Detection and prioritization of COVID-19 infected patients from CXR images: Analysis of AI-assisted diagnosis in clinical settings

In this paper, we present the significant results from the Covid Radiographic imaging System based on AI (Co.R.S.A.) project, which took place in Italy. This project aims to develop a state-of-the-art AI-based system for diagnosing Covid-19 pneumonia from Chest X-ray (CXR) images. The contributions of this work are manifold: the release of the public CORDA dataset, a deep learning pipeline for Covid-19 detection and prioritization, the clinical validation of the developed solution by expert radiologists, and an in-depth analysis of possible biases embedded in the data and in the models, in order to build more trust in our AI-based pipeline. The proposed detection model is based on a two-step approach that provides reliable results based on objective radiological findings. Our prioritization scheme ensures the ordering of the patients so that severe cases are presented first. We showcase the impact of our pipeline on radiologists' workflow with a clinical study, allowing us to assess the real benefits in terms of accuracy and time efficiency. Project homepage: https://corsa.di.unito.it/.

The Incremental Growth of Data Infrastructure in Ecology (1980-2020)

After decades of growth, a research community's network information system and data repository were transformed to become a national data management office and a major element of data infrastructure for ecology and the environmental sciences. Developing functional data infrastructures is key to the support of ongoing Open Science and Open Data efforts. This example of data infrastructure growth contrasts with the top-down development typical of many digital initiatives. The trajectory of this network information system evolved within a collaborative, long-term ecological research community. This particular community is funded to conduct ecological research while collective data management is also carried out across its geographically dispersed study sites. From this longitudinal ethnography, we describe an Incremental Growth Model that includes a sequence of six relatively stable phases where each phase is initiated by a rapid response to a major pivotal event. Exploring these phases and the roles of data workers provides insight into major characteristics of digital growth. Further, a transformation in assumptions about data management is reported for each phase. Investigating the growth of a community information system over four decades as it becomes data infrastructure reveals details of its social, technical, and institutional dynamics. In addition to addressing how digital data infrastructure characteristics change, this study also considers when the growth of data infrastructure begins.

Effectiveness of Protection Motivation Theory on clinical factors, behavior change, and cardiovascular disease: An integrative review

Objective

To identify and synthesize the primary evidence on the effectiveness of Protection Motivation Theory on and cardiovascular disease and diseases that are risk factors for cardiovascular disease.

Method

An integrative review was conducted using the Whittemore and Knafl method (2005).

Results

Eleven articles met the inclusion and quality assessment criteria. The integration of evidence was abundant in three themes 1) Physical activity 2) Weight and Body Mass Index, and 3) Food consumption and each theme having the same six sub-themes of self-efficacy, response-efficacy, response cost, severity, vulnerability and reward. No studies have addressed all clinical factors and behavioral changes associated with cardiovascular disease.

Conclusion

Due to the limited literature on the effectiveness of Protection Motivation Theory on behavioral changes in patients with cardiovascular diseases, generalizations and practice recommendations are limited. Further research is required to evaluate the effectiveness of this theory in patient outcomes.

The impact of IT change on corporate environmental violations: Toward information asymmetry and stakeholder theory

As an essential market player, corporations play a crucial role in achieving green and sustainable economic development, while information technology is vital in influencing corporate environmental decision-making. Therefore, using a sample of A-share listed companies in China from 2009 to 2021, we developed a measurement for IT change through computer-aided text analysis to investigate the impacts of IT change on corporate environmental violations. The key findings are as follows: (1) a 1% increase in external or internal IT change can reduce corporate environmental violations by approximately 0.139 and 0.204. (2) IT change influences corporate environmental violations through monitoring and knowledge acquisition mechanisms. (3) IT change significantly affects environmental violations in firms that are large in size, lack government supervision, and adopt cost leadership strategies. (4) IT change complements green credit policies and positively affects firms in non-green credit-restricted industries. To sum up, this paper contributes to understanding the factors that influence corporate environmental decisions and offers insights into promoting the construction of IT change.

Flipping healthcare by including the patient perspective in integrated care pathway design: A scoping review

BACKGROUND

Despite the recognized benefits of integrating patient perspectives into healthcare design and clinical decision support, theoretical approaches and standardized methods are lacking. Various strategies, such as developing pathways, have evolved to address these challenges. Previous research emphasized the need for a framework for care pathways that includes theoretical principles, extensive user involvement, and data from electronic health records to bridge the gap between different fields and disciplines. Standardizing the representation of the patient perspective could facilitate its sharing across healthcare organizations and domains and its integration into journal systems, shifting the balance of power from the provider to the patient.

OBJECTIVES

This study aims to 1) Identify research approaches taken to develop patient-centred, integrated, care pathways supported by electronic health records 2) Propose a socio-technical framework for designing patient-centred care pathways across multiple healthcare levels that integrates the voice of the patient with the knowledge of the care provider and technological perspectives.

METHODS

This study conducted a scoping review following the Joanna Briggs Institute guidelines and PRISMA-ScR protocol. The databases PubMed, Scopus, Web of Science, ProQuest, IEEE, and Google Scholar were searched using a key term search strategy including variations of patient-centred, integrated care, pathway, framework and model to identify relevant studies. Eligible articles included peer-reviewed literature documenting methodologies for mapping patient-centred, integrated care pathways in healthcare service design.

RESULTS

This review summarizes the application of care pathway modelling practices across various areas of healthcare innovation. The search resulted in 410 studies, with 16 articles included after the full review and grey literature search.

CONCLUSIONS

Our research illustrated incorporating patient perspectives into modelling care pathways and healthcare service design. Regardless of the medical domain, our methodology proposes an approach for modelling patient-centred, integrated care pathways across the care continuum, including using electronic health records to support the pathways.

Expert's View on Central Components of the Actional Model of Older People's Coping with Health-Related Declines: A Pilot Study with Professional Caregivers

Abri & Boll (2022) proposed the "Actional Model of Older People´s Coping with Health-Related Declines" to explain the use of various action alternatives of older persons for dealing with diseases, functional declines, activity limitations, and participation restrictions. It draws on a broad knowledge base: an action-theoretical model of intentional self-development, models of the use of assistive technologies (ATs) and medical services, qualitative studies on reasons for using or not-using ATs, and quantitative studies on older people's health-related goals. The present study aims to gather evidence to further refine this model by additionally relying on expert knowledge from professional caregivers serving older people. Six experienced geriatric nurses working in mobile care services or residential care facilities were interviewed about key components of the above model in relation to 17 older people aged 70 to 95 with stroke, arthrosis, or mild dementia. The results revealed additional goals of reducing or preventing health-related discrepancies beyond those already included in the model (e.g., moving without pain, doing things alone, driving a car again, social return). Moreover, new motivating or demotivating goals for using certain action possibilities were found (e.g., to be at home, to be alone, to rest, to motivate other older people). Finally, some new factors were identified from the biological-functional (e.g., illness, fatigue), technological (e.g., pain inducing ATs, maladaptive devices), and social contexts (e.g., lack of staff time) that are likely to promote or hinder the use of certain action possibilities. Implications for refining the model and future research are discussed.

Establishing and evaluating trustworthy AI: overview and research challenges

Artificial intelligence (AI) technologies (re-)shape modern life, driving innovation in a wide range of sectors. However, some AI systems have yielded unexpected or undesirable outcomes or have been used in questionable manners. As a result, there has been a surge in public and academic discussions about aspects that AI systems must fulfill to be considered trustworthy. In this paper, we synthesize existing conceptualizations of trustworthy AI along six requirements: (1) human agency and oversight, (2) fairness and non-discrimination, (3) transparency and explainability, (4) robustness and accuracy, (5) privacy and security, and (6) accountability. For each one, we provide a definition, describe how it can be established and evaluated, and discuss requirement-specific research challenges. Finally, we conclude this analysis by identifying overarching research challenges across the requirements with respect to (1) interdisciplinary research, (2) conceptual clarity, (3) context-dependency, (4) dynamics in evolving systems, and (5) investigations in real-world contexts. Thus, this paper synthesizes and consolidates a wide-ranging and active discussion currently taking place in various academic sub-communities and public forums. It aims to serve as a reference for a broad audience and as a basis for future research directions.

Impact of Reverse Empowerment and Proactive Motivations on Physicians' Online Knowledge Sharing in Digital Platforms: Survey Study

BACKGROUND

Digital platforms offer a venue for patients and physicians to exchange health information and provide health care services outside traditional organizational contexts. Previous studies have seldom focused on the factors that drive the proactivity of physicians' online behavior. Additionally, there is limited research exploring the influence of patients on physicians' online behavior, particularly from the perspective of patients possessing power.

OBJECTIVE

This study aims to investigate the effect of patient-empowering behaviors on physicians' online knowledge sharing and uncover the potential mechanisms of proactivity. Based on the proactive motivational model and empowerment theory, we propose the existence of a reverse empowerment process, where empowerment flows from patients to physicians. We suggest that patient-empowering behaviors may drive physicians' online knowledge sharing as a form of proactivity. Specifically, 3 proactive motivational states-knowledge-sharing self-efficacy, sharing meaning, and positive professional affect-mediate this relationship. Additionally, platform extrinsic rewards, as a contextual factor, have a moderating effect.

METHODS

To validate our proposed research model, we conducted a survey in China using the WJX platform, targeting physicians engaged in online knowledge sharing. The measurement instrument utilizes validated items adapted from prior research, using a 5-point Likert scale. We collected 257 valid responses, ensuring that participation was both anonymous and voluntary. Data analysis was performed in 2 stages. The first stage assessed the measurement model for reliability and validity, using the Harman 1-factor test and confirmatory factor analysis. The second stage used partial least squares-structural equation modeling to examine the direct, moderation, and mediation effects among the constructs, with bootstrapping used for significance testing. This comprehensive approach ensures a robust analysis of the proposed hypotheses and contributes to the overall validity of our research model.

RESULTS

Perceived patient-empowering behaviors significantly and positively influence physicians' online knowledge sharing (β=0.27, P<.001). Knowledge-sharing self-efficacy (effect=0.06, P=.04), sharing meaning (effect=0.12, P<.001), and positive professional affect (effect=0.10, P=.003) each partially mediate the effect of patient-empowering behaviors on physicians' online knowledge sharing. The overall proactive motivational states play a complete mediation role, meaning the entire indirect effect of the model is significant (effect=0.29, P<.001), while the direct effect in the model is nonsignificant (effect=0.07, P=.26). Additionally, platform extrinsic rewards significantly and negatively moderate the effect of sharing meaning on physicians' online knowledge sharing (β=-0.13, P=.001).

CONCLUSIONS

This study is the first to recognize and examine proactivity as an alternative mediating mechanism for physicians' online knowledge sharing, highlighting the active role of patients in empowering physicians. It makes a significant contribution to the existing literature on empowerment, eHealth, and proactive behavior. Additionally, the findings offer valuable guidance for designing and managing digital platforms to ensure service sustainability.

A Taxonomy and Archetypes of AI-Based Health Care Services: Qualitative Study

BACKGROUND

To cope with the enormous burdens placed on health care systems around the world, from the strains and stresses caused by longer life expectancy to the large-scale emergency relief actions required by pandemics like COVID-19, many health care companies have been using artificial intelligence (AI) to adapt their services. Nevertheless, conceptual insights into how AI has been transforming the health care sector are still few and far between. This study aims to provide an overarching structure with which to classify the various real-world phenomena. A clear and comprehensive taxonomy will provide consensus on AI-based health care service offerings and sharpen the view of their adoption in the health care sector.

OBJECTIVE

The goal of this study is to identify the design characteristics of AI-based health care services.

METHODS

We propose a multilayered taxonomy created in accordance with an established method of taxonomy development. In doing so, we applied 268 AI-based health care services, conducted a structured literature review, and then evaluated the resulting taxonomy. Finally, we performed a cluster analysis to identify the archetypes of AI-based health care services.

RESULTS

We identified 4 critical perspectives: agents, data, AI, and health impact. Furthermore, a cluster analysis yielded 13 archetypes that demonstrate our taxonomy's applicability.

CONCLUSIONS

This contribution to conceptual knowledge of AI-based health care services enables researchers as well as practitioners to analyze such services and improve their theory-led design.

Segmentation of Low-Grade Brain Tumors Using Mutual Attention Multimodal MRI

Early detection and precise characterization of brain tumors play a crucial role in improving patient outcomes and extending survival rates. Among neuroimaging modalities, magnetic resonance imaging (MRI) is the gold standard for brain tumor diagnostics due to its ability to produce high-contrast images across a variety of sequences, each highlighting distinct tissue characteristics. This study focuses on enabling multimodal MRI sequences to advance the automatic segmentation of low-grade astrocytomas, a challenging task due to their diffuse and irregular growth patterns. A novel mutual-attention deep learning framework is proposed, which integrates complementary information from multiple MRI sequences, including T2-weighted and fluid-attenuated inversion recovery (FLAIR) sequences, to enhance the segmentation accuracy. Unlike conventional segmentation models, which treat each modality independently or simply concatenate them, our model introduces mutual attention mechanisms. This allows the network to dynamically focus on salient features across modalities by jointly learning interdependencies between imaging sequences, leading to more precise boundary delineations even in regions with subtle tumor signals. The proposed method is validated using the UCSF-PDGM dataset, which consists of 35 astrocytoma cases, presenting a realistic and clinically challenging dataset. The results demonstrate that T2w/FLAIR modalities contribute most significantly to the segmentation performance. The mutual-attention model achieves an average Dice coefficient of 0.87. This study provides an innovative pathway toward improving segmentation of low-grade tumors by enabling context-aware fusion across imaging sequences. Furthermore, the study showcases the clinical relevance of integrating AI with multimodal MRI, potentially improving non-invasive tumor characterization and guiding future research in radiological diagnostics.

Unlocking the potential of social media on food additives for effective science communication

Ultra-processed foods dominate modern diets, often containing numerous additives to enhance flavor, texture, and shelf life. Concerns about the potential health risks of daily or excessive consumption have sparked online discussions. This study analyzed perceptions of food additives on Zhihu, a popular Chinese social platform. The findings revealed no significant difference in attitudes between experts and non-experts (p > 0.05), with experts relying on statistical data and non-experts drawing from personal experiences. An independent t-test indicated no significance in comment engagement (p > 0.05) among them. However, a chi-square test showed that experts were significantly more interactive, frequently replying directly to original posts and commenters (p < 0.05). This suggests that experts actively facilitate discussions, moving away from reliance on influencers and instead prioritizing high-quality, accessible scientific communication. The findings highlight the crucial role of interactive online platforms in bridging the gap between science and the public.

A meta-analysis of antecedents and consequences of trust in the sharing economy

Purpose

Trust plays a crucial role in overcoming uncertainty and reducing risks. Uncovering the trust mechanism in the sharing economy may enable sharing platforms to design more effective marketing strategies. However, existing studies have inconsistent conclusions on the trust mechanism in the sharing economy. Therefore, this study aims to investigate the antecedents and consequences of different dimensions of trust (trust in platform and trust in peers) in the sharing economy.

Design/methodology/approach

First, we conducted a meta-analysis of 57 related articles. We tested 13 antecedents of trust in platform (e.g. economic benefits, enjoyment, and information quality) and eight antecedents of trust in peers (e.g. offline service quality and providers’ reputation), as well as their consequences. Then, we conducted subgroup analyses to test the moderating effects of economic development level (Developed vs Developing), gender (Female-dominant vs Male-dominant), platform type (Accommodation vs Transportation), role type (Obtainers vs Providers), and uncertainty avoidance (Strong vs Weak).

Findings

The results confirm that all antecedents and consequences significantly affect trust in platform or peers to varying degrees. Moreover, trust in platform greatly enhances trust in peers. Besides, the results of the moderating effect analyses demonstrate the variability of antecedents and consequences of trust under different subgroups.

Originality/value

This paper provides a clear and holistic view of the trust mechanism in the sharing economy from an object-based trust perspective. The findings may offer insights into trust-building in the sharing economy.

Manufacturer's agency channel encroachment on an online retail platform

Channel encroachment intensifies competition among channels and changes the relationships within the supply chain. This study examines the manufacturer's agency channel encroachment decision and its impact when it has already operated a platform reselling channel and a retailer channel on the platform. Equilibrium results reveal that the manufacturer's agency channel encroachment triggers a competition effect, leading to a reduction in market demand for both the platform's reselling channel and the retailer's channel, as a larger share of the market shifts toward the manufacturer's agency channel. To compensate for the losses in sales experienced by the platform and retailer, the manufacturer lowers the wholesale price. The manufacturer consistently benefits from channel encroachment and a Pareto improvement region exists, allowing all supply chain participants to improve their profits. The model is extended to consider sequential decision-making and asymmetric substitution. In comparison, under sequential decision-making, the manufacturer tends to focus more on the competitive effects of channel encroachment, leading to a reduction in channel sales. However, this approach only enhances the manufacturer's agency profit when the retailer's substitution capability is relatively strong. The manufacturer faces greater competitive pressure from the retailer under asymmetric channel substitution. Although the manufacturer increases the wholesale price and adjusts sales across channels according to the competitive situation, its profits are always lower than in the symmetric substitution case. The presence of a Pareto improvement region in the extended model confirms the robustness of our findings.

Government Digital Transformation and the utilization of Basic Public Health Services by China's migrant population

BACKGROUND

This research delves into the ramifications of Government Digital Transformation (GDT) on Basic Public Health Services (BPHS) utilization among China's migrant populace. Within the framework of escalating digital incorporation into public services, it becomes imperative to assess how GDT initiatives could ameliorate healthcare access for migrants, a group habitually confronted with systemic obstacles. Such scrutiny is crucial for the formulation of evidence-based policies that harness digital innovations to mitigate healthcare disparities.

METHODS

The analysis employs data from the 2018 China Migrants Dynamic Survey, matched with details on the National Pilot Cities for Information Benefit to the Public policy at the prefecture city level, yielding a dataset comprising 113,905 observations. To gauge BPHS utilization among migrants, two indicators were developed: the establishment of health records and registration with family doctors. The influence of GDT on BPHS access was estimated utilizing Ordinary Least Squares regression for the initial analysis, incorporating a comprehensive array of individual and household attributes, alongside province fixed effects, to ensure a thorough evaluation.

RESULTS

Baseline regression analyses revealed that GDT is significantly correlated with an enhanced probability of migrants establishing health records and registering with family doctors, with increases of 7.53% and 2.75%, respectively. Subsequent robustness checks, including sensitivity analyses with alternate dependent variables, the Propensity Score Matching method, Double/Debiased Machine Learning method, and placebo tests, further substantiated these findings. Additionally, the analysis explored mechanisms through which GDT facilitates BPHS access, identifying health information accessibility as a significant mediator in promoting healthcare engagement among migrants. Notably, both individual and regional heterogeneity were observed in GDT's impact: female migrants, those with lower educational levels, older individuals, and rural hukou holders experienced more pronounced benefits, while the effects of GDT were also stronger in cities with lower GDP and smaller populations. These findings suggest that GDT is particularly effective in bridging healthcare access gaps in economically or demographically constrained areas.

CONCLUSIONS

The outcomes underscore that GDT significantly enhances BPHS utilization among migrants, positioning digital governance as an instrumental mechanism in bridging healthcare access gaps. The observed differential impacts across various subgroups and regions emphasize the importance of customized digital health strategies that cater to the diverse needs of migrant communities. For instance, targeted interventions in lower-GDP or smaller cities could maximize the benefits of GDT in under-resourced areas. Contributing to the body of empirical evidence on digital governance and public health, this research advocates for the expanded deployment of GDT initiatives to foster more equitable health service provision for migrant demographics, promoting inclusivity within a rapidly urbanizing society.

The Pursuit of Approval: Social Media Users' Decreased Posting Latency Following Online Exclusion as a Form of Acknowledgment-Seeking Behavior

How do individuals behave after the sting of social exclusion on social media? Previous theorizing predicts that, after experiencing exclusion, individuals either engage in activities that reconnect them with others, or, they withdraw from the context. We analyzed data from Twitter (k = 47,399 posts; N = 2,000 users) and Reddit (k = 58,442 posts; N = 2,000 users), using relative (un)popularity of users' own posts (i.e., receiving fewer Likes/upvotes than usual) as an indicator of social exclusion. Both studies found no general increase or decrease in posting latency following exclusion. However, the latency of behaviors aimed at connecting with many others decreased (i.e., posting again quickly), and the latency of behaviors aimed at connecting with specific others increased (i.e., commenting or mentioning others less quickly). Our findings speak in favor of acknowledgment-seeking behavior as a reaction to social exclusion that may be specific to social media contexts.

Benefits of managerial overconfidence for corporate digital transformation: Evidence from China

This article studies how managerial overconfidence shapes a firm's digital transformation and unpacks the pathway and the boundary condition. Using a sample of Chinese listed firms between 2011 and 2022, we find that managerial overconfidence exhibits a positive relationship with corporate digital transformation and the relationship is mediated by R&D investment. In addition, we demonstrate that compared to state-owned companies, R&D investment has a more significant positive effect on digital transformation in non-state-owned companies. This study contributes novel insights about the consequences of cognitive biases of top managers and extends the studies for investigating top managers in the digital transformation age. The findings offer implications for top managers who have to fully recognize the impact of overconfidence on the development of the companies and the impact of overconfident executives on corporate digital transformation depending on R&D investment to create value. In addition, this study also reminds Chinese policymakers to improve the efficiency of converting R&D investment into digital transformation in state-owned enterprises.

Adoption of new banking models from a consumer perspective: the case of Iran

Purpose

Traditionally, the banking and financial sectors have pioneered adoption of new technologies and business models. One important digital banking model that has proven its efficacy in recent times, is Digital-Only Banking (DOB) where consumers interact with their banks through digital channels only. Having detailed knowledge of what actually happens at the consumer level during the adoption of new digital models and technologies is paramount to the success of these technological initiatives. The present study aims to investigate DOB adoption behavior and possible barriers using a quantitative approach at the consumer level. A conceptual model is developed by extending the Unified Theory of Acceptance and Use of Technology (UTAUT) model, incorporating Trust (TR), Perceived Risk (PR) constructs and cultural moderators of Individualism (IDV) and Uncertainty Avoidance (UA).

Design/methodology/approach

For this study, an online survey instrument was created and administered in Iran. The research sample was selected through the application of purposive sampling. Data from 788 respondents were analyzed. The proposed model was tested using Partial Least Square.?.s Structural Equation Modeling (PLS-SEM).

Findings

The results show that DOB adoption is positively influenced by Effort Expectancy (EE), Social Influence (SI), and Facilitating Conditions (FC), while PR negatively influences DOB adoption intention. Unexpectedly, the results indicate that TR has no significant impact on DOB usage intention. Additionally, this study demonstrates that with individuals having a low level of IDV, the relationship between PE and BI is stronger, and with individuals having a low level of UA, the impact of SI on BI is stronger. It also reveals that the impact of TR on BI is stronger in low individualistic cultures.

Practical implications

DOB providers should enhance support features of their services or provide facilities that make it simpler for users to accomplish online transactions. Here, in order to improve the UI/UX design of their apps, DOB product managers should carefully observe the technical guidelines of the operating systems of digital devices, such as the Human Interface Guidelines (HIG) for iOS and Material You for Android. Additionally, DOB providers should build partnerships with mega online retailers to provide hassle-free and easy to use payment solutions for consumers.

Originality/value

DOB, as a novel and business model, has been investigated in very few studies, especially regarding any which focus on its adoption. To fill this gap, this research investigates DOB adoption through a modified version of the UTAUT model. The findings of this study suggest that future research regarding DOB should consider sources of TR, types of non-adopters, and context. This study, as the first of its kind in DOB literature, also highlights the significant role played by cultural values in customer behavior regarding DOB adoption.

Anomaly-based threat detection in smart health using machine learning

BACKGROUND

Anomaly detection is crucial in healthcare data due to challenges associated with the integration of smart technologies and healthcare. Anomaly in electronic health record can be associated with an insider trying to access and manipulate the data. This article focuses around the anomalies under different contexts.

METHODOLOGY

This research has proposed methodology to secure Electronic Health Records (EHRs) within a complex environment. We have employed a systematic approach encompassing data preprocessing, labeling, modeling, and evaluation. Anomalies are not labelled thus a mechanism is required that predicts them with greater accuracy and less false positive results. This research utilized unsupervised machine learning algorithms that includes Isolation Forest and Local Outlier Factor clustering algorithms. By calculating anomaly scores and validating clustering through metrics like the Silhouette Score and Dunn Score, we enhanced the capacity to secure sensitive healthcare data evolving digital threats. Three variations of Isolation Forest (IForest)models (SVM, Decision Tree, and Random Forest) and three variations of Local Outlier Factor (LOF) models (SVM, Decision Tree, and Random Forest) are evaluated based on accuracy, sensitivity, specificity, and F1 Score.

RESULTS

Isolation Forest SVM achieves the highest accuracy of 99.21%, high sensitivity (99.75%) and specificity (99.32%), and a commendable F1 Score of 98.72%. The Isolation Forest Decision Tree also performs well with an accuracy of 98.92% and an F1 Score of 99.35%. However, the Isolation Forest Random Forest exhibits lower specificity (72.84%) than the other models.

CONCLUSION

The experimental results reveal that Isolation Forest SVM emerges as the top performer showcasing the effectiveness of these models in anomaly detection tasks. The proposed methodology utilizing isolation forest and SVM produced better results by detecting anomalies with less false positives in this specific EHR of a hospital in North England. Furthermore the proposal is also able to identify new contextual anomalies that were not identified in the baseline methodology.

Revisiting factors influencing consumer travel for purchasing goods and experiencing services in Shanghai, China

This paper revisits the relationships among the perceived built environment, travel attitude, perceived value, and consumer travel in the particular era of the changing retail landscape amid advances in information technology. This study thus uses consistent partial least squares structural equation modelling (PLSc-SEM) to estimate the focal parameters on the basis of data from 30 commercial centres in Shanghai, China. Additionally, this inquiry uses multigroup analysis structural equation modelling (MGA-SEM) to evaluate the disparities between two types of consumer travel: purchasing goods and experiencing services. The results show that consumers' perceived values and travel attitude significantly influence their travel and that these effects are even more apparent for experience services. In addition, the perceived built environment plays a partially positive role in consumer travel. Notably, however, accessibility cannot directly influence consumer travel and only indirectly influences travel attitude, whereas commercial attractiveness positively influences both of these factors. Accordingly, this study offers valuable insights, enabling urban planning, transportation, and commercial services to better address the evolving retail landscape and emerging consumer travel demands.

Impact of Image Content on Medical Crowdfunding Success: A Machine Learning Approach

BACKGROUND

As crowdfunding sites proliferate, visual content often serves as the initial bridge connecting a project to its potential backers, underscoring the importance of image selection in effectively engaging an audience.

OBJECTIVE

This paper aims to explore the relationship between images and crowdfunding success in cancer-related crowdfunding projects.

METHODS

We used the Alibaba Cloud platform to detect individual features in images. In addition, we used the Recognize Anything Model to label images and obtain content tags. Furthermore, the discourse atomic topic model was used to generate image topics. After obtaining the image features and image content topics, we built regression models to investigate the factors that influence the results of crowdfunding success.

RESULTS

Images with a higher proportion of young people (β=0.0753; P<.001), a larger number of people (β=0.00822; P<.001), and a larger proportion of smiling faces (β=0.0446; P<.001) had a higher success rate. Image content related to good things and patient health also contributed to crowdfunding success (β=0.082, P<.001; and β=0.036, P<.001, respectively). In addition, the interaction between image topics and image characteristics had a significant effect on the final fundraising outcome. For example, when smiling faces are considered in conjunction with the image topics, using more smiling faces in the rest and play theme increased the amount of money raised (β=0.0152; P<.001). We also examined causality through a counterfactual analysis, which confirmed the influence of the variables on crowdfunding success, consistent with the results of our regression models.

CONCLUSIONS

In the realm of web-based medical crowdfunding, the importance of uploaded images cannot be overstated. Image characteristics, including the number of people depicted and the presence of youth, significantly improve fundraising results. In addition, the thematic choice of images in cancer crowdfunding efforts has a profound impact. Images that evoke beauty and resonate with health issues are more likely to result in increased donations. However, it is critical to recognize that reinforcing character traits in images of different themes has different effects on the success of crowdfunding campaigns.

Exploring the spatiotemporal structure and driving mechanism of digital village construction in China based on social network analysis and Geodetector

Clarifying the spatiotemporal structure and driving mechanism of China's digital village construction (DVC) is imperative for ameliorating regional disparities and fostering the holistic progression of DVC in China. This study assesses the index of DVC in 30 Chinese provinces from 2011 to 2021 using the Intuitionistic Fuzzy Analytic Hierarchy Process (IFAHP) and dynamic GRA. It analyzes the spatiotemporal structure of DVC with kernel density, trend surface, and social network analysis techniques. Additionally, it employs Geodetector to elucidate the driving mechanism behind spatial differentiation in China's digital village development network. The results indicate that: (1) Although the index of DVC in China from 2011 to 2021 has shown progressive enhancement, the average DVC index for all regions throughout the years surveyed stands at 0.457, which means that the DVC in China is still at an early stage. (2) The overall network structure analysis suggests that the number of ties in China's DVC spatial correlation network grew slowly but still falls significantly short of the ideal number. Additionally, there is an increase in the network density of China's DVC over the years, providing strong evidence of spatial spillover effects within the network. (3) The block roles of the central and western regions are main inflow and bidirectional spillover while the block roles of the eastern region are agent and main outflow. (4) The main driving factors of DVC in China are investment in information infrastructure and fiscal expenditure on education. Bivariate enhancement effect and nonlinear enhancement were found to exist in all interactions of indicators. These findings offer theoretical insights and practical directives for improving DVC in China and its synergistic effects.

The application and impact of artificial intelligence technology in graphic design: A critical interpretive synthesis

In the field of graphic design, the application of Artificial Intelligence (AI) is reshaping the design process. This study employs the Critical Interpretive Synthesis (CIS) approach to explore the impacts and challenges of AI on graphic design. Through a comprehensive review of 33 papers, this research reveals four research paradigms of AI in graphic design: Artificial Intelligence Driven Design Automation and Generation (AIDAG), Artificial Intelligence Assisted Graphic Design and Image Processing (AGDIP), Artificial Intelligence in Art and Creative Design Processes (AACDP), and Artificial Intelligence Enhanced Visual Attention and Emotional Response Modeling (AVERM). These paradigms demonstrate the multidimensional role of AI in design, ranging from automation to emotional interaction. The findings suggest that AI serves a dual role as both a design tool and a medium for innovation. AI not only enhances the automation and efficiency of the design process but also fosters designers' creative thinking and understanding of users' emotional needs. This study also proposes a path for the application of the four paradigms in the graphic design process, providing effective design ideas for future design workflows.

Strategic collaboration between domestic and foreign firms on production outsourcing processes: Insights from a developing economy Firm's perspective

Worldwide fragmentation of production stages enable the developed and developing economies' firms to gain from 'foreign outsourcing collaboration (FOC)'. Literature indicates that the majority of studies on foreign outsourcing are based on the perspective of developed countries. Nevertheless, a meager amount of literature is available from the perspective of developing countries. This study bridges the research gap by employing field survey based data collected from 217 textile and apparel firms located in Faisalabad-Pakistan, for the fiscal year 2022. This study mainly examines the impact of: (i) investment in information technology (IT) on FOC in the production process; (ii) strategic integration of outsourcing policies on FOC in the production process; and (iii) strategic policy firms' adopted on FOC in the production process. SmartPLS 4 has been utilized to estimate the partial least square structural equation model. The results signify that investment and utilization of IT induce a higher level of firm performance by enhancing firms' efficiency and improving product quality. Moreover, FOC in production processes increases product quality whereas investment in IT attracts FOC in production processes. The study concludes by offering specific policy recommendations.

The Discourse Surrounding Polycystic Ovary Syndrome on TikTok: A Social Media Analysis

BACKGROUND/OBJECTIVES

Individuals are turning increasingly towards online resources, such as TikTok, to educate themselves on their medical conditions. Polycystic ovary syndrome (PCOS) is a prominent example, as women report dissatisfaction with the diagnosis process and treatment options. This study aims to provide a content analysis of the quality of PCOS health information on TikTok.

METHODS

A total of 325 TikTok videos were screened. Pertinent data, including likes, comments, shares, and views, along with video content concerning symptoms, interventions, and provider interactions were analyzed. Two reviewers independently used a modified DISCERN criteria to assess the quality of information for each video. A logistic regression was also utilized to model the probability of healthcare professionals creating educational videos.

RESULTS

A total of 238 videos met the eligibility criteria for analysis. Videos had a median of 468,400 views (Q1 = 146,400, Q3 = 1,100,000) and 18,000 likes (Q1 = 5631, Q3 = 65,100). The mean modified DISCERN scores were 3.6 ± 0.9 for physicians (n = 23), 2.0 ± 1.1 for non-physician healthcare providers (n = 52), and 1.0 ± 0.2 for non-healthcare professionals (n = 141) (p < 0.001). Healthcare professionals were 10.9 times more likely to create educational videos in comparison to non-healthcare professionals (p < 0.001).

CONCLUSIONS

TikTok videos related to PCOS attract considerable engagement but provide low-quality information. Most videos were made by non-healthcare professionals, who discussed treatment options with limited or no research. Healthcare providers should be aware of the impacts on patients' preconceived notions and help to improve patient education.

Deep Structure Usage of Electronic Patient Records: Enhancing the Influence of Nurses' Professional Commitment to Decrease Turnover Intention: Deep Structure Usage and Turnover

Background: Organizational turnover exacerbates the shortage of nurses in the global workforce. However, no study has yet explored how deep structure usage-nurses' integration of electronic patient records into nursing practice delivery-reduces their turnover intention and moderates the impact of affective, continuance, and normative professional commitment on their turnover intention. Aims: To ascertain (1) the linkage between the deep structure usage of electronic patient records and nurses' organizational turnover intention and (2) the moderating role of deep structure usage on the associations between elements of commitment (affective, continuance, and normative) and turnover intention. Methods: Using a cross-sectional survey and proportionate random sampling by ward unit, we collected data from 417 full-time nurses via a self-administered questionnaire. We performed hierarchical regression analyses to test the study hypotheses. Results: Deep structure usage was not directly related to organizational turnover intention (β = -0.07, p=0.06). However, the results suggested that deep structure usage may enhance the effect of high affective commitment on nurses' organizational turnover intention (β = -0.09, p=0.04), while potentially mitigating the effect of low continuance commitment on organizational turnover intention (β = 0.10, p=0.01). Conclusions: Deep structure usage of electronic patient records helps to ease nurses' workload and facilitates their retention, which is particularly due to their affective commitment (attachment) but not their continuance commitment (switching costs). Implications for Nursing Management: Nursing management may advise hospital management that medical records systems need to be improved and fully embedded for nursing care delivery, as a more in-depth use of these systems can help to retain nurses.

Impact of Internet Hospital Consultations on Outpatient Visits and Expenses: Quasi-Experimental Study

BACKGROUND

Internet hospital consultations are emerging in China as a new channel for patients to access health care services. Unlike third-party health care platforms such as Haodf, Teladoc Health, and MDLive, internet hospitals seamlessly integrate patients' offline medical records with online consultations, offering a cohesive online and offline health care experience. However, its impact on outpatient visits remains ambiguous. While it may encourage outpatient visits due to better continuity of care, it could also reduce face-to-face visits because of the convenience of online consultations. Given that patients in China have the autonomy to freely choose their health care providers, it is critical for hospitals to understand the effect of this telehealth technology on outpatient visits.

OBJECTIVE

This study aimed to analyze the impact of patients' adoption of internet hospital consultations on their outpatient frequency and expenses, and whether these impacts vary between urban and rural patients.

METHODS

The data used in this study were collected from a public tertiary hospital situated in a southeastern county of China, covering internet hospital consultations from January 2021 to October 2022, and offline outpatient records from January 2020 to October 2022. The dataset also includes patient demographic information. To estimate the causal effect, we used a quasi-experimental design, combining the difference-in-differences (DiD) analysis with the propensity score matching (PSM). After performing PSM, 2065 pairs of patients (4130 patients) were obtained for data analysis.

RESULTS

Our findings highlight 3 key results. First, patients' adoption of internet hospital consultations increases their frequency of outpatient visits by 2.4% per month (P<.001), and the associated expenses by 15.5% per month (P<.001). Second, such positive effects are more pronounced for patients residing in rural areas. Specifically, for every 1% increase in the distance between patients' residences and the county government (an urban center), the positive effect on monthly outpatient visits increases by 0.3% (P=.06), and the positive effect on monthly outpatient expenses increases by 2.4% (P=.03). Third, our post hoc analysis shows that rural patients living in areas with higher local health care quality experience a mitigated positive effect of internet hospital consultations, compared with those in areas with lower health care quality.

CONCLUSIONS

This study extends the research scope of telehealth technologies by investigating internet hospitals, which are characterized by the integration of online and offline services. Our findings suggest that patients' adoption of internet hospital consultations is associated with an increase in both the frequency and expenses of outpatient visits. In addition, these effects vary based on patients' urban-rural status and local health care quality. These insights offer valuable guidance for policy makers and health care providers in promoting and optimizing the development and operation of internet hospitals.

From infodemic to resilience: Exploring COVID-19 protective measures in armed-conflict zone

The proliferation of inaccurate and misleading information about COVID-19 on social media poses a significant public health concern. This study examines the impact of the infodemic and beneficial information on COVID-19 protective behaviors in an armed-conflict country. Using the protective action decision model (PADM), data were collected from 1439 participants through a questionnaire in Yemen between August 2020 and April 2021. Structural equation modeling tested hypotheses generated by the PADM. The findings indicate that the infodemic reduces the likelihood of individuals adopting protective measures against COVID-19. Surprisingly, official announcements by accountable authorities do not moderate the relationship between the infodemic and protective responses. These results highlight the need for further research on resilience in armed-conflict countries. This study contributes to understanding armed-conflict countries' unique challenges in combating health crises. Addressing the infodemic and promoting accurate information is crucial in enhancing protective behaviors and mitigating the negative impact of misinformation. Policymakers and public health authorities can utilize these insights to develop targeted interventions and communication strategies that ensure accurate information dissemination and encourage the adoption of adequate protective measures.

How stress influences short video addiction in China: an extended compensatory internet use model

Introduction

The rise of short video applications has become a defining feature of modern digital media consumption, drawing increasing attention from researchers due to issues related to short video addiction. While earlier studies have examined the perceived stress as a cause of short video addiction, there is limited understanding of the potential mechanisms underlying the relationship between these two variables. Building on compensatory Internet use (CIU) theory, this study introduces an extended model (E-CIU) to explore how stress, compensatory motivations (i.e., social interaction and relaxing entertainment), and affective responses (i.e., immersion and attitude) relate to short video addiction. This study also examines differences between the age groups.

Methods

Data from 319 Chinese short video users were tested applying partial least squares structural equation modeling (PLS-SEM) and PLS-SEM multigroup analysis.

Results

Findings indicate that stress, immersion, and attitude each contribute positively to short video addiction. Stress is linked to both social interaction and relaxing entertainment. While both factors positively affect attitude toward short videos, only relaxing entertainment enhances immersion. Results confirmed the perceived stress indirectly influences short video addiction through a serial mediating pathway comprising motivations and affective responses. Moreover, the study shows that perceived stress influences social interaction, relaxing entertainment influences attitude and immersion, and social interaction influences immersion across all age groups. The study further identified variations in how different groups experience the relationship between stress and addiction, stress and relaxation, attitude and addiction, and immersion and addiction.

Discussion

Consequently, this study enriches the understanding of the E-CIU as a new theoretical model of short video addiction. These insights offer practical recommendations for short video applications to address user engagement and addiction more effectively.

Development and validation of a novel AI framework using NLP with LLM integration for relevant clinical data extraction through automated chart review

The accurate extraction of surgical data from electronic health records (EHRs), particularly operative notes through manual chart review (MCR), is complex, crucial, and time-intensive, limited by human error due to fatigue and the level of training. This study aimed to develop and validate a novel Natural Language Processing (NLP) algorithm integrated with a Large Language Model (LLM; GPT4-Turbo) to automate the extraction of spinal surgery data from EHRs. The algorithm employed a two-stage approach. Initially, a rule-based NLP framework reviewed and classified candidate segments from the text, preserving their reference segments. These segments were then verified in the second stage through the LLM. The primary outcomes of this study were the accurate extraction of surgical data, including the type of surgery, levels operated, number of disks removed, and presence of intraoperative incidental durotomies. Secondary objectives explored time efficiency, tokenization lengths, and costs. The performance of the algorithm was assessed across two validation databases, analyzing metrics such as accuracy, sensitivity, discrimination, F1-score, and precision, with 95% confidence intervals calculated using percentile-based bootstrapping. The NLP + LLM algorithm markedly outperformed all performance metrics, demonstrating significant improvements in time and cost efficiency. These results suggest the potential for widespread adoption of this technology.

The limits of learning engagement and academic leadership within the higher education digitalization process - analysis by using PLS SEM

There is convincing evidence that the learning environments digitalization of tools and equipment ultimately results in the speed and depth learning involvement of academia members, by raising attainment of each of the digital learning experiences. The majority of the research that was conducted on the topic of enhancing the digital skills of learners, which would ultimately lead to an increase in their active engagement, was conducted on students in primary and secondary education, leaving members of higher education outside of the scope of the study. Given the uninterrupted search for academic performance and innovation, the current research considers the technological changes that lead to the transformation of the traditional academic learning environments as previously known. The current paper considers the changes in the learners' engagement in the context of the dually digital transformation of the higher academic multi-institutional digitally-learning enhancements. An important factor to be considered regards the leadership evolution (in terms of teaching) that over time, led to a different speed contextual shift, according to its effectiveness, leading to higher or lower students learning (dis)engagement. The current manuscript aims to examine how the higher education digitalization levels could affect the student's learning engagement, under the close monitoring of the academia leadership styles practice. Data collection and analysis implied at first a qualitative approach by issuing an online-distributed survey that resulted in a number of 2272 valid responses. After performing structural equation modelling and proving a valid assessment tool, the analysis resulted into statistically proving the validity of two main hypotheses according to which students learning engagement has a positive effect on the practice of academic leadership. Additionally, results emphasized the fact that higher education digitalization has altogether a negative effect of students learning engagement. Consequently, the current study stresses on the importance of different peers' categories in the context of higher education institutions performance, with an emphasis on the different levels of students' engagement and the leadership styles evolution and practice, aspects uniformly developing within a continuously digitally transformation of the higher education environment.

The Effect of Characteristics of Patient Communication on Physician Feedback in Online Health Communities: An Observational Cross-Sectional Study

With the rapid development of e-health and telemedicine, previous studies have explored the relationship between physician-patient communication and patient satisfaction; however, there is a paucity of research on the influence of the characteristics of patient communication on the characteristics of physician feedback. Based on the communication accommodation theory, as well as the computer-mediated communication theory and media richness theory, this study aimed to explore how characteristics of patient communication influence characteristics of physician feedback in online health communities. We employed a crawler software to download the communication data between 1652 physicians and 105,325 patients from the Good Doctor platform, the biggest online health community in China. We built an empirical model using this data and employed a multilevel model to test our hypotheses using Stata and Python software. The results indicate that the amount of patients' rendered information positively influences the physicians' text (α = 0.123, t = 33.147, P < .001) and voice feedback (β = 0.201, t = 40.011, P < .001). Patients' hope for help signals and the provision of their electronic health records weaken the effect of the amount of patients' rendered information on physicians' text feedback (α = -0.040, t = -24.857, P < .001; α = -0.048, t = -15.784, P < .001), whereas, it strengthened the effect of the amount of patients' rendered information on physicians' voice feedback (β = 0.033, t = 14.789, P < .001; β = 0.017, t = 4.208, P < .001). Moreover, the occurrence of high-privacy diseases strengthened the effect of the amount of patients' presented information on physicians' text and voice feedback (α = 0.023, t = 4.870, P < .001; β = 0.028, t = 4.282, P < .001). This research contributes to the development of computer-mediated communication theories and sheds light on service delivery in the online health community.

The impact of digital technology use on nurses' professional identity and relations of power: a literature review

AIM

This study seeks to review how the use of digital technologies in clinical nursing affects nurses' professional identity and the relations of power within clinical environments.

DESIGN

Literature review.

DATA SOURCES

PubMed and CINAHL databases were searched in April 2023.

METHODS

We screened 874 studies in English and German, of which 15 were included in our final synthesis reflecting the scientific discourse from 1992 until 2023.

RESULTS

Our review revealed relevant effects of digital technologies on nurses' professional identity and power relations. Few studies cover outcomes relating to identity, such as moral agency or nurses' autonomy. Most studies describe negative impacts of technology on professional identity, for example, creating a barrier between nurses and patients leading to decreased empathetic interaction. Regarding power relations, technologically skilled nurses can yield power over colleagues and patients, while depending on technology. The investigation of these effects is underrepresented.

CONCLUSION

Our review presents insights into the relation between technology and nurses' professional identity and prevalent power relations. For future studies, dedicated and critical investigations of digital technologies' impact on the formation of professional identity in nursing are required.

IMPLICATIONS FOR THE PROFESSION

Nurses' professional identity may be altered by digital technologies used in clinical care. Nurses, who are aware of the potential effects of digitized work environments, can reflect on the relationship of technology and the nursing profession.

IMPACT

The use of digital technology might lead to a decrease in nurses' moral agency and competence to shape patient-centred care. Digital technologies seem to become an essential measure for nurses to wield power over patients and colleagues, whilst being a control mechanism. Our work encourages nurses to actively shape digital care.

REPORTING METHOD

We adhere to the JBI Manual for Evidence Synthesis where applicable. EQUATOR reporting guidelines were not applicable for this type of review.

PATIENT OR PUBLIC CONTRIBUTION

No patient or public contribution.

Social media and caregivers' well-being: A scoping review and future research directions

Caregivers' well-being has become a major social concern, given the growing demand for long-term care support to meet the challenges of an aging society. As social media becomes substantially pervasive in daily life, the impacts of social media on caregivers' well-being are increasingly gaining public attention. Although research activities are active, studies on social media and caregivers' well-being lack consolidation and receive limited attention from researchers. To bridge the gaps, this study aims to consolidate the existing knowledge through a scoping review (with four databases: PubMed, Web of Science, Scopus, and CINAHL) and call for research on caregivers from the perspective of social media and well-being. Review results from forty eligible papers indicate that impacts of social media use on caregivers' well-being are not always positive, which encourages future research to provide more understanding of how to achieve positive effects and avoid adverse effects simultaneously. We also further discuss the findings and suggest future investigation opportunities.

What Drives Health Information Exchange on Social Media? Social Media Affordances and Social Support Perspectives

Despite the prevalence of health communication on social media, the mechanisms by which social media affect health information exchange have received less than adequate investigation. By integrating the lenses of affordance and social support, this study examines the effect of social media affordances on health information exchange, with a particular focus on the mediation effect of social support and the boundary condition of health consciousness. Results of an online survey indicated that the affordances of information accessibility and metavoicing are positively related to both general and experiential informational support. Moreover, the affordance of association is positively related to experiential informational support and emotional support. Additionally, three types of social support have positive effects on health information exchange, and health consciousness positively moderates the effect of experiential informational support on health information exchange. This study enriches existing literature by revealing the underlying mechanisms and boundary conditions of the relationship between social media affordances and health information exchange. Furthermore, it provides new insights for practitioners to promote health information exchange on social media.

Bias in medical AI: Implications for clinical decision-making

Biases in medical artificial intelligence (AI) arise and compound throughout the AI lifecycle. These biases can have significant clinical consequences, especially in applications that involve clinical decision-making. Left unaddressed, biased medical AI can lead to substandard clinical decisions and the perpetuation and exacerbation of longstanding healthcare disparities. We discuss potential biases that can arise at different stages in the AI development pipeline and how they can affect AI algorithms and clinical decision-making. Bias can occur in data features and labels, model development and evaluation, deployment, and publication. Insufficient sample sizes for certain patient groups can result in suboptimal performance, algorithm underestimation, and clinically unmeaningful predictions. Missing patient findings can also produce biased model behavior, including capturable but nonrandomly missing data, such as diagnosis codes, and data that is not usually or not easily captured, such as social determinants of health. Expertly annotated labels used to train supervised learning models may reflect implicit cognitive biases or substandard care practices. Overreliance on performance metrics during model development may obscure bias and diminish a model's clinical utility. When applied to data outside the training cohort, model performance can deteriorate from previous validation and can do so differentially across subgroups. How end users interact with deployed solutions can introduce bias. Finally, where models are developed and published, and by whom, impacts the trajectories and priorities of future medical AI development. Solutions to mitigate bias must be implemented with care, which include the collection of large and diverse data sets, statistical debiasing methods, thorough model evaluation, emphasis on model interpretability, and standardized bias reporting and transparency requirements. Prior to real-world implementation in clinical settings, rigorous validation through clinical trials is critical to demonstrate unbiased application. Addressing biases across model development stages is crucial for ensuring all patients benefit equitably from the future of medical AI.

Identifying Flowing Change and Learning Towards Healthy Ageing—An Action Research Study

This article identifies change and learning within a complex ecosystem aiming for healthy ageing through action research. Learning, innovation and progress are all referred to as change. Reflecting on a surprisingly good outcome within a collaborative project between dental care and municipal healthcare, fearing that change and learning from a predetermined outcome could overshadow elements that enabled change, a theoretical flow approach was used as an entrance to perspectives of change and learning. Flow approach made it possible to sense movement, focusing on timing, attentionality and undergoing. A research question was formulated: What change and learning outcomes can be identified through the lens of a flow approach? By looking beyond the project, focusing on what enabled change, actions could be traced that might have been overlooked in the absence of a flow approach. Although oral health is important for health, interprofessional collaborative projects have, to our knowledge, never been explored using a flow approach. This article contributes to theory by broadening perspectives on change and learning. Long-term interprofessional collaboration became evident as an enabling factor when mapping the emergence of change through a flow approach aiming for healthy ageing.

Too Anecdotal to Be True? Mechanical Turk Is Not All Bots and Bad Data: Response to Webb and Tangney (2022)

In response to Webb and Tangney (2022) we call into question the conclusion that data collected on Amazon's Mechanical Turk (MTurk) was "at best-only 2.6% valid" (p. 1). We suggest that Webb and Tangney made certain choices during the study-design and data-collection process that adversely affected the quality of the data collected. As a result, the anecdotal experience of these authors provides weak evidence that MTurk provides low-quality data as implied. In our commentary we highlight best practice recommendations and make suggestions for more effectively collecting and screening online panel data.

Quantifying the heterogeneity impact of risk factors on regional bicycle crash frequency: A hybrid approach of clustering and random parameter model

The existence of internal and external heterogeneity has been established by numerous studies across various fields, including transportation and safety analysis. The findings from these studies underscore the complexity of crash data and the multifaceted nature of risk factors involved in accidents. However, most studies consider the effects of unobserved heterogeneity from one perspective -- either within clusters (internal) or between clusters (external) -- and do not investigate the biases from both simultaneously on crash frequency analysis. To fill this gap, this study proposes a hybrid approach combining latent class cluster analysis with the random parameter negative binomial regression model (LCA-RPNB) to explore the association between risk factors and bicycle crash frequency. First, the bicycle crash data is categorized into three clusters using LCA based on crash features such as gender, trip purposes, weather, and light conditions. Then, the separated crash frequency models for different clusters and the overall model are developed based on RPNB using regional factors of crash locations as independent variables and the crash frequency of different clusters respectively as dependent variables. The hybrid approach enables a comprehensive examination of internal and external heterogeneities among bicycle crash frequency factors simultaneously. Results suggest that the proposed hybrid approach exhibits superior fitting and predictive performance compared to the model only considers the effects of unobserved heterogeneity from one perspective with the lower values of Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC), Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE). This approach can help policymakers and urban planners to design more effective safety interventions by understanding the distinct needs of different bicyclist clusters and the specific factors that contribute to crash risk in each group.

Exploring the Effects of Online Physician Voice Pitch Range and Filled Pauses on Patient Satisfaction in Mobile Health Communication

The convenience of mobile devices has driven the widespread use of voice technology in mobile health communication, significantly improving the timeliness of online service. However, the issue of listening to therapeutic content, which requires great cognitive effort and may exceed the patient's information processing capacity (i.e., information overload), is of concern. Based on information processing theory, this study reports how online physicians' voice characteristics (pitch range and filled pauses) affect patient satisfaction. We obtained 10,585 mobile voice consultation records of 1,416 doctors from China's largest mHealth platform and analyzed them using audio mining and empirical methods. Results showed that pitch range (β = 0.0539, p < .01) and filled pauses (β = 0.0365, p < .01) in doctors' voice positively influenced online patient satisfaction. However, the effect of filled pauses becomes weaker for patients with higher health literacy and higher disease risk. This suggests that there is heterogeneity in the way different patients process audio information. This study provides important insights for guiding online physician behaviors, enhancing patient satisfaction, and improving mobile health platform management.

Environmental regulations and firms' trade-offs between innovations: Empirical evidence from the quasi-experiment in China

This paper uses firm-level panel data to study the quasi-experiment of China's low carbon city (LCC) pilots and to estimate the dynamic impact of environmental regulations on firms' trade-offs between green innovation and digital innovation. The results indicate that environmental regulations lead to an increase in firms' green innovation in the short run and promote firms' digital transformation in the long run. Moreover, the key factors influencing firms' trade-offs are their size and the pollution levels of their primary businesses, highlighting the importance of both capability and immediacy for firms when making decisions. Furthermore, the event study shows that environmental regulations initially impact firms' profits in heavily polluting industries in the short run. However, environmental regulations can gradually enhance those profits in the long run by reducing marginal costs and upgrading industrial structures in treated regions. This paper provides insights for a deeper understanding of firms' heterogeneous choices and dynamic effects of innovation behaviors.

Institutional logics and organizational green transformation: Evidence from the agricultural industry in emerging economies

While the agricultural green transformation has garnered significant attention on the political stage as a crucial strategy for climate change mitigation and sustainable development, there remains a notable gap in our understanding of the prevailing logics that shape organizational decisions on this change in emerging economies. Drawing on the institutional logics perspective, this study explores the impact of state logic (manifested through green credit policy) and market logic (manifested through analyst attention) on agriculture-related firms' (ARFs') green transformation, as well as the moderating effects of organizational transparency and environmental, social, and governance (ESG) level. Using longitudinal data on 201 publicly listed ARFs in China from 2008 to 2020, we find that both state and market logics exert unique effects on facilitating the green transformation of ARFs. More notably, a complementarity between the two logics in offering value for ARFs' green transformation is revealed. This complementary effect becomes more pronounced for organizations with a higher level of transparency or a superior ESG profile. Our study contributes to the neo-institutional perspective of organizational green transformation, a better understanding of conflicting institutional logics in emerging economies, and research on agricultural sustainability.

Exploring the Relationship between Sleep Pattern and Quality of Life in the Middle-aged Population Based on Fuzzy-set Qualitative Comparative Analysis

This study aims to investigate the effects of 7 factors (subjective sleep quality, sleep latency, sleep duration, habitual sleep efficiency, sleep disturbances, use of sleeping medications, and daytime dysfunction), as measured by the Pittsburgh Sleep Quality Index, on the quality of life (QOL) in middle-aged individuals. A total of 194 middle-aged individuals aged 45 to 59 years participated in this study. The Chinese version of the medical outcomes study 36-item short-form health survey and the Pittsburgh Sleep Quality Index were used to assess the QOL and sleep quality, respectively. The relationship between sleep patterns and QOL was analyzed using fuzzy-set qualitative comparative analysis. Improvement in sleep disturbances and reduction in the use of sleeping medications are necessary conditions for enhancing QOL. Two configurations of sleep patterns positively influence QOL: the "sufficiency type" with high level of sleep duration, non-sleep disturbances, and non-daytime dysfunction as core conditions; and the "efficiency type" with high habitual sleep efficiency and non-daytime dysfunction as core conditions. Three configurations of sleep patterns negatively influence QOL: the "low-quality type" with non-high-level subjective sleep quality, sleep duration, and habitual sleep efficiency as core conditions; the "barrier type" with high level of sleep disturbances, non-high-level sleep latency, and non-high-level subjective sleep quality/sleep duration as core conditions; and the "drug-based type" with high level of use of sleeping medications and subjective sleep quality as core conditions. Using the fuzzy-set qualitative comparative analysis method, this study initially reveals that the influence of sleep patterns on QOL in middle-aged individuals is a holistic effect combining multiple elements. This deepens and enriches the understanding of the relationship between sleep patterns and QOL and expands new perspectives for more in-depth research on how to improve the QOL of middle-aged individuals through sleep interventions.

Using the Person-Based Approach to Co-Create and Optimize an App-Based Intervention to Support Better Sleep for Adolescents in the United Kingdom: Mixed Methods Study

BACKGROUND

Poor sleep is a common problem in adolescents aged 14 to 18 years. Difficulties with sleep have been found to have a bidirectional link to mental health problems.

OBJECTIVE

This new research sought to involve young people in the co-creation of a new app, particularly those from underserved communities. The Sleep Solved app uses science-based advice to improve sleep-related behaviors and well-being. The app was developed using the person-based approach, underpinned by the social cognitive theory and the social-ecological model of sleep health.

METHODS

Young people (aged 14-18 y) were recruited from across the United Kingdom to contribute to patient and public involvement (PPI) activities. In partnership with our peer researcher (MHJ), we used a multitude of methods to engage with PPI contributors, including web-based workshops, surveys, think-aloud interviews, focus groups, and app beta testing.

RESULTS

A total of 85 young people provided PPI feedback: 54 (64%) young women, 27 (32%) young men, 2 (2%) genderfluid people, 1 (1%) nonbinary person, and 1 (1%) who reported "prefer not to say." Their levels of deprivation ranged from among the 40% most deprived to the 20% least deprived areas. Most had self-identified sleep problems, ranging from 2 to 3 times per week to >4 times per week. Attitudes toward the app were positive, with praise for its usability and use of science-based yet accessible information. Think-aloud interviews and a focus group identified a range of elements that may influence the use of the app, including the need to pay attention to language choices and readability. User experiences in the form of narrated audio clips were used to normalize sleep problems and provide examples of how the app had helped these users.

CONCLUSIONS

Young people were interested in using an app to better support their sleep and mental health. The app was co-created with strong links to theory- and evidence-based sleep hygiene behaviors. Future work to establish the effectiveness of the intervention, perhaps in a randomized controlled trial, would provide support for potential UK-wide rollout.

Enterprise digitization and marine economic performance: An empirical study of listed enterprises in China's maritime economy

The scale and connectivity of marine resources make them more complex than land resource management. Although digitization has been recognized as an organizational change process that can effectively improve resource efficiency and enhance network resilience, however, gaps remain in establishing the theoretical links between digitization and marine economic performance. Based on a panel fixed-effects model, this study evaluates the interrelationships and potential mechanisms of different firms with data from annual reports of listed firms in the marine economy in the eastern coastal region of China. The results indicate that there is a 'U-shaped' relationship between digitalization and enterprise efficiency in the maritime sector, and significant heterogeneity exists in the characteristics of these enterprises. Notably, firms' technological innovation capability can modulate the 'U-shaped' relationship through the interaction of economies of scale and economies of scope. This paper highlights how digitization mitigates the fragmentation and sectionalization of marine information and addresses the digital overload and productivity paradox that firms may face in the early stages of digitization. The study suggests that institutional diversity shapes resilience. Governments need to promote top-down regulation and industry collaboration, while marine enterprises need to coevolve collaboratively with them through bottom-up internal communication and external interaction to enhance the value chain of marine enterprises.

Decoding the Influence of eHealth on Autonomy, Competence, and Relatedness in Older Adults: Qualitative Analysis of Self-Determination Through the Motivational Technology Model

BACKGROUND

Older adults adopt and use eHealth systems to build autonomy, competence, and relatedness and engage in healthy behaviors. The motivational technology model posits that technology features, such as those on websites, smart displays, and mobile phones, must allow for navigability, interactivity, and customizability, which spur feelings of self-determination and intrinsic motivation. We studied ElderTree, an online system for older adults that provides on-demand videos of healthy living content, self-monitoring, and weekly researcher-hosted video meetings.

OBJECTIVE

We aimed to understand the theoretical crossover between the motivational technology model and self-determination theory using features of ElderTree to understand the usability of the technology and how it may support older adults' autonomy, competence, and relatedness.

METHODS

Drawing participants from a randomized controlled trial of a mobile health app for older adults with multiple chronic conditions, we conducted qualitative interviews with 22 older adults about their use of the app; the interviews were coded using qualitative thematic analysis.

RESULTS

Older adults did find that features within ElderTree such as content available on demand, good navigation, and weekly researcher-led video calls supported feelings of autonomy, competence, and relatedness, respectively. Individual differences such as a background using computers also influenced participants' experiences with the smart displays.

CONCLUSIONS

Participants confirmed the features that increased internal motivation, such as interactivity correlating with feelings of relatedness, but they also found other ways to support autonomous health behavior change beyond narrow views of navigability, interactivity, and customization.

Forecasting tourism demand with helpful online reviews

Purpose

Online reviews serve as valuable sources of information, reflecting tourists’ attentions, preferences and sentiments. However, although the existing research has demonstrated that incorporating online review data can enhance the performance of tourism demand forecasting models, the reliability of online review data and consumers’ decision-making process have not been given adequate attention. To address the aforementioned problem, the purpose of this study is to forecast tourism demand using online review data derived from the analysis of review helpfulness.

Design/methodology/approach

The authors propose a novel “identification-first, forecasting-second” framework. This framework prioritizes the identification of helpful reviews through a comprehensive analysis of review helpfulness, followed by the integration of helpful online review data into the forecasting system. Using the SARIMAX model with helpful online review data sourced from TripAdvisor, this study forecasts tourist arrivals in Hong Kong during the period from August 2012 to June 2019. The SNAÏVE/SARIMA model was used as the benchmark model. Additionally, artificial intelligence models including long short-term memory, back propagation neural network, extreme learning machine and random forest models were used to assess the robustness of the results.

Findings

The results demonstrate that online review data are subject to noise and bias, which can adversely affect the accuracy of predictions when used directly. However, by identifying helpful online reviews beforehand and incorporating them into the forecasting process, a notable enhancement in predictive performance can be realized.

Originality/value

First, to the best of the authors’ knowledge, this study is one of the first to focus on the data issue of online reviews on tourism arrivals forecasting. Second, this study pioneers the integration of the consumer decision-making process into the domain of tourism demand forecasting, marking one of the earliest endeavors in this area. Third, this study makes a novel attempt to identify helpful online reviews based on reviews helpfulness analysis.

Assistance systems for patient positioning in radiotherapy practice

Effective radiotherapy for cancer treatment requires precise and reproducible positioning of patients at linear accelerators. Assistance systems in digitally networked radiotherapy can help involved specialists perform these tasks more efficiently and accurately. This paper analyses patient positioning systems and develops new knowledge by applying the Design Science Research methodology. A systematic literature review ensures the rigour of the research. Furthermore, this article presents the results of an online survey on assistance systems for patient positioning, the derived design requirements and an artefact in the form of a conceptual model of a patient positioning system. Both the systematic literature review and the online survey serve as empirical evidence for the conceptual model. This paper thereby contributes to broadening the academic knowledge on patient positioning in radiotherapy and provides guidance to system designers.

Redefining organizational digitality: a relational-ontological approach inspired by new materialism

Organizations, as central actors in societal structure, undergo significant transformations due to the impact of digitalization, often resulting in disruptive changes. Consequently, organizations increasingly view digitalization as an ongoing process of negotiation, which has led to the emergence of new operational modes and organizational norms. In this context, the interaction between organizations and digital technologies is characterized by recursive dynamics, which blur conventional boundaries. This presents a challenge in defining the distinct domains of the digital and the organizational within the framework of recursivity. This article draws upon new materialism and agential realism to propose an ontological-relational approach to understanding organizational digitality. This approach suggests a reconceptualization of organizational digitality as a mechanism that generates relational entities, thereby reshaping their inherent meanings. By transcending traditional boundaries between organizations and digital, this perspective provides a nuanced understanding of digital phenomena within organizational contexts.

Use of Artificial Intelligence tools in supporting decision-making in hospital management

BACKGROUND

The use of Artificial Intelligence (AI) tools in hospital management holds potential for enhancing decision-making processes. This study investigates the current state of decision-making in hospital management, explores the potential benefits of AI integration, and examines hospital managers' perceptions of AI as a decision-support tool.

METHODS

A descriptive and exploratory study was conducted using a qualitative approach. Data were collected through semi-structured interviews with 15 hospital managers from various departments and institutions. The interviews were transcribed, anonymized, and analyzed using thematic coding to identify key themes and patterns in the responses.

RESULTS

Hospital managers highlighted the current inefficiencies in decision-making processes, often characterized by poor communication, isolated decision-making, and limited data access. The use of traditional tools like spreadsheet applications and business intelligence systems remains prevalent, but there is a clear need for more advanced, integrated solutions. Managers expressed both optimism and skepticism about AI, acknowledging its potential to improve efficiency and decision-making while raising concerns about data privacy, ethical issues, and the loss of human empathy. The study identified key challenges, including the variability in technical skills, data fragmentation, and resistance to change. Managers emphasized the importance of robust data infrastructure and adequate training to ensure successful AI integration.

CONCLUSIONS

The study reveals a complex landscape where the potential benefits of AI in hospital management are balanced with significant challenges and concerns. Effective integration of AI requires addressing technical, ethical, and cultural issues, with a focus on maintaining human elements in decision-making. AI is seen as a powerful tool to support, not replace, human judgment in hospital management, promising improvements in efficiency, data accessibility, and analytical capacity. Preparing healthcare institutions with the necessary infrastructure and providing specialized training for managers are crucial for maximizing the benefits of AI while mitigating associated risks.

Navigating workplace conflicts and fostering innovative behaviors: the role of job commitment and socio-instrumental ESM utilization

This study explores the dynamics of intersection between workplace conflicts, job commitment, and socio-instrumental enterprise social media (ESM) utilization for improving innovation. This study proposes job commitment as a pivotal mediator that influences employees' attitudinal responses to the array of conflicts encountered in the workplace. Additionally, it introduce socio-instrumental ESM use as a potent moderator, influencing employees' appraisals of workplace conflicts. To illuminate the intricate tapestry of these interactions, we present a moderated mediation model that delineates the complex nomological network governing the interplay among workplace conflicts, job commitment, and the cultivation of innovative behaviors. Based on empirical data encompassing 447 employees, our findings illuminate the distinctive moderating roles played by these socio-instrumental ESM usage. These patterns differentially moderate the connections between workplace conflicts and job commitment, as well as the indirect connections between workplace conflicts and the stimulation of employee innovation through job commitment. This research significantly advances our theoretical understanding of the multifaceted interplay among workplace conflicts, job commitment, socio-instrumental ESM utilization, and employee innovation. Moreover, it offers implications that can faciliate organizational strategies aimed at fostering innovation. By recognizing the pivotal roles of job commitment and socio-instrumental ESM, organizations can strategically harness these factors to cultivate a culture of innovation, positioning themselves for success in an increasingly competitive and dynamic business environment.