The feasibility of a social media-based foot self-management education and support program for adults with diabetes: A partially randomized preference trial

Aim

To assess the feasibility of Diabetic Foot Care Group (DFCG), a social media-based self-management education and support intervention, for people with diabetes (PWD) empowerment in diabetes-related foot ulceration prevention.

Methods

A partially randomized preference trial was conducted among 32 PWD. DFCG was implemented through Facebook. Participants in the intervention group joined the DFCG in addition to their usual care, while the control group received usual care. Data were collected online using questionnaires on participants' DFCG acceptance, engagement and preliminary efficacy on nine diabetes foot care-related outcomes at baseline, one, and three months post-intervention.

Results

The participants' study intervention acceptability and engagement rates were 84.2% and 55.2%, respectively. DFCG efficacy rate compared to usual care was 88.9% to 22.2%. Three diabetes foot care-related outcomes increased significantly in the intervention group three-month post-intervention: foot self-care adherence (p = 0.001, ηp 2 = 0.35), preventive foot self-care practice (p = 0.002, ηp 2 = 0.33), and physical health status (p < 0.02, ηp 2 = 0.23).

Conclusion

DFCG is feasible and could effectively improve diabetes foot care-related outcomes.

Innovation

Social media is an innovative approach healthcare professionals could utilize to virtually support PWD in ongoing learning and engagement in optimal foot self-care activities.

Trial registration

ClinicalTrials.gov, Identifier: NCT04395521.

Peer-to-peer sharing in public health interventions: strategies when people share health-related personal information on social media

PURPOSE

As sharing on social media has become an integrated part of everyday life, health and public health actors have started to show interest in the potential of people's peer-to-peer sharing of health-related personal information (HRI) for health interventions. In this article we focus on how people make sense of sharing HRI on social media.

METHODS

Twenty-two people between the ages 40 and 60 who had taken part in a regional health intervention were interviewed. Using theories about social media sharing, we explore their understandings and negotiations about whether, how much, and how to share HRI and discuss the results in relation to peer-to-peer sharing as a strategy in interventions.

RESULTS

We identified three aspects that were perceived as particularly risky: loss of control, effects on identity, and affecting others negatively, along with strategies that were used to manage risks in practice: avoiding sharing, allocating, and embedding HRI.

CONCLUSIONS

By allocating and embedding HRI, people can unlock motivating affordances for health work. However, strategies to manage risks can also be counterproductive. For actors to provide equality in health promotion, initiatives that include social media sharing need to be mindful of the sometimes counterproductive effects this may have on people's engagement.

How do medical professionals make sense (or not) of AI? A social-media-based computational grounded theory study and an online survey

To investigate opinions and attitudes of medical professionals towards adopting AI-enabled healthcare technologies in their daily business, we used a mixed-methods approach. Study 1 employed a qualitative computational grounded theory approach analyzing 181 Reddit threads in the several subreddits of r/medicine. By utilizing an unsupervised machine learning clustering method, we identified three key themes: (1) consequences of AI, (2) physician-AI relationship, and (3) a proposed way forward. In particular Reddit posts related to the first two themes indicated that the medical professionals' fear of being replaced by AI and skepticism toward AI played a major role in the argumentations. Moreover, the results suggest that this fear is driven by little or moderate knowledge about AI. Posts related to the third theme focused on factual discussions about how AI and medicine have to be designed to become broadly adopted in health care. Study 2 quantitatively examined the relationship between the fear of AI, knowledge about AI, and medical professionals' intention to use AI-enabled technologies in more detail. Results based on a sample of 223 medical professionals who participated in the online survey revealed that the intention to use AI technologies increases with increasing knowledge about AI and that this effect is moderated by the fear of being replaced by AI.

Development and validation of the mobile adherence satisfaction scale (MASS) for medication adherence apps

OBJECTIVE

To develop and validate the Mobile Adherence Satisfaction Scale (MASS) for assessing user satisfaction with mobile health applications aimed to improve medication adherence.

METHODS

The study involved patients over 18 with asthma, hypertension, heart failure, or diabetes, who used the CareAide® app for six months. Scale development included a literature review, expert consultations, and patient interviews, initially identifying 129 items. These were refined to 27 using a two-round Delphi technique and grouped into six dimensions: user interface, perceived usability, system quality, service quality, feature satisfaction, and general satisfaction. A pilot study with 30 participants further refined the model, which was then validated with 135 participants using exploratory and confirmatory factor analyses in SPSS 29 and SmartPLS 4. Data were collected via self-administered questionnaires.

RESULTS

A total of 135 complete questionnaires were analysed. Respondents had an average age of 66.7 years (SD = 11.6) with 42.2 % male (n = 57) and 57.8 % female (n = 78). After removal of an item due to cross loading, exploratory factor analysis resulted six dimensions and 26 items with Kaiser-Meyer-Olkin measure of 0.837 and Bartlett's Test of Sphericity (χ2(n = 325) = 2085.673, P < 0.001). The confirmatory factor analysis confirmed high reliability and validity: Cronbach's alpha values > 0.70 for each dimension and an overall alpha of 0.89, with Composite Reliability and Average Variance Extracted both >0.70 and >0.50, respectively, for each dimension. Structural model indicated a significant positive impact of user interface (β = 0.226, P = 0.006) and feature satisfaction (β = 0.230, P = 0.002) on general satisfaction, explaining 23.1 % of the variance (R2 = 0.231).

CONCLUSION

The study developed and validated the MASS, a reliable tool for assessing user satisfaction with mHealth apps. User interface design and feature satisfaction are key for long-term engagement and consistent medication adherence.

Technostress in Nuclear Medicine: A Qualitative Study of Causes, Mitigators, and Resolution Levels

BACKGROUND

In contemporary healthcare, information and communication technology enables specialized treatment and efficient information sharing. However, it also causes stress and frustration, so-called technostress, among healthcare staff.

PURPOSE

To investigate the day-to-day occurrence of technostress, we ask the research question: What causes the stressful situations with technology, how are they mitigated, and to what extent are they resolved?

METHOD

We interviewed 15 healthcare providers in the department of nuclear medicine at a Danish hospital about their experiences with technology-induced stress in their daily work.

RESULTS

The interviewees described 185 stressful situations with technology, mostly technology indispensable to their work. The two most frequent causes of stressful situations are system performance (46%) and technology-related organizational procedures (18%). To mitigate the situations, the most frequent strategies are accommodating (51%), consulting others for help (18%), and repeating previous task steps (13%). The mitigation strategies indicate that the stressful situations involve adapting work practices to the technology to a much larger extent than succeeding in adapting the technology to the work. Regarding the level of resolution, as much as 66% of the stressful situations are merely solved for now, that is, the concrete situation is resolved but the underlying issue remains unsolved. The underlying issue is resolved in only 10% of the situations, thereby indicating that the vast majority of the stressful situations are likely to recur later.

CONCLUSION

The staff at the studied hospital department repeatedly experience stressful situations with the technology they rely on in their work. This technostress is an extra stressor on top of those induced by the staff's responsibility for providing quality patient treatment. At the individual level, technostress leads to frustration and possibly burnout; at the organizational level, it calls for preventive action.

Antecedents and consequents of circular economy adoption: A meta-Analytic Investigation

This paper aims to integrate and empirically assess the antecedents and consequents of circular economy (CE) adoption to remove ambiguity existing in the literature and clarify divergent views. This study uses meta-analysis methodology to validate the research framework, considering 106 empirical studies with 210 effect sizes. Based on these studies, we establish twelve antecedents and three consequents related to CE. Antecedents are categorized in a technological-organizational-environmental framework and consequents in the sustainability outcomes. The result suggests that organisational factors are more prominent in driving CE practices, followed by environmental and technological factors. In the organisational category, the three most influencing factors are managing product returns, green manufacturing, and environmental strategy. In the environmental category, coercive pressure is the most influential factor, followed by mimetic and social pressures. Emerging I4.0 technologies are the most prominent factor in the technological category. Our study suggests that CE helps to achieve sustainable performance by significantly enabling economic, environmental, and social outcomes. This study further analyses how contextual factors such as national culture (masculinity) and economic regions influence the various relationships with CE using subgroup analysis. The moderation results show that low masculine culture and developing economies are more effectively using the I4.0 technologies to drive CE adoption than high masculine culture and developed economies. Additionally, different dimensions of sustainability are also influenced by the variations in masculinity and economic regions.

Proposal of a Knowledge Management Model for Complex Systems: Case of the Supervision and Control Subsystem of the Colombian Health System

BACKGROUND

Considering regulatory, supervision, and control health policy, an innovative knowledge management model is proposed for the Colombian health system, which is recognized as a complex system.

METHODS

A model is constructed through a comparative analysis of various theoretical and conceptual frameworks, and an original methodology is proposed based on an analysis of the macroprocesses of the Supervision and Control System (SSC) of the Colombian General Social Security System in Health (SGSSS). After formulating hypotheses and conceptual references, information errors are determined within the different macroprocesses of the SGSSS, including those of governance and the SSC.

RESULTS

The risks of generating duplicate, wrong, hidden, or non-existent information arise when the associated regulations need more specificity to be applied in all cases, thus leading to the risk of different interpretations by some actors. In this way, it is possible to hinder the generation of unified information, as there is no clarity as to who is responsible for the generation or creation of certain data.

CONCLUSIONS

The proposed model is characterized by its flexibility and adaptability, integrating several processes that can be executed simultaneously or cyclically (depending on the system's needs) and allowing for the generation and feedback of knowledge at different stages, with some processes simultaneously executed to complement each other.

The roles of phishing knowledge, cue utilization, and decision styles in phishing email detection

This study investigated the roles of phishing knowledge, cue utilization, and decision styles in contributing to phishing email detection. Participants (N = 145) completed an online email sorting task, and measures of phishing knowledge, email decision styles, cue utilization, and email security awareness. Cue utilization was the only factor that uniquely predicted the capacity to discriminate phishing from genuine emails. Phishing knowledge was associated with greater phishing detection and a bias towards classifying all emails as phishing. A preference for intuitive decision making predicted lower detection of phishing emails, driven by a greater tendency to classify emails as genuine. These findings support the proposition that cue utilization is a distinct cognitive process that enables expert performance. The outcomes indicate that, in addition to increasing phishing knowledge and developing safe behavioral patterns, anti-phishing training needs to provide opportunities for trainees to develop meaningful cue associations.

Social Media Use And Well-Being: A Systematic Review And Meta-Analysis

Prior research has investigated the link between social media use (SMU) and negative well-being. However, the connection with positive well-being has not been extensively studied, leading to a situation where there are inconsistent and inconclusive findings. This study fills this gap by examining the correlation between excessive and problematic SMU and subjective as well as psychological well-being (PWB). We conducted a systematic search across databases such as PubMed, Scopus, and Web of Science, and gray literature sources such as Research Gate and ProQuest, yielding 51 relevant studies for meta-analysis, encompassing a sample size of 680,506 individuals. Employing the Newcastle-Ottawa Scale, we assessed study quality, whereas statistical analysis was executed using R Studio. Excessive SMU showed no significant association with subjective (ES = 0.003, 95% confidence interval [95% CI]: -0.08, 0.09; p = 0.94, I2 = 95.8%, k =16) and PWB (ES = 0.16, 95% CI: -0.15, 0.45; p = 0.26, I2 = 98%, k = 7). Conversely, problematic SMU showed a negative correlation with subjective (ES = -0.14, 95% CI: -0.20, -0.09; p = 0.00, I2 = 93.3%, k = 25) and PWB (ES = -0.19, 95% CI: -0.31, -0.06; p = 0.01, I2 = 95%, k = 5), with two outliers removed. No publication bias was detected. Subgroup analysis highlighted effects of "sampling method" (p < 0.05), "study quality" (p < 0.05), "developmental status" (p < 0.05), "forms of social media" (p < 0.05), and "type of population" (p < 0.01) on the estimated pooled effect sizes. Although univariate meta-regression showed the effects of "% of Internet users" (p < 0.05) and "male%" (p < 0.05), and multivariate meta-regression showed the combined effect of moderators only on the relationship between problematic SMU and subjective well-being.

How users make online privacy decisions in work and personal contexts of use

With the rising usage of contactless work options since COVID-19, users increasingly share their personal data in digital tools at work. Using an experimental online vignette study (N = 93), we examined users' willingness to use a video conferencing tool, while systematically varying the context of use (personal vs. low trustworthiness work vs. high trustworthiness work) and the type of information shared (low vs. medium vs. high sensitivity). We also assessed users' perceived responsibility in work and personal contexts of use and their self-assessed digital competence. Our results highlight employer trustworthiness as an important factor in the willingness to use a third-party video conferencing tool, with increased willingness to use these tools in work contexts of use with high trustworthiness compared to those with low trustworthiness. This effect seems to be reduced when the data to be shared is of high sensitivity, compared to medium and low sensitivity data. Furthermore, despite reduced responsibility for data protection in work compared to personal contexts of use, the willingness to use a video conferencing tool did not decrease between trustworthy work and personal contexts of use. We discuss our findings and their methodological implications for future research and derive implications for privacy decisions at work.

Sociotechnical Cross-Country Analysis of Contextual Factors That Impact Patients' Access to Electronic Health Records in 4 European Countries: Framework Evaluation Study

BACKGROUND

The NORDeHEALTH project studies patient-accessible electronic health records (PAEHRs) in Estonia, Finland, Norway, and Sweden. Such country comparisons require an analysis of the sociotechnical context of these services. Although sociotechnical analyses of PAEHR services have been carried out in the past, a framework specifically tailored to in-depth cross-country analysis has not been developed.

OBJECTIVE

This study aims to develop and evaluate a method for a sociotechnical analysis of PAEHRs that advances a framework for sociotechnical analysis of eHealth solutions first presented by Sittig and Singh. This first article in a series presents the development of the method and a cross-country comparison of the contextual factors that enable PAEHR access and use.

METHODS

The dimensions of the framework for sociotechnical analysis were thoroughly discussed and extended in a series of workshops with international stakeholders, all being eHealth researchers focusing on PAEHRs. All countries were represented in the working group to make sure that important national perspectives were covered. A spreadsheet with relevant questions related to the studied services and the various dimensions of the sociotechnical framework was constructed and distributed to the 4 participating countries, and the project participants researched various national sources to provide the relevant data for the comparisons in the 10 sociotechnical dimensions.

RESULTS

In total, 3 dimensions were added to the methodology of Sittig and Singh to separate clinical content from features and functions of PAEHRs and demonstrate basic characteristics of the different countries regarding national and regional steering of health care and information and communications technology developments. The final framework contained the following dimensions: metadata; hardware and software computing infrastructure; features and functions; clinical content shared with patients; human-computer interface; people; workflow and communication; the health care organization's internal policies, procedures, and culture; national rules, regulations, and incentives; system measurement and monitoring; and health care system context. The dimensions added during the study mostly concerned background information needed for cross-country comparisons in particular. Several similarities were identified among the compared countries, especially regarding hardware and software computing infrastructure. All countries had, for example, one national access point, and patients are provided a PAEHR automatically. Most of the differences could be identified in the health care system context dimension. One important difference concerned the governing of information and communications technology development, where different levels (state, region, and municipality) were responsible in different countries.

CONCLUSIONS

This is the first large-scale international sociotechnical analysis of services for patients to access their electronic health records; this study compared services in Estonia, Finland, Norway, and Sweden. A methodology for such an analysis was developed and is presented to enable comparison studies in other national contexts to enable future implementations and evaluations of PAEHRs.

A local community on a global collective intelligence platform: A case study of individual preferences and collective bias in ecological citizen science

The collective intelligence of crowds could potentially be harnessed to address global challenges, such as biodiversity loss and species' extinction. For wisdom to emerge from the crowd, certain conditions are required. Importantly, the crowd should be diverse and people's contributions should be independent of one another. Here we investigate a global citizen-science platform-iNaturalist-on which citizens report on wildlife observations, collectively producing maps of species' spatiotemporal distribution. The organization of global platforms such as iNaturalist around local projects compromises the assumption of diversity and independence, and thus raises concerns regarding the quality of such collectively-generated data. We spent four years closely immersing ourselves in a local community of citizen scientists who reported their wildlife sightings on iNaturalist. Our ethnographic study involved the use of questionnaires, interviews, and analysis of archival materials. Our analysis revealed observers' nuanced considerations as they chose where, when, and what type of species to monitor, and which observations to report. Following a thematic analysis of the data, we organized observers' preferences and constraints into four main categories: recordability, community value, personal preferences, and convenience. We show that while some individual partialities can "cancel each other out", others are commonly shared among members of the community, potentially biasing the aggregate database of observations. Our discussion draws attention to the way in which widely-shared individual preferences might manifest as spatial, temporal, and crucially, taxonomic biases in the collectively-created database. We offer avenues for continued research that will help better understand-and tackle-individual preferences, with the goal of attenuating collective bias in data, and facilitating the generation of reliable state-of-nature reports. Finally, we offer insights into the broader literature on biases in collective intelligence systems.

Don't believe them! Reducing misinformation influence through source discreditation

Misinformation often continues to influence people's reasoning even after it has been corrected. Therefore, an important aim of applied cognition research is to identify effective measures to counter misinformation. One frequently recommended but hitherto insufficiently tested strategy is source discreditation, that is, attacking the credibility of a misinformation source. In two experiments, we tested whether immediate source discreditation could reduce people's subsequent reliance on fictional event-related misinformation. In Experiment 1, the discreditation targeted a person source of misinformation, pointing to a conflict of interest. This intervention was compared with a commonly employed message-focused correction and a combination of correction and discreditation. The discreditation alone was effective, but less effective than a correction, with the combination of both most effective. Experiment 2 compared discreditations that targeted a person versus a media source of misinformation, pointing either to a conflict of interest or a poor track record of communication. Discreditations were effective for both types of sources, although track-record discreditations were less effective when the misinformation source was a media outlet compared to a person. Results demonstrate that continued influence of misinformation is shaped by social as well as cognitive factors and that source discreditation is a broadly applicable misinformation countermeasure.

Validation of a quantitative instrument measuring critical success factors and acceptance of Casemix system implementation in the total hospital information system in Malaysia

OBJECTIVES

This study aims to address the significant knowledge gap in the literature on the implementation of Casemix system in total hospital information systems (THIS). The research focuses on validating a quantitative instrument to assess medical doctors' acceptance of the Casemix system in Ministry of Health (MOH) Malaysia facilities using THIS.

DESIGNS

A sequential explanatory mixed-methods study was conducted, starting with a cross-sectional quantitative phase using a self-administered online questionnaire that adapted previous instruments to the current setting based on Human, Organisation, Technology-Fit and Technology Acceptance Model frameworks, followed by a qualitative phase using in-depth interviews. However, this article explicitly emphasises the quantitative phase.

SETTING

The study was conducted in five MOH hospitals with THIS technology from five zones.

PARTICIPANTS

Prior to the quantitative field study, rigorous procedures including content, criterion and face validation, translation, pilot testing and exploratory factor analysis (EFA) were undertaken, resulting in a refined questionnaire consisting of 41 items. Confirmatory factor analysis (CFA) was then performed on data collected from 343 respondents selected via stratified random sampling to validate the measurement model.

RESULTS

The study found satisfactory Kaiser-Meyer-Olkin model levels, significant Bartlett's test of sphericity, satisfactory factor loadings (>0.6) and high internal reliability for each item. One item was eliminated during EFA, and organisational characteristics construct was refined into two components. The study confirms unidimensionality, construct validity, convergent validity, discriminant validity and composite reliability through CFA. After the instrument's validity, reliability and normality have been established, the questionnaire is validated and deemed operational.

CONCLUSION

By elucidating critical success factor and acceptance of Casemix, this research informs strategies for enhancing its implementation within the THIS environment. Moving forward, the validated instrument will serve as a valuable tool in future research endeavours aimed at evaluating the adoption of the Casemix system within THIS, addressing a notable gap in current literature.

Analyzing the diffusion of feminist discourses on Chinese social media: A case study of the 2022 Tangshan restaurant attack

Network platforms have ushered in a novel propagation model for feminist discourses. The emergence of oriental feminism in society has led to gender-based public opinions surrounding public events becoming a trending topic on Chinese social media. This study uses the 2022 Tangshan restaurant attack as a case study, an incident that sparked widespread discussions across China in 2022. The research gathered 366,602 network communication nodes within a week and examined the communication networks of three types of content nodes (information, opinion, and appeasement) using the complex network modeling method. The findings revealed that all three types of information communication networks exhibit an apparent scale-free characteristic, and the "key minority" of nodes significantly affects information communication. Information-type and appeasement-type Weibo display notable similarities in the quantity and degree distribution of nodes within the communication networks and in the information decay rate. Moreover, authoritative information issuers have become the primary catalyst for information propagation. Conversely, opinion-type Weibo has the widest communication network diameter and features a high degree of participation, multilevel propagation, and a slow decay rate. This indicates that the interaction between opinion leaders and netizens has enhanced the depth and breadth of information diffusion for opinion-type Weibo.

How Does an Online Mental Health Community on Twitter Empower Diverse Population Levels and Groups? A Qualitative Analysis of #BipolarClub

BACKGROUND

Social media, including online health communities (OHCs), are widely used among both healthy people and those with health conditions. Platforms like Twitter (recently renamed X) have become powerful tools for online mental health communities (OMHCs), enabling users to exchange information, express feelings, and socialize. Recognized as empowering processes, these activities could empower mental health consumers, their families and friends, and society. However, it remains unclear how OMHCs empower diverse population levels and groups.

OBJECTIVE

This study aimed to develop an understanding of how empowerment processes are conducted within OMHCs on Twitter by identifying members who shape these communities, detecting the types of empowerment processes aligned with the population levels and groups outlined in Strategy 1 of the Integrated People-Centred Health Services (IPCHS) framework by the World Health Organization (WHO), and clarifying members' involvement tendencies in these processes.

METHODS

We conducted our analysis on a Twitter OMHC called #bipolarclub. We captured 2068 original tweets using its hashtag #bipolarclub between December 19, 2022, and January 15, 2023. After screening, 547 eligible tweets by 182 authors were analyzed. Using qualitative content analysis, community members were classified by examining the 182 authors' Twitter profiles, and empowerment processes were identified by analyzing the 547 tweets and categorized according to the WHO's Strategy 1. Members' tendencies of involvement were examined through their contributions to the identified processes.

RESULTS

The analysis of #bipolarclub community members unveiled 5 main classifications among the 182 members, with the majority classified as individual members (n=138, 75.8%), followed by health care-related members (n=39, 21.4%). All members declared that they experience mental health conditions, including mental health and general practitioner members, who used the community as consumers and peers rather than for professional services. The analysis of 547 tweets for empowerment processes revealed 3 categories: individual-level processes (6 processes and 2 subprocesses), informal carer processes (1 process for families and 1 process for friends), and society-level processes (1 process and 2 subprocesses). The analysis also demonstrated distinct involvement tendencies among members, influenced by their identities, with individual members engaging in self-expression and family awareness support and health care-related members supporting societal awareness.

CONCLUSIONS

The examination of the #bipolarclub community highlights the capability of Twitter-based OMHCs to empower mental health consumers (including those from underserved and marginalized populations), their families and friends, and society, aligning with the WHO's empowerment agenda. This underscores the potential benefits of leveraging Twitter for such objectives. This pioneering study is the very first to analyze how a single OMHC can empower diverse populations, offering various health care stakeholders valuable guidance and aiding them in developing consumer-oriented empowerment programs using such OMHCs. We also propose a structured framework that classifies empowerment processes in OMHCs, inspired by the WHO's Strategy 1 (IPCHS framework).

The path to cashless transaction: A study of user intention and attitudes towards quick response mobile payments

This study explores the factors influencing users' behavioral intentions, attitudes and actual adoption of quick response (QR) mobile payment in the least developed country (LDC) of Bangladesh, by extending the original unified theory of acceptance and use of technology (UTAUT) model. The study conducts a mixed-methods investigation by combining the partial least squares (PLS) and focus group discussion (FGD) methods to empirically evaluate the research model and cross-validate the findings. Using purposive sampling, data were gathered from 412 respondents, followed by 10 respondents who took part in the FGD, who all met the sample criteria. The study findings indicate that performance expectancy, effort expectancy, and social influence significantly positively influence users' behavioral intention, while self-concept, perceived self-efficacy, and habit substantially influence their attitudes towards using QR mobile payments. The findings also confirm a positive effect of users' attitudes toward using QR mobile payment on both behavioral intention and actual use, and a positive effect of behavioral intention on the actual use of QR mobile payments. These findings offer several important theoretical and managerial implications.

Fundraiser engagement, third-party endorsement and crowdfunding performance: A configurational theory approach

Reward-based crowdfunding is a typical two-sided platform (fundraiser side and backer side) with high information asymmetry. While existing research indicates that signals from fundraisers and backers can impact crowdfunding performance, the interplay among these signals warrants further investigation. Drawing on signaling theory, this study adopts a configurational perspective and utilizes the fsQCA method and linear regression to investigate the combined effects of fundraiser engagement (update and fundraiser comment), third-party endorsement (backer comment and Facebook sharing), and project preparedness (video, image, and description) on crowdfunding performance. Drawing data from the reward-based crowdfunding platform Indiegogo, this research pointed out that these signals cannot generate better crowdfunding performance alone and examined substitution and complementary effects among different signals. Based on the linear regression and fsQCA results, configurations that lead to high crowdfunding performance are identified. We found that project preparedness must work with other signals to produce high crowdfunding performance. Besides, we summarized these configurations into two patterns that may lead to high crowdfunding performance: a fundraiser engagement-driven pattern and a third-party endorsement-driven pattern. This study contributes a configurational perspective and valuable insights into how signals can work together to mitigate information asymmetry in crowdfunding.

Geographic information systems adoption model: A partial least square-structural equation modeling analysis approach

The ability of Geographic Information System (GIS) to organize, analyze, visualize and integrate spatial data has been at the top of its primary uses among professional industries. However, considering the extensive adoption of Information System (IS) throughout history for government organizations' or citizens' disaster response, the implementation of geographical elements is still minimal. Previous GIS models and framework studies, particularly in developing countries, were affected by pandemic pressure, competitiveness pressure, change management, and security factors. Thus, this study aims to develop a model for the successful adoption of GIS using the Technology Acceptance Model (TAM), and De Lone and Mc Lean Information Success Model and analyze the applicability of the existing factors to enhance the performance of Public Sector Organizations (PSOs). From the study, a new conceptual framework was proposed to examine the effects of factors on GIS adoption that impact performance among PSOs from the perspective of Saudi Arabia. Quantitative methods were used to collect data through a questionnaire distributed to 350 respondents from PSO, and only 272 were found to be valid. Partial Least Square Structural Equation Modeling (PLS-SEM) validated the GIS model. The finding revealed that system quality, service quality, change management, competitiveness pressure, perceived ease of use, perceived usefulness, and security factors significantly and positively affected GIS adoption. The study also showed that GIS adoption substantially affected PSO performance. The proposed model provides insight into how GIS adoption can eventually enhance performance among PSOs. In essence, the study contributes to the running of PSO and the decisions taken by policymakers.

Information consumption city and carbon emission efficiency: Evidence from China's quasi-natural experiment

The transformation of public consumption patterns has become a burning question, but there are few studies on public consumption patterns. Therefore, evaluating the impact of Information consumption city (ICC) policy on carbon emission efficiency holds significant implications. This study settles on 104 pilot cities in China from 2006 to 2020 to assess the impact and the response mechanism of ICC policy on carbon emission efficiency through the time-vary Difference-in-Difference (DID) model. The result shows that: (1) ICC policy significantly promotes the local carbon emission efficiency, which remains robust after a battery of sensitivity tests. (2) It improves carbon emission efficiency through production factors agglomeration effect, industrial structural changing effect, innovation promotion effect, and environmental attention effect; (3) The direct impact of ICC policy on carbon emission efficiency varies across regions with different information consumption and carbon emission base. (4) ICC can improve carbon emission efficiency through the joint implementation of smart city (SC), new urbanization (NU), ecological civilization city construction (EC), Belt and Road Initiative (BR), Broadband China (BC), low-carbon city pilot policy (LCC), and air quality standards (AQS) policy.

Improving realty management ability based on big data and artificial intelligence decision-making

Realty management relies on data from previous successful and failed purchase and utilization outcomes. The cumulative data at different stages are used to improve utilization efficacy. The vital problem is selecting data for analyzing the value incremental sequence and profitable utilization. This article proposes a knowledge-dependent data processing scheme (KDPS) to augment precise data analysis. This scheme operates on two levels. Data selection based on previous stagnant outcomes is performed in the first level. Different data processing is performed in the second level to mend the first level's flaws. Data processing uses knowledge acquired from the sales process, amenities, and market value. Based on the knowledge determined from successful realty sales and incremental features, further processing for new improvements and existing stagnancy mitigation is recommended. The stagnancy and realty values are used as knowledge for training the data processing system. This ensures definite profitable features meeting the amenity requirements under reduced stagnancy time. The proposed scheme improves the processing rate, stagnancy detection, success rate, and training ratio by 8.2%, 10.25%, 10.28%, and 7%, respectively. It reduces the processing time by 8.56% compared to the existing methods.

Machine learning applications in preventive healthcare: A systematic literature review on predictive analytics of disease comorbidity from multiple perspectives

Artificial intelligence is constantly revolutionizing biomedical research and healthcare management. Disease comorbidity is a major threat to the quality of life for susceptible groups, especially middle-aged and elderly patients. The presence of multiple chronic diseases makes precision diagnosis challenging to realize and imposes a heavy burden on the healthcare system and economy. Given an enormous amount of accumulated health data, machine learning techniques show their capability in handling this puzzle. The present study conducts a review to uncover current research efforts in applying these methods to understanding comorbidity mechanisms and making clinical predictions considering these complex patterns. A descriptive metadata analysis of 791 unique publications aims to capture the overall research progression between January 2012 and June 2023. To delve into comorbidity-focused research, 61 of these scientific papers are systematically assessed. Four predictive analytics of tasks are detected: disease comorbidity data extraction, clustering, network, and risk prediction. It is observed that some machine learning-driven applications address inherent data deficiencies in healthcare datasets and provide a model interpretation that identifies significant risk factors of comorbidity development. Based on insights, both technical and practical, gained from relevant literature, this study intends to guide future interests in comorbidity research and draw conclusions about chronic disease prevention and diagnosis with managerial implications.

Enhancing Digital Health Engagement in Chronic Disease: The Role of Doctor-Patient Communication and Media Use in Acceptance of Digital Health Care Among End-Stage Kidney Disease Patients

This study examines digital health challenges among end-stage kidney disease (ESKD) patients, a population characterized by older age, lower socioeconomic status, and limited access to modern technologies. Drawing from the Mere Exposure Effect, the Technology Acceptance Model, and insights from doctor-patient communication literature, our study implemented a month-long intervention across three distinct groups. The Digital Media Exposure Group watched doctor-recommended videos on YouTube using a tablet PC twice weekly for four weeks. The Digital Media Exposure with Doctor-Patient Communication Group engaged in physician-led discussions about the viewed content during their medical visits in addition to the activities in the first group. The Control Group received printed medical information that mirrored the content of the videos. Participants in this study, all of whom were diagnosed with ESKD, were recruited from a university hospital in South Korea (n = 88, Mage = 64.8). Their perceptions, attitudes, and behavioral intentions regarding digital health care were measured and compared between groups. The results unveiled significant group differences [Wilk's Λ = 0.829, F(8, 164) = 2.02, p = 0.047, partial η2 = 0.090], with variations in attitudes, perceived ease of use, and intentions among groups, and effect sizes ranging from 0.069 to 0.096.These findings underscore the importance of tailored interventions to address digital health disparities, particularly among underserved demographic groups. Strategies that prioritize user-friendly interfaces and clear communication between doctors and patients are advocated to promote digital health engagement, ensuring equitable access and improved outcomes for patients with chronic disease.

High-performing teams: Is collective intelligence the answer?

BACKGROUND/OBJECTIVES

The concept of a general factor of collective intelligence, proposed by Woolley et al. in 2010, has spurred interest in understanding collective intelligence within small groups. This study aims to extend this investigation by examining the validity of a general collective intelligence factor, assessing its underlying factor structure, and evaluating its utility in predicting performance on future group problem-solving tasks and academic outcomes.

METHODS

Employing a correlational study design, we engaged 85 university students in a series of complex cognitive tasks designed to measure collective intelligence through individual, group, and predictive phases.

RESULTS

Contrary to the hypothesized single-factor model, our findings favor a two-factor model influenced by Cattell's theory of crystalized and fluid intelligence. These two factors accounted for substantial variance in group performance outcomes, challenging the prevailing single-factor model. Notably, the predictive validity of these factors on group assignments was statistically significant, with both individual and collective intelligence measures correlating moderately with group assignment scores (rs = .40 to .47, p < .05).

CONCLUSIONS

Our research suggests that collective intelligence in small group settings may not be uniformly governed by a single factor but rather by multiple dimensions that reflect established theories of individual intelligence. This nuanced understanding of collective intelligence could have significant implications for enhancing group performance in both educational and organizational contexts. Future research should explore these dimensions and their independent contributions to group dynamics and outcomes.

Do patients prefer a human doctor, artificial intelligence, or a blend, and is this preference dependent on medical discipline? Empirical evidence and implications for medical practice

Today the doctor-patient relationship typically takes place in a face-to-face setting. However, with the advent of artificial intelligence (AI) systems, two further interaction scenarios are possible: an AI system supports the doctor's decision regarding diagnosis and/or treatment while interacting with the patient, or an AI system could even substitute the doctor and hence a patient interacts with a chatbot (i.e., a machine) alone. Against this background, we report on an online experiment in which we analyzed data from N = 1,183 people. The data was collected in German-speaking countries (Germany, Austria, Switzerland). The participants were asked to imagine they had been suffering from medical conditions of unknown origin for some time and that they were therefore visiting a health center to seek advice from a doctor. We developed descriptions of patient-doctor interactions (referred to as vignettes), thereby manipulating the patient's interaction partner: (i) human doctor, (ii) human doctor with an AI system, and (iii) an AI system only (i.e., chatbot). Furthermore, we manipulated medical discipline: (i) cardiology, (ii) orthopedics, (iii) dermatology, and (iv) psychiatry. Based on this 3 × 4 experimental within-subjects design, our results indicate that people prefer a human doctor, followed by a human doctor with an AI system, and an AI system alone came in last place. Specifically, based on these 12 hypothetical interaction situations, we found a significant main effect of a patient's interaction partner on trust, distrust, perceived privacy invasion, information disclosure, treatment adherence, and satisfaction. Moreover, perceptions of trust, distrust, and privacy invasion predicted information disclosure, treatment adherence, and satisfaction as a function of interaction partner and medical discipline. We found that the situation in psychiatry is different from the other three disciplines. Specifically, the six outcome variables differed strongly between psychiatry and the three other disciplines in the "human doctor with an AI system" condition, while this effect was not that strong in the other conditions (human doctor, chatbot). These findings have important implications for the use of AI in medical care and in the interaction between patients and their doctors.

An integration of expectation confirmation model and information systems success model to explore the factors affecting the continuous intention to utilise virtual classrooms

Virtual classrooms have recently gained significant consideration in educational institutes and universities due to their potential to encourage and support students' learning activities. Although recent research has focused extensively on online learning, virtual classrooms and the factors affecting their continuous use have garnered little attention, especially in Arab Gulf countries such as Saudi Arabia. Thus, this study integrates the expectation confirmation model and the information systems success model to assess the factors affecting students' continuous intention to utilise virtual classrooms in higher education. We examined the effects of information quality, service quality, system quality, confirmation, perceived usefulness, and satisfaction on the continuous intention to utilise virtual classrooms. Data were collected from 441 students and analysed using structural equation modelling "SEM". SEM is a powerful multivariate approach used increasingly in empirical investigation for evaluating and testing casual relationships. The results revealed that the proposed model demonstrated high explanatory power in explaining students' continuous intention to utilise virtual classrooms (R2 = 0. 86). Additionally, information quality had a significant effect on confirmation and an insignificant effect on perceived usefulness. System quality affected perceived usefulness and confirmation. Contrary to our expectations, service quality had a significant negative effect on perceived usefulness and confirmation. Additionally, perceived usefulness and confirmation affected students' satisfaction with using virtual classrooms, and satisfaction affected students' continuous intention to utilise virtual classrooms. This study contributes to the literature by offering a holistic integrated model that increases the understanding of the factors influencing students' continuous intention to utilise virtual classrooms, hence aiding in increasing their utilisation. Furthermore, it provides practical implications for enhancing students' continuous intention to utilise virtual classrooms. Virtual classroom developers must focus on improving the system quality of virtual classrooms. According to our results, higher system quality led the students to perceive virtual classrooms as useful and confirmed their favourable experiences with virtual classrooms. Additionally, providing students with high information quality in virtual classrooms would enhance their confirmation experiences, leading to the continuous intention to utilise virtual classrooms.

The impact of directors' information technology experience on the financing constraints of internet start-ups-Data mining with python on the China's New OTC market

In the era of Internet information technology, the IT background of directors plays a significant role in corporate governance. However, existing research lacks sufficient discussion on the financing constraints faced by Internet startups. This paper examines Internet entrepreneurial enterprises listed on the New Third Board as the research sample. Using content analysis coding methods and Python software for text mining, the study empirically analyzes the impact of directors' IT backgrounds and media reports on the financing constraints of these enterprises. The results indicate the following: First, directors with IT backgrounds help reduce the financing constraints of Internet startups. The higher the directors' proficiency in information technology, the more favorable it is for obtaining financing. Second, directors with IT backgrounds have a significant positive impact on the tone of media reports. Third, the tone of media reports reflects the spirit and development of the enterprises, serving as a mediating factor between directors' IT backgrounds and the financing constraints of the enterprises. The findings of this study are valuable for guiding Internet startups to better leverage the IT expertise of their directors. Additionally, they provide useful insights for reducing the financing constraints faced by these startups.

The Extent to Which Technostress Is Related to Employees' Work-Life Fit: A Multilevel Meta-Analysis

BACKGROUND

This multilevel meta-analytic review is designed to analyze and quantify the effect size of the association between technostress and work-life fit among employees.

METHOD

By adhering to the PRISMA 2020 Statement, seven databases (i.e., PubMed, PsycARTICLES, PsycINFO, the Psychology and Behavioral Sciences Collection, MEDLINE, Wiley Online Library, and Web of Science) were searched for studies reporting the association between technostress and work-life fit.

RESULTS

Out of the 476 articles that were screened, 7 met the established criteria and were subsequently included in this multilevel meta-analytic review. A majority of these studies highlighted the existence and intensity of the association between technostress and work-life fit, as evidenced by multiple Pearson's r. Our findings supported a medium association (Cohen's d = -0.41). Noteworthy differences were found when considering the interdependence of effect sizes within and between studies.

CONCLUSION

The results of this multilevel meta-analytic review underscore the significance of understanding this association to inform optimal choices in terms of welfare policies and organizational decisions that promote employee well-being. This knowledge may serve as a scientific foundation for viewing new technologies not merely as hurdles but also as potential resources.

A methodology for planning, implementation and evaluation of skills intelligence management - results of a design science project in technology organisations

Introduction

The evolving labour market requirements amidst digital transformation necessitate robust skills intelligence for informed decision-making and adaptability. Novel technologies such as Big Data, Machine Learning, and Artificial Intelligence have significant potential for enhancing skills intelligence.

Methods

This study bridges the gap between theory and practice by designing a novel software artefact for skills intelligence management. With its systematic framework for identifying skills intelligence elements, an assessment instrument, and an implementation methodology, the artefact ensures a thorough approach to skills intelligence management.

Results

The artefact was demonstrated in 11 organisations. Feedback collected from interviews, focus group sessions, and observations (N = 19) indicated that the artefact is a feasible starting point for implementing or systematising skills intelligence management. Participants suggested improvements but concurred that the systematic approach enhances skills intelligence data collection and quality.

Discussion

The study shows that the artefact facilitates the application of advanced technologies in skills intelligence management. Additionally, it contributes a set of principles for effective skills intelligence management, fostering a broader conversation on this critical topic. Participants' feedback underscores the artefact's potential and provides a basis for further refinement and application in diverse organisational contexts.

A decade of change towards Value-Based Health Care at a Dutch University Hospital: a complexity-informed process study

BACKGROUND

While healthcare organizations in several countries are embracing Value-Based Health Care (VBHC), there are limited insights into how to achieve this paradigm shift. This study examines the decade-long (2012-2023) change towards VBHC in a pioneering Dutch university hospital.

METHOD

Through retrospective, complexity-informed process research, we study how a Dutch university hospital's strategy to implement VBHC evolved, how implementation outcomes unfolded, and the underlying logic behind these developments. Data include the hospital's internal documents (n = 10,536), implementation outcome indicators (n = 4), a survey among clinicians (n = 47), and interviews with individuals contributing to VBHC at the hospital level (n = 20).

RESULTS

The change towards VBHC is characterized by three sequential strategies. Initially, the focus was on deep change through local, tailored implementation of multiple VBHC elements. The strategy then transitioned to a hospital-wide program aimed at evolutionary change on a large scale, emphasizing the integration of VBHC into mainstream IT and policies. Recognizing the advantages and limitations of both strategies, the hospital currently adopts a "hybrid" strategy. This strategy delicately combines deep and broad change efforts. The strategy evolved based on accumulated insights, contextual developments and shifts in decision-makers. The complexity of change was downplayed in plans and stakeholder communication. By the end of 2023, 68 (sub)departments engaged in VBHC, enabled to discuss patients' responses to Patient Reported Outcomes Measures (PROMs) during outpatient care. However, clinicians' use of PROMs data showed limitations. While pioneers delved deeper into VBHC, laggards have yet to initiate it.

CONCLUSIONS

VBHC does not lend itself to linear planning and is not easily scalable. While there appears to be no golden standard for implementation, blending local and larger-scale actions appears advantageous. Local, deep yet harmonized and system-integrated changes culminate in large scale transformation. Embracing complexity and focusing on the ultimate aims of (re)institutionalization and (re)professionalization are crucial.

The METRIC-framework for assessing data quality for trustworthy AI in medicine: a systematic review

The adoption of machine learning (ML) and, more specifically, deep learning (DL) applications into all major areas of our lives is underway. The development of trustworthy AI is especially important in medicine due to the large implications for patients' lives. While trustworthiness concerns various aspects including ethical, transparency and safety requirements, we focus on the importance of data quality (training/test) in DL. Since data quality dictates the behaviour of ML products, evaluating data quality will play a key part in the regulatory approval of medical ML products. We perform a systematic review following PRISMA guidelines using the databases Web of Science, PubMed and ACM Digital Library. We identify 5408 studies, out of which 120 records fulfil our eligibility criteria. From this literature, we synthesise the existing knowledge on data quality frameworks and combine it with the perspective of ML applications in medicine. As a result, we propose the METRIC-framework, a specialised data quality framework for medical training data comprising 15 awareness dimensions, along which developers of medical ML applications should investigate the content of a dataset. This knowledge helps to reduce biases as a major source of unfairness, increase robustness, facilitate interpretability and thus lays the foundation for trustworthy AI in medicine. The METRIC-framework may serve as a base for systematically assessing training datasets, establishing reference datasets, and designing test datasets which has the potential to accelerate the approval of medical ML products.

Being a focused employee: Effects of job reattachment on cyberloafing

Cyberloafing, the use of the Internet for non-work-related activities while at work, has become a growing problem in the workplace. Since cyberloafing is commonly regarded as counterproductive, anticipating and controlling employees' cyberloafing behaviour is becoming increasingly important. Previous research suggests that individuals who engage in cyberloafing may have difficulties regulating their attention to important tasks. Based on self-regulation theory, our study examines the influence of job reattachment on cyberloafing through the mediating role of mindfulness and the moderating role of goal commitment. Data for this study were collected in China through surveys conducted with 275 employees at three different time points. The results showed that: (1) job reattachment was significantly negatively associated with cyberloafing; (2) mindfulness mediated this relationship between job reattachment and cyberloafing; and (3) goal commitment moderated the direct effect of job reattachment on mindfulness as well as the indirect effect of job reattachment on cyberloafing through mindfulness. Overall, our research findings emphasize the importance of the impact of job reattachment on cyberloafing through mindfulness. Theoretical and practical implications of this study are discussed.

A study of the psychological mechanisms of job burnout: implications of person-job fit and person-organization fit

Job burnout and work pressure are pivotal concerns in human resource management and workplace mental health, profoundly impacting organizational sustainability and individual well-being. Grounded in the person-environment fit theory, this empirical study quantitatively investigates the psychological mechanisms of person-job fit and person-organization fit in job burnout, highlighting the mediating role of work pressure. To test our hypotheses, we investigated 477 employees from 63 IT enterprises around China's Pearl River Delta region. The findings reveal that person-job fit is negatively associated with job burnout and work pressure, while work pressure positively influences job burnout, partially mediating the relationship between person-job fit and job burnout. Similarly, person-organization fit negatively affects job burnout and work pressure. However, its direct influence on job burnout is insignificant, indicating that work pressure fully mediates the relationship between person-organization fit and job burnout. These findings are consistent with the person-environment fit theory, enhancing our understanding of how individuals fit with their jobs and how organizations affect job burnout through work pressure. This study offers valuable insights for organizations seeking to mitigate burnout and promote employee well-being.

Exploring healthy lifestyle with health locus of control and sociodemographic variables in Türkiye

In this study, we aimed to explore the effects of Internal/Chance/Powerful Others Health Locus of Control (IHLC/CHLC/PHLC) on the healthy lifestyle and to assess the sensitivity of the healthy lifestyle to sociodemographic variables. To achieve this goal, we collected data by performing online and hand-delivered surveys (n = 950) with individuals aged 18 or older in Türkiye. The results showed that IHLC and PHLC had positive and significant effects on Healthy Lifestyle Index (HLI). However, the results expressed that CHLC had no negative and significant effect on HLI. Our assessment of a healthy lifestyle in terms of health locus of control (HLC) and sociodemographic variables revealed important findings, which may contribute to the development of public health strategies in several ways; for example, they can be used as a framework to conduct public health interventions that promote a healthy lifestyle.

Cultivating Innovative Employees: The Influence of Regulatory Focus and Institutional Empowerment

The relationship between regulatory focus, a pivotal trait, and innovative behavior has been long recognized, with previous scholars often emphasizing the reluctance of individuals possessing a prevention focus to engage in innovation due to their risk-averse tendencies. This study introduces a research model proposing that the relationship between promotion focus, prevention focus, and innovation behavior, is positively mediated by knowledge sharing. Additionally, institutional empowerment is posited as a moderating variable that enhances the positive relationship between regulatory focus and knowledge sharing. Empirical investigation of a moderated-mediation model reveals that the impact of regulatory focus on innovation behaviors is mediated by knowledge sharing, with this mediation being more pronounced under conditions of elevated perceptions of institutional empowerment. This research significantly advances the understanding of regulatory focus and its implications for innovation behavior. In addition, it highlights the significance of institutional empowerment as a boundary condition that encourages individuals with diverse regulatory focus to expand their behavioral boundaries. It specifically emphasizes the managerial capacity to leverage the needs and motivations of individuals with a pronounced prevention focus through institutional empowerment, resulting in transformative outcomes even in unfavorable situations.

Digital transformation and environmental, social, and governance greenwashing: Evidence from China

This paper examines the relationship between digital transformation(DT) and environmental, social, and governance(ESG) greenwashing using Chinese listed companies as a sample from 2012 to 2022. Furthermore, it analyzes the enterprise and regional heterogeneity as well as the influencing mechanisms on this relationship. The research results indicate that corporate digital transformation significantly inhibits ESG greenwashing, with a more pronounced effect on companies in non-high-pollution industries, high-tech industries, and the eastern region. In addition, mechanism tests reveal that internal control and financing constraints play a partial mediating role. Digital transformation suppresses ESG greenwashing by enhancing the quality of internal control and alleviating financing constraints. The primary contribution of this paper lies in demonstrating that digital transformation can serve as a strategic tool to mitigate ESG greenwashing. This enriches the research on the outcomes of digital transformation as well as the factors influencing ESG greenwashing. The conclusions of this paper provide theoretical foundations and policy recommendations for better ESG development by enterprises and governments in emerging markets. At the same time, this paper has a certain guiding role for the introduction and implementation of policies to encourage digital transformation.

Application of a Data Quality Framework to Ductal Carcinoma In Situ Using Electronic Health Record Data From the All of Us Research Program

PURPOSE

The specific aims of this paper are to (1) develop and operationalize an electronic health record (EHR) data quality framework, (2) apply the dimensions of the framework to the phenotype and treatment pathways of ductal carcinoma in situ (DCIS) using All of Us Research Program data, and (3) propose and apply a checklist to evaluate the application of the framework.

METHODS

We developed a framework of five data quality dimensions (DQD; completeness, concordance, conformance, plausibility, and temporality). Participants signed a consent and Health Insurance Portability and Accountability Act authorization to share EHR data and responded to demographic questions in the Basics questionnaire. We evaluated the internal characteristics of the data and compared data with external benchmarks with descriptive and inferential statistics. We developed a DQD checklist to evaluate concept selection, internal verification, and external validity for each DQD. The Observational Medical Outcomes Partnership Common Data Model (OMOP CDM) concept ID codes for DCIS were used to select a cohort of 2,209 females 18 years and older.

RESULTS

Using the proposed DQD checklist criteria, (1) concepts were selected and internally verified for conformance; (2) concepts were selected and internally verified for completeness; (3) concepts were selected, internally verified, and externally validated for concordance; (4) concepts were selected, internally verified, and externally validated for plausibility; and (5) concepts were selected, internally verified, and externally validated for temporality.

CONCLUSION

This assessment and evaluation provided insights into data quality for the DCIS phenotype using EHR data from the All of Us Research Program. The review demonstrates that salient clinical measures can be selected, applied, and operationalized within a conceptual framework and evaluated for fitness for use by applying a proposed checklist.

The Association Between Neuroticism and Problematic Social Networking Sites Use: The Role of Fear of Missing out and Self-Control

Problematic use of social networking sites (SNS) has a negative impact on mental health. It has been found that people who score high on neuroticism are especially vulnerable towards engaging with SNS in a problematic way but it is not clear which psychological mechanisms explain this relationship. We addressed this issue by examining the mediating role of fear of missing out and self-control in the relationship between neuroticism and problematic SNS use. For this purpose, we conducted a cross-sectional study (n = 151, 69.5% female, Mage = 26.23, SD = 7.52) and tested for parallel mediation using structural equation modelling. Neuroticism was found to be predictive of increased levels of problematic SNS use. Moreover, neuroticism was associated with both increased levels of fear of missing out and decreased levels of self-control. However, only fear of missing out was found to robustly mediate the relationship between neuroticism and problematic use of SNS. These findings suggest that fear of missing out could be an intervention target to prevent people scoring high on neuroticism from engaging in problematic SNS use.

Building a Logic Model to Foster Engagement and Learning Using the Case of a Province-Wide Multispecies Antimicrobial Use Monitoring System

Successfully designing and implementing a program is complex; it requires a reflexive balance between the available resources and the priorities of various stakeholders, both of which change over time. Logic models are theory-based evaluation approaches used to identify and address key challenges of a program. This article describes the process of building a logic model on advanced theories in complexity studies. The models aim to support a province-wide multispecies monitoring system of antimicrobial use (AMU), designed in collaboration with the animal health sector in Quebec (Canada). Based on a rigorous theoretical foundation, the logic model is built in three steps: (1) mapping, a narrative review of literature on similar programs in other jurisdictions; (2) framing, iterative consultations with project members to elaborate the logic model; (3) shaping, hypotheses based on the logic model. The model emerges from the reflexive balancing of current scientific knowledge and empirical insights to gather relevant information about stakeholders from interdisciplinary experts that led a 3-year consensus-building process within the community. Recognizing the challenge of unpacking theories for practical use, we illustrate how the process of an "open" logic model building could enable governance coordination in complex processes. Logic models are useful for evaluating public, private, and academic partnerships in One Health programs that characterize an adaptive governance process.

Countering technostress in virtual work environments: The role of work-based learning and digital leadership in enhancing employee well-being

Virtual work has been highlighted as an important business trend nowadays. Technostress caused by information and communication technology threatens employee well-being in a virtual context. We integrate insights from the Job Demands-Resources theory to explore the impact of technostress on employee well-being and examine work-based learning and digital leadership capability as buffers of this negative impact via the attenuating effect of work exhaustion. We collected multi-wave survey data from 300 virtual employees to test the theoretical model. Results revealed a significant negative impact of technostress on employee well-being. However, work-based learning and digital leadership capability buffered employees' well-being from this negative effect by reducing their work exhaustion. Our findings extend our understanding of work design and shed light on how to cope with virtual work demands.

Information Management in Hospital Unit Daily Operations: A Descriptive Study With Nurses and Physicians

Operations management of a hospital unit is a shared activity involving nursing and medical professionals, characterized by suddenly changing situations, constant interruptions, and ad hoc decision-making. Previous studies have explored the informational needs affecting decision-making, but only limited information has been collected regarding factors affecting information management related to the daily operations of hospital units. The aim of this study was to describe the experiences of nursing and medical professionals of information management in the daily operations of hospital units. This qualitative study consists of interviews following the critical incidence technique. Twenty-six nurses and eight physicians working in operational leadership roles in hospital units were interviewed, and the data were subjected to thematic analysis. The data analysis showed that strengths of current systems were organizational operational procedures, general instruments supporting information management, and a digital operations dashboard, whereas opportunities for improvement included the information architecture, quality of information, and technology use. The study findings highlight that despite several decades of efforts to provide solutions to support information management in hospital daily operations, further measures need to be taken in developing and implementing information systems with user-centered strategies and systematic approaches to better support healthcare professionals.

Does digital economy keep enterprises out of green innovation trouble? evidence from "double carbon" goal

The proposal of the "dual carbon" targets has provided direction for promoting China's economic green transformation and green innovation. As enterprises are important entities driving technological innovation in China, enhancing their green innovation capabilities holds significant practical significance for reducing carbon emissions. While the flourishing development of the digital economy currently provides new avenues and impetus for enterprise green innovation, research on the micro-level effects of green innovation driven by digital economy is still lacking. Therefore, based on data from Chinese cities spanning 2010 to 2021 and micro-level data of listed enterprises, this paper employs fixed effect model and intermediary effect model to empirically test the effect and mechanism of urban digital economy development on the quality and quantity of enterprise green innovation. Additionally, it investigates the carbon emission reduction effect resulting from digital economy development. The findings are as follows: (1) The development of digital economy exhibits a significant green innovation effect, effectively enhancing both the "quality and quantity" of enterprise green innovation. Particularly noteworthy is its impact on the quality of green innovation in enterprises, a result corroborated by several robustness tests. (2) Promoting regional knowledge spillover and alleviating enterprise financing constraints emerge as mechanisms through which the digital economy enhances the "quality and quantity" of enterprise green innovation. (3) Heterogeneity testing reveals that the digital economy exerts a greater green innovation effect on enterprises located in regions characterized by higher environmental regulations, high-tech industries, and state-owned enterprises. (4) Further analysis demonstrates that the digital economy effectively reduces enterprises carbon emissions and enhances their carbon performance by driving green innovation, thus providing endogenous impetus for achieving the "dual carbon" goals. By examining the effects and mechanisms of green innovation driven by digital economy from the perspectives of promoting knowledge spillover and alleviating financing constraints, this study enriches theoretical research on the relationship between the digital economy and green innovation at the enterprise micro-level. It offers new insights for governments in formulating green innovation policies and serves as a reference for green innovation decision-making in other developing countries.

A Review of the Validity and Reliability of Accelerometer-Based Metrics From Upper Back-Mounted GNSS Player Tracking Systems for Athlete Training Load Monitoring

ABSTRACT

Dawson, L, Beato, M, Devereux, G, and McErlain-Naylor, SA. A review of the validity and reliability of accelerometer-based metrics from upper back-mounted GNSS player tracking systems for athlete training load monitoring. J Strength Cond Res 38(8): e459-e474, 2024-Athlete load monitoring using upper back-mounted global navigation satellite system (GNSS) player tracking is common within many team sports. However, accelerometer-based load monitoring may provide information that cannot be achieved with GNSS alone. This review focuses on the accelerometer-based metrics quantifying the accumulation of accelerations as an estimation of athlete training load, appraising the validity and reliability of accelerometer use in upper back-mounted GNSS player tracking systems, the accelerometer-based metrics, and their potential for application within athlete monitoring. Reliability of GNSS-housed accelerometers and accelerometer-based metrics are dependent on the equipment model, signal processing methods, and the activity being monitored. Furthermore, GNSS unit placement on the upper back may be suboptimal for accelerometer-based estimation of mechanical load. Because there are currently no feasible gold standard comparisons for field-based whole-body biomechanical load, the validity of accelerometer-based load metrics has largely been considered in relation to other measures of training load and exercise intensity. In terms of convergent validity, accelerometer-based metrics (e.g., PlayerLoad, Dynamic Stress Load, Body Load) have correlated, albeit with varying magnitudes and certainty, with measures of internal physiological load, exercise intensity, total distance, collisions and impacts, fatigue, and injury risk and incidence. Currently, comparisons of these metrics should not be made between athletes because of mass or technique differences or between manufacturers because of processing variations. Notable areas for further study include the associations between accelerometer-based metrics and other parts of biomechanical load-adaptation pathways of interest, such as internal biomechanical loads or methods of manipulating these metrics through effective training design.

The effect of therapeutic and deterrent messages on Internet users attempting to access 'barely legal' pornography

Online child sexual abuse material (CSAM) is a growing problem. Prevention charities, such as Stop It Now! UK, use online messaging to dissuade users from viewing CSAM and to encourage them to consider anonymous therapeutic interventions. This experiment used a honeypot website that purported to contain barely legal pornography, which we treated as a proxy for CSAM. We examined whether warnings would dissuade males (18-30 years) from visiting the website. Participants (n = 474) who attempted to access the site were randomly allocated to one of four conditions. The control group went straight to the landing page (control; n = 100). The experimental groups encountered different warning messages: deterrence-themed with an image (D3; n = 117); therapeutic-themed (T1; n = 120); and therapeutic-themed with an image (T3; n = 137). We measured the click through to the site. Three quarters of the control group attempted to enter the pornography site, compared with 35 % to 47 % of the experimental groups. All messages were effective: D3 (odds ratio [OR] = 5.02), T1 (OR = 4.06) and T2 (OR = 3.05). Images did not enhance warning effectiveness. We argue that therapeutic and deterrent warnings are useful for CSAM-prevention.

Exploring the Usage Effectiveness of a Nursing Charge System

The nursing charge system for inpatient accounting has been utilized in healthcare institutions for years. However, the level of its effectiveness in meeting the needs of nursing services, including further development, has not been systematically evaluated. A cross-sectional study based in Delone and McLean's information system success model was applied to explore the level of effective nursing charge system usage across the five dimensions of system quality, information quality, service quality, user satisfaction, and net benefits. We conducted a survey of the inpatient units of a medical center in Taiwan from June 23, 2021, to July 23, 2021. A total of 214 valid questionnaires were collected. Using a 5-point Likert scale, the dimension with the highest score was information quality (3.71), followed by service quality (3.37), user satisfaction (3.36), net benefits (3.31), and system quality (3.23). Older nurses ( r = -0.176) and those with more clinical experience ( r = -0.151) viewed the nursing charge system as having less information quality. The comfort level with using the computer was positively associated with system quality ( r = 0.396), information quality ( r = 0.378), service quality ( r = 0.275), user satisfaction ( r = 0.417), and net benefits ( r = 0.355). The opinions of nurses are vital. User feedback and advice should be investigated regularly to achieve system optimization.

Catalysts of digital excellence: Unraveling the threads of connectivity, embedment, and visibility in enterprise social media for enhanced task and innovative performances

In an era characterized by rapid digital transformation, this research delves into the intricate dynamics of enterprise social media (ESM) ubiquitous connectivity, drawing on the influential framework of social cognitive theory (SCT), we unravel the complex relationships among compatibility, presenteeism, ESM-enabled embeddedness, and the visibility of digital interactions, shedding light on how these factors collectively shape task performance and innovative contributions in the contemporary hybrid work landscape. By incorporating SCT into our research model, we not only enhance the theoretical underpinning but also respond to the imperative of understanding how individual cognition and observational learning impact behaviors in digital work environments. Data is collected from 467 participants from China. Results indicated that ESM-enabled embeddedness mediates the connection between presenteeism and both digital-enabled task performance, digital-enabled innovative performance. Findings conformed that ESM-enabled embeddedness mediates the connection between compatibility and both digital-enabled task performance, digital-enabled innovative performance. ESM visibility strengthens the connection between ubiquitous connectivity (presenteeism, compatibility) and ESM-enabled embeddedness. In addition, results also indicated that ESM visibility moderates the mediation effect of ESM-enabled embeddedness between the dimensions of ESM ubiquitous connectivity (presenteeism) and both digital-enabled task performance, digital-enabled innovative performance. In contrast, ESM visibility did not moderates the mediation effect of ESM-enabled embeddedness between the dimensions of ESM ubiquitous connectivity (compatibility) and both digital-enabled task performance, digital-enabled innovative performance. This study not only contributes depth to ESM literature but also lays the groundwork for future research endeavors, encouraging exploration of diverse contexts and additional moderating factors to comprehensively grasp the intricate dynamics within modern digital workspaces.

The impact of digital inclusive finance on the collaborative reduction of pollutant and carbon emissions: spatial spillover and mechanism analysis

In light of the escalating global climate risks threatening human survival, there is a global consensus on the necessity for collaborative reduction of pollutant and carbon emissions (CRPC). Within this context, digital inclusive finance (DIF) is recognized for its unique inclusiveness and digital characteristics as a critical factor in promoting environmentally friendly and sustainable development. DIF provides advantageous channels for environmental governance, thereby making the achievement of CRPC objectives feasible. However, the impact of DIF on CRPC has not been fully explored. This study employs a spatial econometric model to investigate the impact of DIF on CRPC in 278 prefecture-level cities in China from 2011 to 2020. The findings indicate that DIF has a positive impact on CRPC, with significant spatial spillover effects. The analysis highlights the pivotal mediating roles played by technology effect and electrified effect of the energy mix, while environmental regulation effect plays a moderating role. Notably, disparities in the impact of DIF on CRPC are evident, particularly in non-resource-based cities, cities with low carbon intensity, and eastern regions where spatial spillover effects are more pronounced. These experiences enrich the relevant thesis in terms of DIF on CRPC, providing a theoretical basis for formulating CRPC schemes.

Online Medical Consultation Service-Oriented Recommendations: Systematic Review

BACKGROUND

Online health communities have given rise to a new e-service known as online medical consultations (OMCs), enabling remote interactions between physicians and patients. To address challenges, such as patient information overload and uneven distribution of physician visits, online health communities should develop OMC-oriented recommenders.

OBJECTIVE

We aimed to comprehensively investigate what paradigms lead to the success of OMC-oriented recommendations.

METHODS

A literature search was conducted through e-databases, including PubMed, ACM Digital Library, Springer, and ScienceDirect, from January 2011 to December 2023. This review included all papers directly and indirectly related to the topic of health care-related recommendations for online services.

RESULTS

The search identified 611 articles, of which 26 (4.3%) met the inclusion criteria. Despite the growing academic interest in OMC recommendations, there remains a lack of consensus among researchers on the definition of e-service-oriented recommenders. The discussion highlighted 3 key factors influencing recommender success: features, algorithms, and metrics. It advocated for moving beyond traditional e-commerce-oriented recommenders to establish an innovative theoretical framework for e-service-oriented recommenders and addresses critical technical issues regarding 2-sided personalized recommendations.

CONCLUSIONS

This review underscores the essence of e-services, particularly in knowledge- and labor-intensive domains such as OMCs, where patients seek interpretable recommendations due to their lack of domain knowledge and physicians must balance their energy levels to avoid overworking. Our study's findings shed light on the importance of customizing e-service-oriented personalized recommendations to meet the distinct expectations of 2-sided users considering their cognitive abilities, decision-making perspectives, and preferences. To achieve this, a paradigm shift is essential to develop unique attributes and explore distinct content tailored for both parties involved.

Predicting hospital length of stay using machine learning on a large open health dataset

BACKGROUND

Governments worldwide are facing growing pressure to increase transparency, as citizens demand greater insight into decision-making processes and public spending. An example is the release of open healthcare data to researchers, as healthcare is one of the top economic sectors. Significant information systems development and computational experimentation are required to extract meaning and value from these datasets. We use a large open health dataset provided by the New York State Statewide Planning and Research Cooperative System (SPARCS) containing 2.3 million de-identified patient records. One of the fields in these records is a patient's length of stay (LoS) in a hospital, which is crucial in estimating healthcare costs and planning hospital capacity for future needs. Hence it would be very beneficial for hospitals to be able to predict the LoS early. The area of machine learning offers a potential solution, which is the focus of the current paper.

METHODS

We investigated multiple machine learning techniques including feature engineering, regression, and classification trees to predict the length of stay (LoS) of all the hospital procedures currently available in the dataset. Whereas many researchers focus on LoS prediction for a specific disease, a unique feature of our model is its ability to simultaneously handle 285 diagnosis codes from the Clinical Classification System (CCS). We focused on the interpretability and explainability of input features and the resulting models. We developed separate models for newborns and non-newborns.

RESULTS

The study yields promising results, demonstrating the effectiveness of machine learning in predicting LoS. The best R2 scores achieved are noteworthy: 0.82 for newborns using linear regression and 0.43 for non-newborns using catboost regression. Focusing on cardiovascular disease refines the predictive capability, achieving an improved R2 score of 0.62. The models not only demonstrate high performance but also provide understandable insights. For instance, birth-weight is employed for predicting LoS in newborns, while diagnostic-related group classification proves valuable for non-newborns.

CONCLUSION

Our study showcases the practical utility of machine learning models in predicting LoS during patient admittance. The emphasis on interpretability ensures that the models can be easily comprehended and replicated by other researchers. Healthcare stakeholders, including providers, administrators, and patients, stand to benefit significantly. The findings offer valuable insights for cost estimation and capacity planning, contributing to the overall enhancement of healthcare management and delivery.