out a series of sensitivity analyses to provide an examination of the data and models. Furthermore, comprehensive data acquisition methods are developed and implemented. Tested on New York City using actual hourly electricity consumption data, the framework demonstrates significant predictive power of tweet frequency on urban electricity use. The framework also exhibits significant seasonality in X data, identifying patterns and trends that can inform time series urban building energy models (UBEM). The results offer new insights into the determinants of urban energy behavior and provide crucial perspectives for augmenting UBEMs, ensuring they are closely aligned with the complex social dynamics of contemporary urban environments. By integrating both digital and physical data, this study sheds light on urban energy behavior, supporting the formulation of effective and sustainable energy policies for urban futures.

Exploring ChatGPT as a writing assessment tool

ABSTRACT This research paper employs an exploratory framework to evaluate the potential of ChatGPT as an Automated Writing Evaluation (AWE) tool in teaching English as a Foreign Language (EFL) in Thailand. The main objective is to investigate how well ChatGPT can assess students’ writing using prompts and pre-defined rubrics compared to human raters. Moreover, the study examines its strengths and weaknesses as an assessment tool by analysing the teachers’ reflections during the assessment process. Quantitative analyses revealed significant relationships between trial accounts in comparison with the human ratings. Qualitative analysis unearths patterns in the feedback, shedding light on ChatGPT’s strengths and its limitations as an AWE tool. ChatGPT displays substantial promise as an AWE tool, offering distinct features such as human-like interface, consistency, efficiency, and scalability. Nonetheless, educators must be cognisant of its limitations. This study recognises that the strategic use of ChatGPT could enhance the evaluation process among teachers and foster the development of EFL students’ written communication skills.

Reflections on walking and the disruptive experience

Our main interest is based on understanding spatial relationships from first-person experience, from our virtual and real body. Through the act of walking, movement in real time, we become cursors that dash across the interface of reality. A continuous process that brings us closer to the production of meanings, new relationships and representations, and also a dialogue with space and time, and the network. This article seeks to present a series of disruptive experiences, documented by the authors themselves, which constitute an exploratory framework of space to discover different symbolic interrelationships, and sketch out constructions of the common space in haptic, political, social and cultural mode. It offers a repository of unexpected, intersubjective encounters, from the empirical practice of walking, which arouses new perspectives to be able to interpret circumstantial spaces, to lose oneself in ‘non-places’, or reflect as to how to approach the landscape and/or the city by opening new imaginaries that add value to the ‘glocal’ place that we traverse and/or inhabit.

Development of a Ramayana-based Counseling Module for Persons with Common Mental Health Disorders

Background: Common mental health disorders (CMDs) affect nearly 10% of the population, with the majority (80%) remaining untreated. Culturally relevant counseling approaches can be a means to reach many untreated persons. We describe the development and validation of a socioculturally relevant counseling module based on the Ramayana for persons with CMDs. Methods: The study employed an exploratory research framework to design the counseling module. The module underwent a comprehensive review of the Valmiki Ramayana to ensure its cultural relevance. Content validation was performed by mental health professionals from the field of Indian psychology. Concurrently, a panel of 15 experts from diverse backgrounds in Indian psychology validated the counseling module based on the Ramayana. Results: Based on the results of expert interviews ( n = 15), the counseling techniques, encompassing concepts, anecdotes, and narratives, were finalized. These interviews also contributed to the refinement of the counseling module. The primary objective of the content validation process was to systematically assess the appropriateness, accuracy, and practicality of the various components within the module. The content validation demonstrated that all items contained within the module exceeded the established threshold of p = .59. Conclusions: The developed counseling module based on the Ramayana is feasible to be implemented as a sociocultural intervention for persons with CMDs. By assisting persons to enhance coping abilities and improve overall well-being, this module offers a valuable resource for mental health intervention in the Indian sociocultural context. It fosters a culturally sensitive and practical approach to address the diverse mental health needs of the population.

Cultural mobilities and cultural heritage: concepts for an Asia-centric approach

This article sets out an original and exploratory framework for examining emerging concepts of cultural mobilities and heritage with a key focus on the infrastructure and spatialities of cultural mobilities in, of and through Asia—specifically China. To date, the scholarly analysis of mobilities has been dominated by the social sciences in relation to central themes of migration, national borders, crisis and transnational flows of objects and people. This paper seeks to expand the focus in mobilities discourses to humanities, not only to research how infrastructures and spatialities are shaped by culture and heritage but also to analyse cultural mobility through infrastructure and spatialities. We set out epistemic considerations for approaching cultural mobilities through an interdisciplinary lens that seeks to address heritage studies of diverse kinds, from environmental to religious to architectural. By centring Asian epistemes, the paper also challenges recognition and interpretation biases in the humanities and social sciences that continue to privilege Eurocentric hegemonies. Together, the co-authors examine: how Asian cultures operate through material and non-material infrastructures that defy singular location in geopolitical crossings and networks; how culture is mobilised in different ways through infrastructure; the entanglement of cultural heritage with political infrastructure and living practice as well as embedded values. The article discusses China’s Belt and Road Initiative (BRI), yoga as a cultural infrastructure between India and China, and the infrastructure and multiple spatialities of Chinatown as a evolving practice of cultural heritage.

A new framework for exploratory network mediator analysis in omics data.

Omics methods are widely used in basic biology and translational medicine research. More and more omics data are collected to explain the impact of certain risk factors on clinical outcomes. To explain the mechanism of the risk factors, a core question is how to find the genes/proteins/metabolites that mediate their effects on the clinical outcome. Mediation analysis is a modeling framework to study the relationship between risk factors and pathological outcomes, via mediator variables. However, high-dimensional omics data are far more challenging than traditional data: (1) From tens of thousands of genes, can we overcome the curse of dimensionality to reliably select a set of mediators? (2) How do we ensure that the selected mediators are functionally consistent? (3) Many biological mechanisms contain nonlinear effects. How do we include nonlinear effects in the high-dimensional mediation analysis? (4) How do we consider multiple risk factors at the same time? To meet these challenges, we propose a new exploratory mediation analysis framework, medNet, which focuses on finding mediators through predictive modeling. We propose new definitions for predictive exposure, predictive mediator, and predictive network mediator, using a statistical hypothesis testing framework to identify predictive exposures and mediators. Additionally, two heuristic search algorithms are proposed to identify network mediators, essentially subnetworks in the genome-scale biological network that mediate the effects of single or multiple exposures. We applied medNet on a breast cancer data set and a metabolomics data set combined with food intake questionnaire data. It identified functionally consistent network mediators for the exposures' impact on the outcome, facilitating data interpretation.

Decentralized Clinical Trials in Early Drug Development-A Framework Proposal.

The COVID-19 pandemic has led to a rethinking of clinical trial design to maintain clinical research activity, with regulatory changes allowing for the wider implementation and development of decentralized design models. Evidence of the feasibility and benefits associated with a remote design comes mainly from observational studies or phase 2 and 3 clinical trials, in which implementation is easier with a better-established safety profile. Early drug development is a slow and expensive process in which accrual and safety are key aspects of success. Applying a decentralized model to phase 1 clinical trials could improve patient accrual by removing geographic barriers, improving patient population diversity, strengthening evidence for rare tumors, and reducing patients' financial and logistical burdens. However, safety monitoring, data quality, shipment, and administration of the investigational product are challenges to its implementation. Based on published data for decentralized clinical trials, we propose an exploratory framework of solutions to enable the conceptualization of a decentralized model for phase 1 clinical trials.

Solidarity brand activism influences Black-Asian American linked fate: toward a minority-minority advertising paradigm shift

The concurrence of sociopolitical movements concerning multiple marginalized racial groups (e.g. #BlackLivesMatter and #StopAsianHate during the COVID-19 pandemic) has posed challenges in integrating activism advocacy and DEI (diversity, equity & inclusion) in marketing communication. However, the existing intergroup inclusive advertising scholarship has been predominantly guided by the hegemonic majority-minority paradigm. The present study aims to provide an exploratory research framework to shed light on the critical minority-minority relations that has been largely overlooked in advertising research. Through integrating racial triangulation, social identity, framing, and linked fate theories, this study examines how the Black-Asian solidarity activism integration messaging approach—as opposed to Black-centric and Asian-centric approaches—in brand activism advertising influences Black and Asian Americans’ perceived linked fate and group attitudes towards Black and/or Asian Americans as well as their consumer responses. Also, this study proposes a model to explore whether linked fate mediates such joint effects of activism integration approaches and audience identity. The findings demonstrated solidarity activism integration could positively impact consumers’ perceived collective linked fate with both Black and Asian Americans, while Asian-centric activism integration is more effective in increasing positive brand attitude and ad trust. Along with its theoretical, practical, and social implications, this work carves out new directions for brand activism research.