Systematic design of health monitoring systems centered on older adults and ADLs

BACKGROUND

Older adults face unique health challenges as they age, including physical and mental health issues and mood disorders. Negative emotions and social isolation significantly impact mental and physical health. To support older adults and address these challenges, healthcare professionals can use Information and Communication Technologies (ICTs) such as health monitoring systems with multiple sensors. These systems include digital biomarkers and data analytics that can streamline the diagnosis process and help older adults to maintain their independence and quality of life.

METHOD

A design research methodology is followed to define a conceptual model as the main artifact and basis for the systematic design of successful systems centered on older adults monitoring within the health domain.

RESULTS

The results include a conceptual model focused on older adults' Activities of Daily Living (ADLs) and Health Status, considering various health dimensions, including social, emotional, physical, and cognitive dimensions. We also provide a detailed instantiation of the model in real use cases to validate the usefulness and feasibility of the proposal. In particular, the model has been used to develop two health systems intended to measure the degree of the elders' frailty and dependence with biomarkers and machine learning.

CONCLUSIONS

The defined conceptual model can be the basis to develop health monitoring systems with multiple sensors and intelligence based on data analytics. This model offers a holistic approach to caring for and supporting older adults as they age, considering ADLs and various health dimensions. We have performed an experimental and qualitative validation of the proposal in the field of study. The conceptual model has been instantiated in two specific case uses, showing the provided abstraction level and the feasibility of the proposal to build reusable, extensible and adaptable health systems. The proposal can evolve by exploiting other scenarios and contexts.

Exploring the role of professional identity in the implementation of clinical decision support systems-a narrative review

BACKGROUND

Clinical decision support systems (CDSSs) have the potential to improve quality of care, patient safety, and efficiency because of their ability to perform medical tasks in a more data-driven, evidence-based, and semi-autonomous way. However, CDSSs may also affect the professional identity of health professionals. Some professionals might experience these systems as a threat to their professional identity, as CDSSs could partially substitute clinical competencies, autonomy, or control over the care process. Other professionals may experience an empowerment of the role in the medical system. The purpose of this study is to uncover the role of professional identity in CDSS implementation and to identify core human, technological, and organizational factors that may determine the effect of CDSSs on professional identity.

METHODS

We conducted a systematic literature review and included peer-reviewed empirical studies from two electronic databases (PubMed, Web of Science) that reported on key factors to CDSS implementation and were published between 2010 and 2023. Our explorative, inductive thematic analysis assessed the antecedents of professional identity-related mechanisms from the perspective of different health care professionals (i.e., physicians, residents, nurse practitioners, pharmacists).

RESULTS

One hundred thirty-one qualitative, quantitative, or mixed-method studies from over 60 journals were included in this review. The thematic analysis found three dimensions of professional identity-related mechanisms that influence CDSS implementation success: perceived threat or enhancement of professional control and autonomy, perceived threat or enhancement of professional skills and expertise, and perceived loss or gain of control over patient relationships. At the technological level, the most common issues were the system's ability to fit into existing clinical workflows and organizational structures, and its ability to meet user needs. At the organizational level, time pressure and tension, as well as internal communication and involvement of end users were most frequently reported. At the human level, individual attitudes and emotional responses, as well as familiarity with the system, most often influenced the CDSS implementation. Our results show that professional identity-related mechanisms are driven by these factors and influence CDSS implementation success. The perception of the change of professional identity is influenced by the user's professional status and expertise and is improved over the course of implementation.

CONCLUSION

This review highlights the need for health care managers to evaluate perceived professional identity threats to health care professionals across all implementation phases when introducing a CDSS and to consider their varying manifestations among different health care professionals. Moreover, it highlights the importance of innovation and change management approaches, such as involving health professionals in the design and implementation process to mitigate threat perceptions. We provide future areas of research for the evaluation of the professional identity construct within health care.

Personality traits and their influence on Echo chamber formation in social media: a comparative study of Twitter and Weibo

The echo chamber effect on social media has attracted attention due to its potentially disruptive consequences on society. This study presents a framework to evaluate the impact of personality traits on the formation of echo chambers. Using Weibo and Twitter as platforms, we first define an echo chamber as a network where users interact solely with those sharing their opinions, and quantify echo chamber effects through selective exposure and homophily. We then employ an unsupervised personality recognition method to assign a personality model to each user, and compare the distribution differences of echo chambers and personality traits across platforms and topics. Our findings show that, although user personality trait models exhibit similar distributions between topics, differences exist between platforms. Among 243 personality model combinations, over 20% of Weibo echo chamber members are "ynynn" models, while over 15% of Twitter echo chamber members are "nnnny" models. This indicates significant differences in personality traits among echo chamber members between platforms. Specific personality traits attract like-minded individuals to engage in discussions on particular topics, ultimately forming homogeneous communities. These insights are valuable for developing targeted management strategies to prevent the spread of fake news or rumors.

A three-player game model for promoting enterprise green technology innovation from the perspective of media coverage

Objective

The objective of this study was to explore the game relationship among enterprise, the government, and the public under the new media environment, so as to provide decision-making reference for improving enterprise green technology innovation and promoting economy high-quality development with new media participation.

Methods

This study constructs a three-subject evolutionary game model of enterprise, government, and public based on multi-agent relationship analysis and evolutionary game theory. In addition, the derivation of an evolutionary equilibrium strategy and numerical simulation analysis is carried out to comprehensively explore the evolution trajectory of green technology innovation system under the new media environment.

Findings

(1) The system may have four stable evolutionary strategies: (1,0,0), (0,0,1), (1,0,1), and (1,1,1). (2) The initial strategy probability of various actors would affect the system evolution speed but not the evolution result, and the authenticity of new media reports is an important factor determining the system evolution of green technology innovation. (3) Numerical simulation results show that a fair and just new media environment can effectively constrain the traditional production behavior of enterprise, actively guide the public to participate in supervision, and play an alternative role to government regulation to a certain extent.

Value

This study explores the evolutionary balance strategy of green technology innovation system under the new media environment, which not only enriches relevant theories of media environment governance but also has important reference value for promoting enterprises' green technology innovation and establishing an environmental governance system jointly governed by government, enterprise, public, and media.

Unveiling the affecting mechanism of digital transformation on total factor productivity of Chinese firms

The importance of digital transformation (DGT) for increasing productivity cannot be negated and Chinese firms are rapidly embracing the digital transformation for their sustainability. But the mechanism and impact of digital transformation on total factor productivity (TFP) of firms is still unclear and this study is intended to fill this gap using the data of 3112 listed firms of China during 2011 to 2022. We applied various econometric techniques like stepwise regression analysis, instrumental variable approach, differences in difference approach, and mediating analysis to determine the relationship between digital transformation and TFP and robustness of estimated findings. The findings indicate that DGT has a positive impact on overall TFP of firms in China while operating efficiency, cheaper costs, and a stronger capacity for innovation mediates this relationship. Moreover, it is explored that conventional information and communication technologies have not significant impact on TFP of firms. The findings of the study remain valid even applying many robustness checks and attempts to control the issue of endogeneity. To fully leverage the potential benefits of digital transformation on TFP, it is essential to focus on enhancing digital literacy and skills among the workforce. Governments and relevant stakeholders should prioritize and invest in comprehensive digital literacy and skills training programs to empower the workforce with the knowledge and expertise needed to navigate the digital age effectively.

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.

The impact of digital infrastructure on provincial green innovation efficiency-empirical evidence from China

In the epoch of the digital economy, technological innovation and energy conservation are significantly facilitated by digital infrastructure, leading to substantial improvements in green innovation efficiency at the provincial level. This study employed the feasible generalized least square (FGLS) method to examine the effects of digital infrastructure on the green innovation efficiency across 30 provinces in the Chinese mainland, utilizing panel data from 2011 to 2020. Additionally, this investigation delves into the intervening role of industrial structure upgrading and the amplifying effects of environmental regulation and human capital on the process. Findings indicate that, to begin with, digital infrastructure contributes to the meaningful enhancement of green innovation efficiency within provinces. Subsequently, the industrial structure upgrading partially mediates the impact of digital infrastructure on the efficiency of provincial green innovation. Lastly, both human capital and environmental regulations amplify the beneficial influence of digital infrastructure on the effectiveness of green innovation at the provincial level. This study provides valuable insights into the mechanisms through which digital infrastructure boosts green innovation efficiency, aiding policymakers in formulating appropriate policies to augment digital infrastructure, thereby promoting provincial green innovation efficiency.

How artificial intelligence is reshaping the autonomy and boundary work of radiologists. A qualitative study

The application of artificial intelligence (AI) in medical practice is spreading, especially in technologically dense fields such as radiology, which could consequently undergo profound transformations in the near future. This article aims to qualitatively explore the potential influence of AI technologies on the professional identity of radiologists. Drawing on 12 in-depth interviews with a subgroup of radiologists who participated in a larger study, this article investigated (1) whether radiologists perceived AI as a threat to their decision-making autonomy; and (2) how radiologists perceived the future of their profession compared to other health-care professions. The findings revealed that while AI did not generally affect radiologists' decision-making autonomy, it threatened their professional and epistemic authority. Two discursive strategies were identified to explain these findings. The first strategy emphasised radiologists' specific expertise and knowledge that extends beyond interpreting images, a task performed with high accuracy by AI machines. The second strategy underscored the fostering of radiologists' professional prestige through developing expertise in using AI technologies, a skill that would distinguish them from other clinicians who did not pose this knowledge. This study identifies AI machines as status objects and useful tools in performing boundary work in and around the radiological profession.

Health literacy needs and preferences for a technology-based intervention to improve college students' sexual and reproductive health

Objective: To explore health literacy needs and preferences for a technology-based intervention (app) to improve sexual and reproductive health (SRH) among college students. Participants: In Spring 2019, in-depth interviews were conducted with 20 participants (10 male, 10 female) from a large, public university. Methods: Interview guide was developed based on Integrated Model of Health Literacy domains and Diffusion of Innovation constructs. Data were analyzed in MaxQDA using applied thematic analysis. Results: Dominant themes included accessing health information and services, evaluating options to make decisions, intervention utility and characteristics, and the emergent theme of credibility. Specific topics included accessing STI testing, contraceptive decision making, information on human papillomavirus (HPV) and the HPV vaccine, patient-provider communication, app design and function elements, and modifying the app to meet the SRH needs of diverse college students. Conclusions: Findings identified areas where an app could address college students' SRH literacy, ultimately improving SRH outcomes among this population.

Automated Natural Language Processing-Based Supplier Discovery for Financial Services

Public procurement is viewed as a major market force that can be used to promote innovation and drive small and medium-sized enterprises growth. In such cases, procurement system design relies on intermediates that provide vertical linkages between suppliers and providers of innovative services and products. In this work we propose an innovative methodology for decision support in the process of supplier discovery, which precedes the final supplier selection. We focus on data gathered from community-based sources such as Reddit and Wikidata and avoid any use of historical open procurement datasets to identify small and medium sized suppliers of innovative products and services that own very little market shares. We look into a real-world procurement case study from the financial sector focusing on the Financial and Market Data offering and develop an interactive web-based support tool to address certain requirements of the Italian central bank. We demonstrate how a suitable selection of natural language processing models, such as a part-of-speech tagger and a word-embedding model, in combination with a novel named-entity-disambiguation algorithm, can efficiently analyze huge quantity of textual data, increasing the probability of a full coverage of the market.

Framing doctor-managers' resilience during Covid-19 pandemic: A descriptive analysis from the Italian NHS

With the aim of providing evidence about doctor-managers' resilience during the Covid-19 pandemic, this study analyzes the characteristics of 114 doctor-managers operating within the Italian National Health Service (NHS). During the emergency, doctor-managers had to show adaptive capacities to deal with unexpected situations and develop new paradigms, procedures, and quick responses to patients' needs. This is in line with resilience, and in this perspective, it is crucial to investigate resilience determinants. The paper, therefore, provides an identikit of the resilient doctor-manager. The research was conducted between November and December 2020. Primary data were collected through an online questionnaire consisting of six sections. Participation was voluntary and anonymous. Data were analyzed using quantitative techniques and employing Stata 16. Confirmatory Factor Analysis was employed to test construct validity and scale reliability. Results show that increasing levels of individual resilience are related to increasing levels of managerial identity. Moreover, physicians' individual resilience has a positive association with commitment, knowledge diffusion, and Evidence-Based Medicine adoption. Finally, physicians' individual resilience has a negative association with their role in the university, their specialty, and their gender. The study suggests some practical implications for healtcare organizations. In general, career paths are decided primarily on competency assessment, while an important role should be devoted to behavioral characteristics. Furthermore, organizations should take care of the levels of individual commitment and encourage professional networking because both help doctor-managers cope with uncertainty. The originality of the study relies on a fresh look at all previous work. There are currently few contributions in the literature to explore and investigate resilience elements in doctor-managers during the pandemic era.

Social influence in the darknet market: The impact of product descriptions on cocaine sales

BACKGROUND

The rise of the darknet market, supported by technologies such as the Tor Browser and cryptocurrencies, has created a secure environment in which illicit transactions can occur. However, due to the lack of government oversight in this hidden online domain, darknet markets face significant challenges in upholding social order. Hence, this study explores the social dynamics that promote social order in a darknet market, focusing on the impact of item descriptions on sales. In particular, the study examines how text contained in product listings can influence sales and contribute to social order.

METHOD

To conduct this analysis, we examined 4160 cocaine listings on AlphaBay, which was active from December 2014 to July 2017 and is one of the largest darknet markets in history. Using generalised additive models (GAMs), we assessed the impact of various listing description features, including content and semantic structure, on cocaine sales.

RESULTS

The results showed that sales increased by 61.6 % when listings included delivery information in their description, compared to offers that did not. In addition, the standardised sentiment score (ranging 0,1) of the product description increased positively, and estimated sales increased by 260.5 %. We also found that international shipping reduced sales by 28.3 %. Finally, we found that listings stating the product origin increased sales for all continents except Asia.

CONCLUSION

The study sheds light on the characteristics of product advertising that facilitate social order within a darknet market. Listings that include delivery details in the description reduce uncertainty about a critical stage of the transaction process while using positive language increases trust. This study makes both an empirical and a theoretical contribution by demonstrating the influence of ad descriptions on sales and the intricate role of social influences in shaping market order.

Evaluation in design science: A framework to support project studies in the context of University Research Centres

This paper proposes a discussion of the evaluation of an artefact developed under the Design Science paradigm using the Delphi method. It evaluates the Ecosystem framework of University Research Centres in Project Studies, considering a set of criteria pre-established in the literature. The Delphi method is an evaluation implemented in an electronic platform involving twenty-one participants, among whom were academics, practitioners, and PhD candidates in the field of project management. It reached consensus and stability in two rounds: the results indicate a consensus among the participants in the applicability, novelty, simplicity, completeness, fidelity to modelled phenomena, consistency and internal coherence, scalability, flexibility, interest, elegance, and reusability criteria. Usability was the only criterion that did not attain the predefined percentage of consensus among the participants (70%). Given the framework's characteristics, Delphi participants indicated the need to produce complementary guidelines for its implementation.

Beyond Intrinsic Motivation: Why Researchers Should Consider the Full Motivation Continuum in Games for Health Research

Research within the domain of games for health has predominantly focused on individuals' intrinsic motivation characterized by an inherent enjoyment of the activity. Despite the apparent benefits of intrinsic motivation, we argue that it is imperative to adopt a more nuanced and refined perspective on motivation. Relying on the motivation continuum as outlined within Self-Determination Theory, research within this domain needs to distinguish between both intrinsic and extrinsic (i.e., external, introjected, identified, and integrated regulation) types of motivation. Researchers should, therefore, embrace instruments that assess a broader continuum of motivation rather than just intrinsic motivation alone. By doing so, future research can yield more insight into what fosters autonomous forms of motivation in the field of health-related games, including intrinsic and identified and integrated regulation.

Deep learning-aided decision support for diagnosis of skin disease across skin tones

Although advances in deep learning systems for image-based medical diagnosis demonstrate their potential to augment clinical decision-making, the effectiveness of physician-machine partnerships remains an open question, in part because physicians and algorithms are both susceptible to systematic errors, especially for diagnosis of underrepresented populations. Here we present results from a large-scale digital experiment involving board-certified dermatologists (n = 389) and primary-care physicians (n = 459) from 39 countries to evaluate the accuracy of diagnoses submitted by physicians in a store-and-forward teledermatology simulation. In this experiment, physicians were presented with 364 images spanning 46 skin diseases and asked to submit up to four differential diagnoses. Specialists and generalists achieved diagnostic accuracies of 38% and 19%, respectively, but both specialists and generalists were four percentage points less accurate for the diagnosis of images of dark skin as compared to light skin. Fair deep learning system decision support improved the diagnostic accuracy of both specialists and generalists by more than 33%, but exacerbated the gap in the diagnostic accuracy of generalists across skin tones. These results demonstrate that well-designed physician-machine partnerships can enhance the diagnostic accuracy of physicians, illustrating that success in improving overall diagnostic accuracy does not necessarily address bias.

'Is your accuser me, or is it the software?' Ambiguity and contested expertise in probabilistic DNA profiling

What happens when an algorithm is added to the work of an expert group? This study explores how algorithms pose a practical problem for experts. We study the introduction of a Probabilistic DNA Profiling (PDP) software into a forensics lab through interviews and court admissibility hearings. While meant to support experts' decision-making, in practice it has destabilized their authority. They respond to this destabilization by producing alternating and often conflicting accounts of the agency and significance of the software. The algorithm gets constructed alternately either as merely a tool or as indispensable statistical backing; the analysts' authority as either independent of the algorithm or reliant upon it to resolve conflict and create a final decision; and forensic expertise as resting either with the analysts or with the software. These tensions reflect the forensic 'culture of anticipation', specifically the experts' anticipation of ongoing litigation that destabilizes their control over the deployment and interpretation of expertise in the courtroom. The software highlights tensions between the analysts' supposed impartiality and their role in the courtroom, exposing legal and narrative implications of the changing nature of expertise and technology in the criminal legal system.

Telemedicine experiences and perspectives of healthcare providers in long-term care: A scoping review

OBJECTIVE

To consolidate existing evidence on experiences and perspectives of healthcare providers involved in telemedicine services in long-term residential care.

METHODS

A scoping review was conducted. A systematic search for articles published in 2000-2021 was performed in CINAHL, Web of Science, PubMed, EMBASE and Scopus; further, relevant journals and grey literature websites were hand searched. Key search terms included 'telemedicine', 'telehealth' and 'nursing homes'.

RESULTS

Twenty-six articles were included. A narrative synthesis of evidence was conducted. The review identified four themes: (1) Presence of multidisciplinary care, (2) perceived usefulness of telemedicine, (3) perceived ease of use and (4) expanded role of nursing home staff. The presence of multidisciplinary care providers provided a wide range of telemedicine services to residents and promoted interprofessional collaboration between acute and long-term care. Telemedicine was perceived to increase timely onsite management by remote specialists, which enabled care quality improvement. However, technical problems associated with equipment usage reduced the ease of use of telemedicine. Concerns emerged from the expanded role of nursing home staff, which could negatively affect clinical decision-making and create medico-legal risks.

CONCLUSION AND IMPLICATIONS

Telemedicine is valuable in distance-based care, especially in the current 2019 coronavirus pandemic, for supporting continuity of care to nursing home residents. This review provided evidence from multiple healthcare providers' perspectives. Further research can elucidate their specific roles and responsibilities in telemedicine and challenges in work processes, which will facilitate developing evidence-based competencies and improving technical infrastructure, thus contributing to personal and organisational readiness for telemedicine integration.

Applying the Affective Events Theory to Explore the Effect of Daily Micro-Interruptions on Mental Health: The Mediating Role of Affect and the Moderating Role of Pets at Work

This study relied on the affective events theory and the social exchange theory to develop a framework that explains how situational factors (daily micro-interruptions) enhance affective reactions (negative affect) and, in turn, impair health conditions (mental health) at work. We further delineate theoretical arguments to propose the pet-human's health effect by demonstrating that pets are boundary conditions that attenuate this relation, and as such are protective conditions for employees' mental health. We conducted a 5-day diary study with two groups of participants, one with participants who owned pets (N = 82 x 5 = 410), and the other who did not own pets (N = 87 x 5 = 435). The multilevel results showed an indirect effect of daily micro-interruptions on individuals' mental health through negative affect, with a daily backdrop of poorer mental health for those who did not own a pet (compared to those who owned a pet). These results evidence the benefits of owning a pet for individuals' mental health, even at work, and as such provide recommendations for teleworking practices. Moreover, this study resorts to an innovative and robust data collection method to demonstrate the pet-human' health effect. This study expands knowledge on the role of pets in working daily routines and shows that pets may be a personal resource for individuals while working.

Digital transformation and corporate financialization in emerging markets: Evidence from China

Corporate financialization poses serious challenges to the development of the real economy. In the context of promoting the deep integration of the digital economy and the real economy, it is crucial to explore whether digital transformation can inhibit corporate financialization. Using data from Chinese listed companies from 2009 to 2021, we construct a fixed effects model and find that digital transformation significantly reduces the level of corporate financialization, a conclusion that still holds after a series of robustness tests such as propensity score matching and adding control variables. Channel analysis shows that that digital transformation inhibits corporate financialization by enhancing the information mobility and operational capability of corporations. In addition, this effect is more pronounced at higher levels of industry competition as well as marketization. Finally, we also find structural differences in the impact of digital transformation on corporate financialization. Our study explores the determinants of corporate financialization in terms of a firm's mode of operation and type of strategy, and the findings provide a theoretical basis for the active development of digital technologies in emerging markets that are undergoing economic transitions, as well as for guarding against the shift of the economy from the real to the virtual.

An Environmental Uncertainty Perception Framework for Misinformation Detection and Spread Prediction in the COVID-19 Pandemic: Artificial Intelligence Approach

BACKGROUND

Amidst the COVID-19 pandemic, misinformation on social media has posed significant threats to public health. Detecting and predicting the spread of misinformation are crucial for mitigating its adverse effects. However, prevailing frameworks for these tasks have predominantly focused on post-level signals of misinformation, neglecting features of the broader information environment where misinformation originates and proliferates.

OBJECTIVE

This study aims to create a novel framework that integrates the uncertainty of the information environment into misinformation features, with the goal of enhancing the model's accuracy in tasks such as misinformation detection and predicting the scale of dissemination. The objective is to provide better support for online governance efforts during health crises.

METHODS

In this study, we embraced uncertainty features within the information environment and introduced a novel Environmental Uncertainty Perception (EUP) framework for the detection of misinformation and the prediction of its spread on social media. The framework encompasses uncertainty at 4 scales of the information environment: physical environment, macro-media environment, micro-communicative environment, and message framing. We assessed the effectiveness of the EUP using real-world COVID-19 misinformation data sets.

RESULTS

The experimental results demonstrated that the EUP alone achieved notably good performance, with detection accuracy at 0.753 and prediction accuracy at 0.71. These results were comparable to state-of-the-art baseline models such as bidirectional long short-term memory (BiLSTM; detection accuracy 0.733 and prediction accuracy 0.707) and bidirectional encoder representations from transformers (BERT; detection accuracy 0.755 and prediction accuracy 0.728). Additionally, when the baseline models collaborated with the EUP, they exhibited improved accuracy by an average of 1.98% for the misinformation detection and 2.4% for spread-prediction tasks. On unbalanced data sets, the EUP yielded relative improvements of 21.5% and 5.7% in macro-F1-score and area under the curve, respectively.

CONCLUSIONS

This study makes a significant contribution to the literature by recognizing uncertainty features within information environments as a crucial factor for improving misinformation detection and spread-prediction algorithms during the pandemic. The research elaborates on the complexities of uncertain information environments for misinformation across 4 distinct scales, including the physical environment, macro-media environment, micro-communicative environment, and message framing. The findings underscore the effectiveness of incorporating uncertainty into misinformation detection and spread prediction, providing an interdisciplinary and easily implementable framework for the field.

A survey of deep learning applications in cryptocurrency

This study aims to comprehensively review a recently emerging multidisciplinary area related to the application of deep learning methods in cryptocurrency research. We first review popular deep learning models employed in multiple financial application scenarios, including convolutional neural networks, recurrent neural networks, deep belief networks, and deep reinforcement learning. We also give an overview of cryptocurrencies by outlining the cryptocurrency history and discussing primary representative currencies. Based on the reviewed deep learning methods and cryptocurrencies, we conduct a literature review on deep learning methods in cryptocurrency research across various modeling tasks, including price prediction, portfolio construction, bubble analysis, abnormal trading, trading regulations and initial coin offering in cryptocurrency. Moreover, we discuss and evaluate the reviewed studies from perspectives of modeling approaches, empirical data, experiment results and specific innovations. Finally, we conclude this literature review by informing future research directions and foci for deep learning in cryptocurrency.

Knowledge Transfer Within Organizations: Mechanisms, Motivation, and Consideration

Knowledge transfer-the extent to which one unit learns from or is affected by the experience of another-has the potential to improve the performance of organizations. Through knowledge transfer, developments made in one unit of an organization can benefit others. Studies have found, however, considerable variation in the extent to which knowledge transfers across organizational units. In some cases, knowledge transfers seamlessly, whereas in others, knowledge transfer is far from complete. This article reviews research with the aim of explaining the variation observed in knowledge transfer. Key factors identified as explaining the variation include knowledge transfer opportunities, knowledge characteristics, mechanisms for knowledge transfer, motivation for transfer, and the depth of consideration of knowledge. These factors are integrated into a theoretical framework that predicts when knowledge transfer will be successful. The article concludes with a discussion of directions for future research to increase our understanding of knowledge transfer in organizations.

The impact of environmental regulatory instruments on agribusiness technology innovation-A study of configuration effects based on fsQCA

This paper investigates the complex causal relationships between various types of environmental regulatory instruments (ERI) and agri-firms' technological innovation employing fuzzy set qualitative comparative analysis (fsQCA). The study finds a well-designed set of ERI can promote technological innovation in agribusiness; control-command ERI cannot promote technological innovation in agribusiness solely, market-incentivized ERI is indispensable in promoting firms' innovation performance, implicit ERI plays an important role in promoting firms' innovation and voluntary ERI does not play a significant role in promoting firms' technological innovation. The government should coordinate among various types of ERI and improve the design of ERI to achieve a win-win situation for both economic and environmental performance in the agriculture sector.

Systematic review using a spiral approach with machine learning

With the accelerating growth of the academic corpus, doubling every 9 years, machine learning is a promising avenue to make systematic review manageable. Though several notable advancements have already been made, the incorporation of machine learning is less than optimal, still relying on a sequential, staged process designed to accommodate a purely human approach, exemplified by PRISMA. Here, we test a spiral, alternating or oscillating approach, where full-text screening is done intermittently with title/abstract screening, which we examine in three datasets by simulation under 360 conditions comprised of different algorithmic classifiers, feature extractions, prioritization rules, data types, and information provided (e.g., title/abstract, full-text included). Overwhelmingly, the results favored a spiral processing approach with logistic regression, TF-IDF for vectorization, and maximum probability for prioritization. Results demonstrate up to a 90% improvement over traditional machine learning methodologies, especially for databases with fewer eligible articles. With these advancements, the screening component of most systematic reviews should remain functionally achievable for another one to two decades.

Intra- and Inter-Individual Associations of Family-to-Work Conflict, Psychological Distress, and Job Satisfaction: Gender Differences in Dual-Earner Parents during the COVID-19 Pandemic

The COVID-19 pandemic changed the work-family interface dynamics in some families. For couples who kept earning a double income during the pandemic, their family demands may entail a loss of psychological resources that affect the work domain. This study explored the intra-individual and inter-individual (crossover) direct and indirect effects of family-to-work conflict (FtoWC) on psychological distress and job satisfaction in a non-probabilistic sample of 860 different-sex dual-earner parents with adolescent children from Temuco and Rancagua, Chile. Mothers and fathers answered an online questionnaire measuring FtoWC, the Depression, Anxiety, and Stress Scale, and the Overall Job Satisfaction Scale. The data were analyzed using the actor-partner interdependence model with structural equation modeling. Results showed that a higher FtoWC is linked to greater psychological distress and lower job satisfaction in both parents. In contrast, psychological distress is directly linked to lower job satisfaction in fathers. In both fathers and mothers, they and their partners' FtoWC were indirectly linked to lower job satisfaction via the fathers' psychological distress. These findings indicate the need for gender-sensitive social and labor policies aimed at reducing the conflict between family and work to increase job satisfaction in both parents and reduce psychological distress, particularly in fathers.

Research on the evaluation and configuration path of China's rural common Prosperity-NCA and fsQCA based on provincial panel data

Based on 2013-2019 panel data covering 31 provinces and cities in China, this paper uses the entropy weight technique for order of preference by similarity to ideal solution (TOPSIS) method to measure rural common prosperity (CP). Based on the global and local Moran's I methods, we analyze the dynamic evolutionary characteristics of China's rural CP and the regional differences. Additionally, we use the necessary condition analysis (NCA) and fuzzy-set qualitative comparative analysis (fsQCA) methods to explore how six antecedents at the technology-organization-environment (TOE) level interact to affect CP. This research finds that, first, China's rural CP showed a fluctuating upward trend, with the highest level of CP in rural areas in the eastern region, followed by the central and western regions. Additionally, the gap between the three gradually narrowed. Second, China's rural CP had "high-high" and "low-low" agglomeration characteristics, with positive spatial autocorrelation, no transition changes, and strong spatial stability. Third, individual digital economic elements and organizational and environmental elements were not necessary conditions for promoting rural CP. Fourth, the multiple concurrent factors of the digital economy, organizations and the environment constituted three diversified configurations of rural CP, showing that the driving path of rural CP was characterized by "different paths that lead to the same goal". Moreover, "perfect digital facilities" and "high entrepreneurial activity" had a universal role in promoting rural CP. The conclusions of this research hold important theoretical and practical significance for improving China's rural CP.

From atoms to bits: Resource mobilization of non-digital, hybrid, and digital cleantech startups

Climate change is one of the most critical challenges to tackle in this century, where innovations developed and commercialized by cleantech startups are crucial contributors to achieve emission reduction targets. Entrepreneurship scholars have long presumed that resource mobilization is essential for startups to transit successfully through the conception and commercialization life cycle stages. Yet, we have a limited understanding of how resource mobilization varies across the three startups types of non-digital, hybrid - an intermediate type of non-digital and digital startups -, and digital cleantech startups. Drawing on insights from 16 semi-structured interviews with startups, investors, and industry experts in the U.S. cleantech industry, as well as secondary data, this study provides a novel framework that identifies the resource mobilization approaches of cleantech startups disentangled by the three startup types. The findings indicate that non-digital cleantech startups face the most severe resource mobilization challenges, followed by hybrid and digital cleantech startups, respectively. The study contributes to the literature on resource mobilization of cleantech startups and digital entrepreneurship. It also outlines implications for startups and venture capital investors as practitioners as well as for policymakers.

Social media use and social capital: Social media usage habits and perceptions of school administrators

Social media allows people to spend time together online and interact and connect. The relationship between well-being and social media usage is being studied more and more because it affects many areas and is discussed from different aspects. In this study, a complete count was made, and an online questionnaire was applied to all 1282 school administrators working at Antalya Provincial National Education public schools. Results reveal how the social media usage habits and social media perceptions of school administrators are, and accordingly how social media may help fertilize their social capital and establishing a healthy work environment. In light of the findings, social media usage habits are gathered under four dimensions: research/information, entertainment, recognition, and development. These gratified needs are the key elements of building one's social capital.

Can Positive Emotions Predict Consumer Satisfaction in Adverse Services?

Background

Managing emotions during hospital visits is important to enhance patient satisfaction. The purpose of this paper is to explore the relationship between patients' happiness and satisfaction through patients' mood and perceived service quality at a healthcare setup.

Methods

This study was conducted in a tertiary care hospital located in coastal Karnataka during the period from November to December 2021. Primary data was collected through a structured questionnaire from 227 respondents. "Statistical Package for the Social Sciences (SPSS) 27.0" and "SmartPLS 3.0" software was used for data analysis.

Results

Hypotheses proposed in this study were examined by comparing the direct effect of patients' happiness level on perceived service quality and the indirect effect of the level of patients' happiness on patient satisfaction. The influence of all the exogenous latent variables namely, happiness, mood, perceived service quality, on the endogenous latent variable of patient satisfaction is estimated to be moderate (R 2=62.5%).

Conclusion

This study empowers hospital managers to recognize how patient satisfaction is dependent on patients' happiness. In order to enhance patient satisfaction, the quality of care provided by health services, human resources, and infrastructure must be improved. As a result, the entire service encounter can be made enjoyable to the customers by reducing the distress caused by adverse services. Managers can utilize the outcomes of the study to develop marketing strategies to influence patients' emotions in the healthcare setup by modifying the servicescape elements.

Predictive Model of the Relationship between Positive and Negative Affect, Self-Consciousness of Appearance, and Positive Body Image in Physical Exercise Practice

The biopsychosocial development of individuals is influenced by body image and physical exercise. This study aimed to evaluate seven hypotheses regarding the impact of positive affect, negative affect, self-consciousness of appearance, and positive body image on the practice of physical exercise. The data were processed using the statistical package SmartPLS 4.0.9.5. Using an exploratory methodology based on structural equation modeling (SEM) of structural equations applied to small sample sizes, a tentative model has been generated to define the drivers of physical activity. The survey used the Body Mass Index, Body Appreciation Scale-Revised, and Derriford Appearance Scale-14 items. The study involved 129 men and 279 women (N = 408). The model demonstrated a distinct internal consistency in that, out of the seven hypotheses analyzed, only one was rejected. The findings of the multigroup analysis indicate that there are no statistically significant discrepancies between the constructed measures and the practice of physical activity. The conclusions of this study showed that, although positive and negative affect, and self-consciousness influence positive body image, there were no statistically significant differences between those who exercise and those who do not exercise. Various factors influence biopsychosocial development. Future studies should explore the influence of psychological and social variables on understanding body image and physical exercise.

Privacy Concerns About Sharing General and Specific Health Information on Twitter: Quantitative Study

BACKGROUND

Twitter is a common platform for people to share opinions, discuss health-related topics, and engage in conversations with a wide audience. Twitter users frequently share health information related to chronic diseases, mental health, and general wellness topics. However, sharing health information on Twitter raises privacy concerns as it involves sharing personal and sensitive data on a web-based platform.

OBJECTIVE

This study aims to adopt an interactive approach and develop a model consisting of privacy concerns related to web-based vendors and web-based peers. The research model integrates the 4 dimensions of concern for information privacy that express concerns related to the practices of companies and the 4 dimensions of peer privacy concern that reflect concerns related to web-based interactions with peers. This study examined how this interaction may affect individuals' information-sharing behavior on Twitter.

METHODS

Data were collected from 329 Twitter users in the United States using a web-based survey.

RESULTS

Results suggest that privacy concerns related to company practices might not significantly influence the sharing of general health information, such as details about hospitals and medications. However, privacy concerns related to companies and third parties can negatively shape the disclosure of specific health information, such as personal medical issues (β=-.43; P<.001). Findings show that peer-related privacy concerns significantly predict sharing patterns associated with general (β=-.38; P<.001) and specific health information (β=-.72; P<.001). In addition, results suggest that people may disclose more general health information than specific health information owing to peer-related privacy concerns (t165=4.72; P<.001). The model explains 41% of the variance in general health information disclosure and 67% in specific health information sharing on Twitter.

CONCLUSIONS

The results can contribute to privacy research and propose some practical implications. The findings provide insights for developers, policy makers, and health communication professionals about mitigating privacy concerns in web-based health information sharing. It particularly underlines the importance of addressing peer-related privacy concerns. The study underscores the need to build a secure and trustworthy web-based environment, emphasizing the significance of peer interactions and highlighting the need for improved regulations, clear data handling policies, and users' control over their own data.

Extending the job demands-resources model to understand the effect of the interactions between home and work domains on work engagement

Scholars have extensively used the job demands-resources model to explain the dynamics underlying work engagement and proposed several versions of the model. However, in theoretical terms, nonwork elements have not been incorporated into the model. This study investigated the roles of home demands and resources in the model by testing the boost/buffer hypotheses for work engagement from the perspective of the work-home interface. We demonstrated that (1) the demands of a domain boost the positive impact of resources drawn from another domain on work engagement, (2) the resources of a domain buffer the negative impact of demands derived from another domain on work engagement; and (3) the buffering and boosting effects of home demands and resources impact work engagement. We conducted a diary study on a group of coffee shop employees in Ireland. The results partially supported the proposed hypotheses but nevertheless indicated support for the cross-domain boost/buffer hypotheses with regard to work engagement. The proposed model may serve as a theoretical foundation for research on issues related to the impact of work and nonwork domains on work engagement.

Motivation crowding effects on the intention for continued use of gamified fitness apps: a mixed-methods approach

It has become an emerging idea for fitness apps to be gamified to intrinsically and extrinsically motivate user's usage intention or behavior. For the long-term success of gamified fitness apps, understanding what and how various motivations affect continued use is critical. A combination of qualitative and quantitative methodologies was used to explore two research questions concerning gamified fitness applications. Specifically, the research questions focused on understanding the impacts of intrinsic and extrinsic motivations on continued usage. Additionally, the study aimed to investigate whether extrinsic motivations enhance or diminish the influence of intrinsic motivations. Results from qualitative study identified three intrinsic motivations (self-development, self-control and hedonic motivation) and two extrinsic motivations (social recognition and financial reward) in gamified fitness apps. Results from quantitative study indicated that intrinsic motivations (formed by self-development, self-control and hedonic motivation), financial reward and social recognition could significantly improve intention for continued use; and further, both financial reward and social recognition could crowd-in intrinsic motivations. This research offers insights into the phenomenon of motivation crowding effects on the intention to continue using gamified fitness apps.

A Sustainable Model for Healthcare Systems: The Innovative Approach of ESG and Digital Transformation

In recent years, the globe has faced a series of topics of growing concern, such as the COVID-19 pandemic, the international financial crisis, rising socio-economic inequalities, the negative outcomes of greenhouse gas emissions, which resulted in climate change, and many others. Organizations worldwide have confronted these new challenges of sustainable finance by incorporating environmental, social, and corporate governance (ESG) factors and digital transformation (DT) in their innovation business strategies. The healthcare sector represents a large share of the global economy (about 10% of global economic output), employs a large number of workers, and needs to rely more on an open innovation model where interested parties, especially patients, are going to have a say in their own well-being. Thus, it is imperative that healthcare providers be efficient, effective, resilient, and sustainable in the face of significant challenges and risks. At the same time, they must offer sustainable development goals and digital transformation to healthcare users through limited governmental resources. This study investigates the role, importance, and correlation of ESG factors and digital transformation to the sustainable finance of healthcare systems through an innovative model. The main purpose of the paper is to present the already implemented ESG and DT factors in the healthcare sector and to propose a mutual and combined implementation strategy based on common evaluation tools, methods, and actions. A set of proposed actions and strategies are presented for the sustainability and resilience of the healthcare sector.

How article category in Wikipedia determines the heterogeneity of its editors

Collaboration is essential to advancing knowledge and, ultimately, entire societies. With the development of Web 2.0, the possibilities have risen to unprecedented levels and allowed for the collaborative creation of the world's largest compendium of knowledge that ever existed - Wikipedia. Collaboration is not a safeguard of quality per se, however. Rather, the quality of Wikipedia articles rises with the number of editors per article as well as a greater diversity among them. Here, we address a not yet documented potential threat to those preconditions: self-selection of Wikipedia editors to articles. Specifically, we expected articles with a clear-cut link to a specific country (e.g., about its highest mountain, "national" article category) to attract a larger proportion of editors of that nationality when compared to articles without any specific link to that country (e.g., "gravity", "universal" article category), whereas articles with a link to several countries (e.g., "United Nations", "international" article category) should fall in between. Across several language versions, hundreds of different articles, and hundreds of thousands of editors, we find the expected effect within Wikipedia: The more exclusively an article topic is linked to a particular nation, the higher the proportion of editors from that country is among the contributors.

The willingness and influencing factors to choose institutional elder care among rural elderly: an empirical analysis based on the survey data of Shandong Province

BACKGROUND

The ageing of the population has become an escalating problem in China, which has led to an increasing demand for healthcare throughout society. The care services of elderly institutions, as a more mature way of aging, can alleviate various social problems brought about by ageing to a certain extent. The aim of this paper is to explore the degree of acceptance of institutional care by rural elderly people in Shandong Province and the factors that influence whether rural elderly people accept institutional care services.

METHODOLOGY

Based on the theory of planned behavior, an analytical framework was constructed for the willingness of rural elderly people to receive nursing services from elderly care institutions. Using survey data from 192 rural elderly people in Shandong Province, descriptive statistics, binary logistic regression, and horizontal comparative analysis methods were used to analyze the willingness of rural elderly people to provide for the elderly and its influencing factors.

RESULT

Only 17.71% of respondents expressed willingness to receive services from elderly care institutions. Among them, education level, trust in elderly care institutions, and support from adult children have a significant positive impact on whether rural elderly people receive nursing services from elderly care institutions; The number of children, the level of understanding of elderly care institutions, neighbors' choices of elderly care methods, and their ability to contribute to the family have a significant negative impact on whether rural elderly people receive nursing services from elderly care institutions. There are significant differences in the willingness and influencing factors of rural elderly people to provide for the elderly among different regions.

CONCLUSION

The non-acceptance of institutional care by rural older people is a general phenomenon rather than a sample characteristic, thus justifying the supplementary status of institutional care services. The pension intention of the rural elderly in Shandong Province is obviously affected by personal will factors, and the influencing factors are various. The traditional concept of old-age care in Shandong province has a strong path-dependent effect on the choice of the rural elderly. There is heterogeneity in the willingness and influencing factors of the rural elderly in different regions and countries. Based on this, this paper puts forward the following suggestions: strengthen the spiritual and cultural construction of residents; The government should pay attention to the correct guidance of public opinion; And increase pension subsidies. It is hoped that reduce the burden of national elderly care through these suggestions.

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 acceptance and attitudes towards using assistive technology for people with stroke in Jordan: caregivers' perspectives

The aim of this study is to evaluate the acceptance and attitudes toward assistive technology (AT) for people with stroke from their caregivers' perspectives in Jordan. Also, this study further validates the Unified Theory of Acceptance and Use of Technology (UTAUT) questionnaire. This is a cross-sectional study that includes a three-sections survey: the first section contains demographic data, the second section includes the Unified Theory of Acceptance and Use of Technology (UTAUT) questionnaire, and the third section consists of two optional open-ended questions. Among 123 caregivers of people with stroke over 18 years of age who participated in this study, 23% were male. The findings indicate a moderate to high acceptance of using AT for stroke survivors, confirm the validity and reliability of the UTAUT questionnaire, and clarify novel issues regarding AT based on the caregivers' perspectives.

Academics' continuance intention to use learning technologies during COVID-19 and beyond

The COVID-19 pandemic has revolutionized the teaching pedagogy in higher education as universities are forecasted to increase investments in learning technology infrastructure to transition away from traditional teaching methods. Therefore, it is crucial to investigate whether academics intend to continually integrate learning technologies as part of a permanent pedagogical change beyond the COVID-19 pandemic. Drawing upon the Unified Theory of Acceptance and Use of Technology (UTAUT), and Expectation Confirmation Model (ECM), this study examines the salient determinants influencing the continuance intention of academics to use learning technologies in their teaching pedagogy during and after COVID-19. Primary data collected from a private university was analyzed using the partial least squares structural equation modelling technique (PLS-SEM). The findings revealed two sequential mediating relationships which serve as the mechanism linking the relationship between facilitating conditions and their continuance intention to use learning technologies during and beyond the COVID-19 pandemic.

Sorting Insiders From Co-Workers: Remote Synchronous Computer-Mediated Triage for Investigating Insider Attacks

OBJECTIVE

Develop and investigate the potential of a remote, computer-mediated and synchronous text-based triage, which we refer to as InSort, for quickly highlighting persons of interest after an insider attack.

BACKGROUND

Insiders maliciously exploit legitimate access to impair the confidentiality and integrity of organizations. The globalisation of organisations and advancement of information technology means employees are often dispersed across national and international sites, working around the clock, often remotely. Hence, investigating insider attacks is challenging. However, the cognitive demands associated with masking insider activity offer opportunities. Drawing on cognitive approaches to deception and understanding of deception-conveying features in textual responses, we developed InSort, a remote computer-mediated triage.

METHOD

During a 6-hour immersive simulation, participants worked in teams, examining password protected, security sensitive databases and exchanging information during an organized crime investigation. Twenty-five percent were covertly incentivized to act as an 'insider' by providing information to a provocateur.

RESULTS

Responses to InSort questioning revealed insiders took longer to answer investigation relevant questions, provided impoverished responses, and their answers were less consistent with known evidence about their behaviours than co-workers.

CONCLUSION

Findings demonstrate InSort has potential to expedite information gathering and investigative processes following an insider attack.

APPLICATION

InSort is appropriate for application by non-specialist investigators and can be quickly altered as a function of both environment and event. InSort offers a clearly defined, well specified, approach for use across insider incidents, and highlights the potential of technology for supporting complex time critical investigations.

Exploring nationwide policy interventions to control COVID-19 from the perspective of the rapid learning health system approach

Introduction

The health systems needed to improve their learning capacities during the COVID-19 pandemic. Iran is one of the countries massively struck by the pandemic. This study aimed to explore whether and how the policy interventions made by Iran's policymakers at the national level to control COVID-19, could improve the rapid learning characteristics of the health system.

Methods

A guide to clarify rapid learning health system (RLHS) characteristics was developed. The guide was used by two independent authors to select the policy interventions that could improve RLHS characteristics, then, to analyze the content of the selected policy interventions. In each stage, results were compared and discussed by all three authors. Final results were presented based on different RLHS characteristics and the potential mechanisms of contribution.

Results

Five hundred policy interventions were developed during the first 7 months of the outbreak. Thirty-one policy interventions could potentially improve RLHS characteristics (6.2%). Two characteristics, such as the timely production of research evidence and the appropriate decision support were addressed by selected policy interventions. Policies, that could improve learning capacities, focused on decision-maker groups more than user groups or researcher groups.

Conclusions

Most of the developed policy interventions during the first months of the epidemic did not address the learning capacities of the health system. To improve health system functions, improving RLHS characteristics of the health system, especially in patient-centered and data linkage characteristics, is recommended.

Empirical Evidence of Internal and External Factors Influencing Users' Motivation Toward Teleconsultation Use

Introduction: The use of teleconsultation systems has increased in recent years, which has improved patients' access to health care providers and enabled seamless interaction between them. The literature points out several factors that either facilitate or impede the use of teleconsultation. However, there is a lack of studies that provide empirical evidence of factors that influence consumers' motivation toward the use of teleconsultation systems. Aim and Objective: This study aimed to provide empirical evidence of the internal and external factors that influence consumers' motivation toward the use of teleconsultation systems. Methods: A cross-sectional survey was used to collect data from consumers who used a real-time teleconsultation system called the Sehha application in Saudi Arabia between March 13 and June 14, 2021. SPSS 27.0.1 was used for descriptive analysis. Results: Four hundred eighty-five participants completed the survey, 471 of whom were included in the analysis. The findings confirmed that internal and external factors exert an influence on consumers' motivation toward the use of teleconsultation systems. The findings indicated that the presence of factors such as saving time, saving cost, accessibility to health care, ease-of-use, reliable internet access, availability of devices, and appropriate places during the online connection would increase consumers' motivation toward teleconsultation systems use. Also, the findings indicated that users' familiarity with systems similar to teleconsultation systems, users' perception of teleconsultation convenience, the influence of others on users' decision to use teleconsultation, and user's skills and confidence in using teleconsultation easily, and their trust in the teleconsultation system would also increase their motivation to use it. Furthermore, the findings showed that demographic factors, including age, gender, level of education, and employment status, did not influence users' motivation toward the use of teleconsultation Conclusions: This study provided empirical evidence of a variety of internal and external factors that exert an influence on consumers' motivation toward the use of teleconsultation systems.

Examining information systems use to facilitate the workplace accommodation process

BACKGROUND

The workplace accommodation process is often affected by ineffective and inefficient communications and information exchanges among disabled employees and other stakeholders. Information systems (IS) can play a key role in facilitating a more effective and efficient accommodation process since IS has been shown to facilitate business processes and effect positive organizational changes.

OBJECTIVE

Since there is little to no research that exists on IS use to facilitate the workplace accommodation process, this paper, as a critical first step, examines how IS have been used in the accommodation process.

METHODS

Thirty-six interviews were conducted with disabled employees from various organizations. Open, axial, and selective coding were part of the analysis. Fuzzy set qualitative comparative analysis was used to identify different levels of IS use based on participants' descriptions.

RESULTS

An IS used in the workplace accommodation process consists of electronic request form, accommodation checklist, special budget, specific role, ancillary service, formal policy and procedure. There are different levels of IS use in the current accommodation process. The high-level IS use often results in a better accommodation performance than the low-level IS use, including high efficiency, high effectiveness, and low emotional tolls. Nevertheless, the high-level IS use often uses a specific, inflexible template as well as disregards human elements in the accommodation process.

CONCLUSION

This work provides implications that future IS design should raise awareness of disability and accommodation, account for individual differences, involve multiple stakeholder inputs, as well as address the fundamental social issues in the accommodation process.

Association between digital engagement and urban-rural disparities in Chinese women's depressive symptoms: A national-level cross-sectional study

Objectives

This study aims to investigate the impact of digital engagement on urban-rural disparities in depressive symptoms among Chinese women.

Methods

Using a dataset from the China Family Panel Studies (CFPS) wave 2020, this study analyzes the impact of digital engagement on the urban-rural disparity in women's depressive symptoms using multiple linear regression and recentered influence function (RIF) models. Furthermore, the extent to which digital engagement affects the urban-rural disparity in women's depressive symptoms was calculated using the RIF decomposition method.

Results

Analysis showed that rural women had significantly higher levels of depressive symptoms compared to urban women; digital engagement significantly reduced women's depressive symptoms levels and mitigated the urban-rural disparity for women with moderate to high levels of depressive symptoms, and the mitigating effect was stronger for the highly depressed sample, but still widened the urban-rural disparity in women's depressive symptoms overall. In addition, the results of the RIF decomposition showed that digital engagement explained 28.28% of the urban-rural disparity in women's depressive symptoms.

Conclusion

There is a significant disparity in depressive symptoms levels between urban and rural women in China. Digital engagement reduces women's depressive symptoms, but it also widens the depressive symptoms disparity between urban and rural women overall. Digital engagement is potentially positive for reducing women's depressive symptoms.

Strategy development to increase consumer intention of purchasing prescription drugs through e-pharmacy in Indonesia

BACKGROUND

The global COVID-19 pandemic has forced people to obtain health products and services from home. Similar to other e-commerce, medicines are bought online and delivered using a courier service.

OBJECTIVE

By being fully concerned to patient safety, this study aims to determine development strategies to increase intention in purchasing prescription drugs through e-pharmacy.

METHODS

Two stages of measurement are employed in this study, namely confirmatory factor analyis using PLS-SEM and pairwise comparison using AHP method. To discover consumer perception in using e-pharmacy, the basic model of Theory of Planned Behavior (TPB) is employed with several extensions.

RESULTS

The results of PLS-SEM express that Trust has a major role as an intervening variable to enhance the indirect effect of Subjective Norms and Perceived Values on Purchase Intention. In general, PLS-SEM structural model is declared "fit" (GFI = 0.93 ≥ 0.90; RMSEA = 0.045 ≤ 0.08; SRMR = 0.033 ≤ 0.05). Measurement model test proves that all selected indicators are valid to represent their related constructs (Loading Factor ≥ 0.50), and all selected constructs are reliable to build the whole path model (CR ≥ 0.7; AVE ≥ 05). Meanwhile, the results of AHP indicate that strengthening government policies and regulations is prioritized to increase consumer intention of purchasing prescription drugs through e-pharmacy, followed by protection of user confidential data in the second place. Those two eigenvectors are 0.236 and 0.185 respectively.

CONCLUSION

Future research is suggested to add perceived risk as latent variable in the study of consumer behavior for any high-risk products.

Use of Social Media in Orthopaedic Surgery Training and Practice: A Systematic Review

Background

Social media use has grown across healthcare delivery and practice, with dramatic changes occurring in response to the coronavirus (COVID-19) pandemic. The purpose of this study was to conduct a comprehensive systematic review to determine the current landscape of social media use by (1) orthopaedic surgery residencies/fellowship training programs and (2) individual orthopaedic surgeons and the change in use over time.

Methods

We searched 3 electronic databases (PubMed, MEDLINE, and Embase) from their inception to April 2022 for all studies that analyzed the use of social media in orthopaedic surgery. Two reviewers independently determined study eligibility, rated study quality, and extracted data. Methodology was in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines.

Results

Twenty-eight studies were included, of which 11 analyzed social media use by orthopaedic surgery residency and fellowship training programs and 17 examined its use by individual orthopaedic surgeons. Among residency and fellowship programs, Instagram was identified as the most common platform used, with 42% to 88% of programs reporting program-specific Instagram accounts, followed by Twitter/X (20%-52%) and Facebook (10%-38%). Social media was most commonly used by programs for recruitment and information dissemination to prospective residency applicants (82% and 73% of included studies, respectively). After the start of the COVID-19 pandemic, there was a 620% and 177% increase in the number of training programs with Instagram and Twitter/X accounts, respectively. Individual use of social media ranged from 1.7% to 76% (Twitter/X), 10% to 73% (Facebook), 0% to 61% (Instagram), 22% to 61% (LinkedIn), and 6.5% to 56% (YouTube).

Conclusions

Instagram, Twitter/X, and Facebook are the premier platforms that patients, residency applicants, and institutions frequent. With the continued growth of social media use anticipated, it will be critical for institutions and individuals to create and abide by guidelines outlining respectful and professional integration of social media into practice.

Level of Evidence

Level IV.

The Store-and-Forward Telemedicine Service User-satisfaction Questionnaire: Development and validation of a questionnaire to monitor and assess health care providers' experiences

INTRODUCTION

With wider adoption of store-and-forward telemedicine accelerating, particularly post-coronavirus disease 2019, it is essential to understand health care providers' (HCPs) satisfaction with digital solutions offered by telemedicine organizations to (continuously) guarantee service quality. We developed the Store-and-Forward Telemedicine Service User-satisfaction Questionnaire to monitor and assess HCPs' experiences with contracted telemedicine organizations.

METHODS

Questionnaire construction (phase 1) consisted of exploratory literature search on validated telemedicine satisfaction questionnaires, a telemedicine domain and human factors expert focus group, stakeholder focus group (customer service employee and telemedicine account managers), and two pre-testing rounds among 18 HCPs. The pilot questionnaire (phase 2) was sent to 2179 HCPs for validity and reliability assessment.

RESULTS

Phase 1: Two validated questionnaires (73 items overall) were used as input for Store-and-Forward Telemedicine Service User-satisfaction Questionnaire. Revisions resulted in 61 items. Phase 2: the pilot 61-item Store-and-Forward Telemedicine Service User-satisfaction Questionnaire instrument was completed by 181 of 2179 invited HCPs. Forty-one mandatory items of the pilot Store-and-Forward Telemedicine Service User-satisfaction Questionnaire rated on a 5-point Likert scale were included in psychometric analyses and resulted in six reliable scales: training, communication, organization policy and strategy, interaction platform, usage platform, and working conditions.

DISCUSSION

The Store-and-Forward Telemedicine Service User-satisfaction Questionnaire is a reliable and valid questionnaire for measuring HCPs' satisfaction with store-and-forward telemedicine services as part of a continuous quality improvement cycle. Reimbursement questions were excluded due to low response. As adoption of telemedicine may be impeded by financial compensation issues, this requires consideration in future telemedicine questionnaires. Store-and-Forward Telemedicine Service User-satisfaction Questionnaire including video consultation items is needed to monitor also synchronous services as these expanded in the coronavirus disease 2019 pandemic.

Review of the Factors Affecting Acceptance of AI-Infused Systems

OBJECTIVE

The study aimed to provide a comprehensive overview of the factors impacting technology adoption, to predict the acceptance of artificial intelligence (AI)-based technologies.

BACKGROUND

Although the acceptance of AI devices is usually defined by behavioural factors in theories of user acceptance, the effects of technical and human factors are often overlooked. However, research shows that user behaviour can vary depending on a system's technical characteristics and differences in users.

METHOD

A systematic review was conducted. A total of 85 peer-reviewed journal articles that met the inclusion criteria and provided information on the factors influencing the adoption of AI devices were selected for the analysis.

RESULTS

Research on the adoption of AI devices shows that users' attitudes, trust and perceptions about the technology can be improved by increasing transparency, compatibility, and reliability, and simplifying tasks. Moreover, technological factors are also important for reducing issues related to human factors (e.g. distrust, scepticism, inexperience) and supporting users with lower intention to use and lower trust in AI-infused systems.

CONCLUSION

As prior research has confirmed the interrelationship among factors with and without behaviour theories, this review suggests extending the technology acceptance model that integrates the factors studied in this review to define the acceptance of AI devices across different application areas. However, further research is needed to collect more data and validate the study's findings.

APPLICATION

A comprehensive overview of factors influencing the acceptance of AI devices could help researchers and practitioners evaluate user behaviour when adopting new technologies.

Convolutional neural network-based program to predict lymph node metastasis of non-small cell lung cancer using 18F-FDG PET

PURPOSE

To develop a convolutional neural network (CNN)-based program to analyze maximum intensity projection (MIP) images of 2-deoxy-2-[F-18]fluoro-D-glucose (FDG) positron emission tomography (PET) scans, aimed at predicting lymph node metastasis of non-small cell lung cancer (NSCLC), and to evaluate its effectiveness in providing diagnostic assistance to radiologists.

METHODS

We obtained PET images of NSCLC from public datasets, including those of 435 patients with available N-stage information, which were divided into a training set (n = 304) and a test set (n = 131). We generated 36 maximum intensity projection (MIP) images for each patient. A residual network (ResNet-50)-based CNN was trained using the MIP images of the training set to predict lymph node metastasis. Lymph node metastasis in the test set was predicted by the trained CNN as well as by seven radiologists twice: first without and second with CNN assistance. Diagnostic performance metrics, including accuracy and prediction error (the difference between the truth and the predictions), were calculated, and reading times were recorded.

RESULTS

In the test set, 67 (51%) patients exhibited lymph node metastases and the CNN yielded 0.748 predictive accuracy. With the assistance of the CNN, the prediction error was significantly reduced for six of the seven radiologists although the accuracy did not change significantly. The prediction time was significantly reduced for five of the seven radiologists with the median reduction ratio 38.0%.

CONCLUSION

The CNN-based program could potentially assist radiologists in predicting lymph node metastasis by increasing diagnostic confidence and reducing reading time without affecting diagnostic accuracy, at least in the limited situations using MIP images.

First test of the theory of reasoned goal pursuit: predicting physical activity

OBJECTIVE

This study applied the theory of reasoned goal pursuit (TRGP) in predicting physical activity among Australian undergraduate students, providing the first empirical test of the model.Methods: The research comprised an elicitation study (N = 25; MAge= 25.76, SDAge= 11.33, 20 female, 5 male) to identify readily accessible procurement and approval goal beliefs and behavioural, normative, and control beliefs; and, a two-wave prospective online survey study (N = 109; MAge = 21.88, SDAge = 7.04, 63 female, 46 male) to test the tenets of the TRGP in relation to meeting World Health Organization physical activity guidelines during the COVID-19 pandemic among first year university students.Results: A linear PLS-SEM model displayed good fit-to-data, predicting 38%, 74%, and 48% of the variance in motivation, intention, and physical activity, respectively. The model supported the majority of hypothesised pattern of effects among theory constructs; in particular, the proposition that beliefs corresponding to procurement and approval goals would be more consequential to people's motivation and, thus, their intentions and behaviour, than other behavioural and normative beliefs, respectively.Conclusions: Results lend support for the TRGP and sets the agenda for future research to systematically test the proposed direct, indirect, and moderation effects for different health behaviours, populations, and contexts.

Supplemental data for this article is available online at https://doi.org/10.1080/08870446.2022.2026946 .