Information Platform Research Articles

Assessment of Reliability and Validity of Celiac Disease-Related YouTube Videos: Content Analysis.

YouTube is an increasingly used platform for medical information. However, the reliability and validity of health-related information on celiac disease (CD) on YouTube have not been determined. This study aimed to analyze the reliability and validity of CD-related YouTube videos. On November 15, 2023, a search was performed on YouTube using the keyword "celiac disease." This search resulted in a selection of videos, which were then reviewed by 2 separate evaluators for content, origin, and specific features. The evaluators assessed the reliability and quality of these videos using a modified DISCERN (mDISCERN) score, the Journal of the American Medical Association (JAMA) benchmark criteria score, the usefulness score, video power index (VPI), and the Global Quality Scale (GQS) score. In the analysis of 120 initially screened CD videos, 85 met the criteria for inclusion in the study after certain videos were excluded based on predefined criteria. While the duration of the videos uploaded by health care professionals was significantly longer than the other group (P=.009), it was concluded that the median scores for mDISCERN (4, IQR 4-5 vs 2, IQR 2-3; P<.001), GQS (4, IQR 4-5 vs 3, IQR 2-3; P<.001), JAMA (4, IQR 3-4 vs 2, IQR 2-3; P<.001), and usefulness (8, IQR 7-9 vs 6, IQR 3-6; P<.001) of the videos from this group were significantly higher than those from non-health care professionals. Video interaction parameters, including the median number of views, views per day, likes, dislikes, comments, and VPI, demonstrated no significant difference between the 2 groups. This study showed that YouTube videos about CD vary significantly in reliability and quality depending on their source. Increasing the production of reliable videos by health care professionals may help to improve patient education and make YouTube a more reliable resource.

Day-to-day dynamics in two-sided ridesourcing markets

To understand why ridesourcing markets may be prone to evolve towards potentially socially undesirable equilibrium states, we conceptualize the network effects present in ridesourcing provision. In addition, we propose an agent-based model that allows simulating the effect of market conditions and platform strategies on system performance, accounting for such network effects. This day-to-day model captures sequential decentralized processes characterizing both sides of the two-sided ridesourcing market, i.e. information diffusion, platform registration, platform participation, and learning based on experience. We apply the model on a case representing Amsterdam, the Netherlands. Our simulation results suggest that a profit-maximizing ridesourcing platform may trade-off market transaction volume for higher earnings on successful transactions, a strategy that is harmful to the interests of travellers and drivers, and possibly of (very) limited benefit to the platform. Moreover, we find that ridesourcing operations may be viable even when potential supply and demand in an area are limited.

Construction and application of a stratified nursing intervention program for postoperative delirium after Stanford type A aortic dissection: a quasi-experimental trial

BackgroundPostoperative delirium is a common and significant consequence in patients with type A aortic dissection following surgery; however, there is currently no effective postoperative care plan.ObjectiveThe purpose of this study is to develop and evaluate the practical implementation of a stratified nursing intervention program for postoperative delirium in patients undergoing surgery for Stanford type A aortic dissection.MethodsA stratified nursing intervention program for postoperative delirium was created using a literature review, group discussions, and the Delphi method via expert mail inquiry. A quasi-experimental design was used, with patients admitted to the general ward of a tertiary hospital in Wenzhou, China, functioning as subjects. From September to December 2022, 43 patients were included in the control group and given standard treatment. From January to June 2023, 37 patients were recruited in the intervention group, receiving both routine care and the intervention plan. The intervention’s effects were compared in the two groups.ResultsThe intervention group had considerably shorter delirium and hospital stays than the control group (P < 0.05), as well as higher patient satisfaction levels. There were no inadvertent extubations in the intervention group. When the incidence and beginning time of delirium in the general ward were compared, there were no significant differences between the two groups (P > 0.05).ConclusionImplementing this nurse intervention in the ward context can shorten postoperative delirium and hospital stays, increase patient satisfaction with care, and improve patient prognosis and quality of life. This intervention will also serve as a great resource for future clinical management of postoperative delirium.Trial registrationThe National Health Security Information Platform’s Medical Research Registration Information System has registered this study under the registration number MR-33–22-022978.

ChatGPT 35 Better Improves Comprehensibility of English, Than Spanish, Generated Responses to Osteosarcoma Questions.

Despite adequate discussion and counseling in the office, inadequate health literacy or language barriers may make it difficult to follow instructions from a physician and access necessary resources. This may negatively impact survival outcomes. Most healthcare materials are written at a 10th grade level, while many patients read at an 8th grade level. Hispanic Americans comprise about 25% of the US patient population, while only 6% of physicians identify as bilingual. (1) Does ChatGPT 3.5 provide appropriate responses to frequently asked patient questions that are sufficient for clinical practice and accurate in English and Spanish? (2) What is the comprehensibility of the responses provided by ChatGPT 3.5 and are these modifiable? Twenty frequently asked osteosarcoma patient questions, evaluated by two fellowship-trained musculoskeletal oncologists were input into ChatGPT 3.5. Responses were evaluated by two independent reviewers to assess appropriateness for clinical practice, and accuracy. Responses were graded using the Flesch Reading Ease Score (FRES) and the Flesch-Kincaid Grade Level test (FKGL). The responses were then input into ChatGPT 3.5 for a second time with the following command "Make text easier to understand". The same method was done in Spanish. All responses generated were appropriate for a patient-facing informational platform. There was no difference in the Flesch Reading Ease Score between English and Spanish responses before the modification (p = 0.307) and with the Flesch-Kincaid grade level (p = 0.294). After modification, there was a statistically significant difference in comprehensibility between English and Spanish responses (p = 0.003 and p = 0.011). In both English and Spanish, none of the ChatGPT generated responses were found to be factually inaccurate. ChatGPT was able to modify responses upon follow-up with a simplified command. However, it was shown to be better at improving English responses than equivalent Spanish responses.

Identifying protected health information by transformers-based deep learning approach in Chinese medical text.

Purpose: In the context of Chinese clinical texts, this paper aims to propose a deep learning algorithm based on Bidirectional Encoder Representation from Transformers (BERT) to identify privacy information and to verify the feasibility of our method for privacy protection in the Chinese clinical context. Methods: We collected and double-annotated 33,017 discharge summaries from 151 medical institutions on a municipal regional health information platform, developed a BERT-based Bidirectional Long Short-Term Memory Model (BiLSTM) and Conditional Random Field (CRF) model, and tested the performance of privacy identification on the dataset. To explore the performance of different substructures of the neural network, we created five additional baseline models and evaluated the impact of different models on performance. Results: Based on the annotated data, the BERT model pre-trained with the medical corpus showed a significant performance improvement to the BiLSTM-CRF model with a micro-recall of 0.979 and an F1 value of 0.976, which indicates that the model has promising performance in identifying private information in Chinese clinical texts. Conclusions: The BERT-based BiLSTM-CRF model excels in identifying privacy information in Chinese clinical texts, and the application of this model is very effective in protecting patient privacy and facilitating data sharing.

Evaluation of tumor-colonizing Salmonella strains using the chick chorioallantoic membrane model.

The chick embryo chorioallantoic membrane (CAM) tumor model is a valuable preclinical model for studying the tumor-colonizing process of Salmonella enterica serovar Typhimurium. It offers advantages such as cost-effectiveness, rapid turnaround, reduced engraftment issues, and ease of observation. In this study, we explored and validated the applicability of the partially immune-deficient CAM tumor model. Herein, we demonstrate that Salmonella preferentially colonizes tumors and directly causes tumor cell death. Bacterial migration, tumor colonization, and intra-tumor distribution did not require flagellar-mediated motility. The vast majority of Salmonella that colonized the CAM tumor were extracellular. Thus, tumor invasion was independent of both Salmonella pathogenicity island-1-encoded and Salmonella pathogenicity island-2-encoded type III secretion systems. Surprisingly, the extracellular residence of Salmonella on CAM tumors did not require biofilm formation. We evaluated our wild-type parental strain compared to the attenuated clinical strain VNP20009 and discovered a reduced tumor colonization capability of VNP20009. The inability to effectively colonize CAM tumors potentially explains the reduced anti-tumor efficacy of VNP20009. Our work establishes the xenograft CAM model as an informative and predictive screening platform for studying tumor-colonizing Salmonella.IMPORTANCECancer has a major impact on society, as it poses a significant health burden to human populations worldwide. Salmonella Typhimurium has demonstrated promise in cancer treatment by exerting direct tumoricidal effects and enhancing host-mediated anti-tumor immunity in xenograft mouse studies. A general understanding of its pathogenesis and the relative ease of genetic manipulation support the development of attenuated strains for therapeutic use. Alternative in ovo models, such as the chorioallantoic membrane tumor model, present a suitable screening platform to accelerate the development of therapeutic strains. It allows for rapid evaluation of Salmonella strains to assess their efficacy and potential as oncolytic agents. The present study establishes that the in ovo tumor model can be utilized as a preclinical tool for evaluating oncolytic Salmonella, bridging the gap between in vitro and in vivo screening.

Research motivation as a mediating variable between system intelligence, academic grit, and academic achievement among postgraduate students, faculty of education, Zagazig University

BackgroundRecent years have witnessed a revolutionary transformation in information technology, characterized by the proliferation of electronic information platforms, with the Egyptian Knowledge Bank being a notable example. Understanding how to effectively navigate these complex systems requires investigation into key factors, particularly system intelligence.ObjectivesThis study aimed to examine the mediating role of research motivation in the relationship between system intelligence, Academic Grit, and Academic Achievement.MethodUsing a correlational design, the study surveyed 600 post-graduate students aged 25–55 years (M = 33.22, SD = 8.09) through online snowball sampling. Data were collected using the Research Motivation Scale, System Intelligence Scale, Academic Grit Scale, and Grade Point Average (GPA). Statistical analyses were conducted using IBM Amos, Spss 23.ResultsPath analysis revealed that system intelligence had an indirect effect on Academic Grit through research motivation. Additionally, direct effects were observed from system intelligence to research motivation, system intelligence to Academic Grit, and Academic Grit to Academic Achievement.ConclusionsThis study highlights research motivation as a crucial mediator in the relationship between system intelligence, academic grit, and academic achievement. The findings suggest potential interventions to enhance academic achievement by fostering system intelligence and academic grit through the development of research motivation.

Proactive Threat Detection in Cloud Ecosystems: SIEM, Monitoring, and Automated Remediation

In modern cloud ecosystems, the stakes are higher than ever for proactive threat detection and rapid incident response. As organizations embrace multi-cloud and hybrid environments, traditional security controls must evolve to protect distributed workloads and data. Security Information and Event Management (SIEM) platforms, continuous monitoring, and automated remediation mechanisms form the backbone of a robust, proactive security strategy. This white paper delves into the technical underpinnings of these solutions, highlighting architectural components, methodologies, and implementation considerations. This paper examines various challenges such as scale, heterogeneous data sources, and advanced attack vectors, along with corresponding solutions. In addition, explores real-world case studies that demonstrate the viability and effectiveness of implementing SIEM, monitoring, and automated remediation strategies in production environments. Keywords: Threat Detection, Cloud Security, SIEM, Continuous Monitoring, Automated Remediation, Hybrid Cloud, Multi-Cloud, Architecture, Implementation

Research on time series analysis of data products in electric power marketing management information platform based on recurrent neural networks

Abstract The power marketing management information platform can promote the energy industry's decision-making ability for power marketing business and improve demand-side management. Due to the large number of power marketing users and heterogeneous data, traditional time series analysis methods are difficult to flexibly deal with this scenario, and deep learning methods based on feedforward neural networks have weak modeling capabilities for the time dimension of data, making it difficult to effectively use these data to support decision-making. To this end, this study proposes a new time series analysis model for the power marketing management information platform, which uses a recurrent neural network (RNN) enhanced by an attention mechanism. By integrating the long-term time dependency modeling capability of RNN and the adaptive extraction capability of the attention mechanism, the model can efficiently represent time series data. Experimental evaluation shows that our method greatly outperforms traditional models and achieves higher prediction accuracy on several key performance indicators.

P0907 Health literacy and digital health literacy in Inflammatory Bowel Disease as conditions for individualized adapted digital health information

Abstract Background Digital health information appears inadequate of providing medical expertise and clinical guidelines in IBD, including optimized accessibility, relevance and understandability for effective self-management. This study examines IBD patients’ health literacy (HL) and digital health literacy (DHL) and their covariance with various patient characteristics as implications for a digital health information platform. Methods IBD patients at a Norwegian university hospital underwent a cross-sectional survey. Clinical data were extracted from EHR. Other data were reported via questionnaire with the following instruments: HL – Health Literacy Questionnaire; DHL – eHealth Literacy Questionnaire; Quality of Life (QoL) – EQ-5D-5L; Self-efficacy (GSE) - General Self-Efficacy Scale; Illness perception (IP) – Brief Illness Perception Questionnaire; Medication adherence (MA) – The OsloMet Adherence to medication Survey tool. Data underwent a canonical correlation analysis (CCA) followed by hierarchical clustering. Variables omitted from CCA underwent bivariate analyses and logistic regression to assess cluster affiliation. Results Of 380 patients, 173 had UC and 53 % were male. Mean age was 43.6 years. No diagnose-specific PROM differences were found. CCA demonstrated positive associations for GSE, IP and health status against having sufficient capability to understand, assess and act upon health information and digital health services (p &amp;lt; .001). CCA also proved a negative association for age and actively engaging with digital health services (p &amp;lt; .001). Two clusters were identified (p &amp;lt;. 05). Cluster 1 embodied the oldest patients, lowest levels of HL, DHL, GSE, health status, IP and longest disease duration. Compared to Cluster 2, Cluster 1 significantly demonstrated lower MA (p &amp;lt;.05), higher shares of unemployment (p &amp;lt;.05), active disease (p &amp;lt;.001), and more severe QoL problems in terms of mobility (p &amp;lt; .05), usual activities (p &amp;lt; .001), pain/discomfort (p &amp;lt; .001) and anxiety/depression (p &amp;lt; .001). Disease activity (p &amp;lt;. 01) and biological treatment (p &amp;lt; .001) proved to be the strongest factors for cluster affiliation in a logistic model. Conclusion Our results imply a relation between health status, age, self-efficacy, biological treatment, disease perception and disease activity for IBD patients with low HL and DHL. The findings are implicative for a targeted intervention addressing digital health information dissemination for IBD patients.

Organizational and legal aspects of providing medical care using telemedicine technologies in Russian Federation and foreign countries

The global telemedicine technology (TMT) regulatory landscape is currently evolving in a number of ways. The goal is to carry out an analytical examination of the institutional and legal frameworks for medical treatment including telemedicine consultation (TMC) in Russian Federation and foreign countries. The regulatory legal regulation of 22 international healthcare systems was included in the final version of the review. The review’s findings allow for the identification of the most important organizational and legal facets of delivering healthcare via TMC abroad: the majority of foreign nations lack comprehensive legal regulations governing the use of TMT in healthcare; the ability to establish a diagnosis within the context of TMC only follows an initial in-person consultation with a doctor; conducting TMC exclusively by licensed medical professionals and medical organizations; the option to conduct TMC via government-designed platforms and publicly accessible information platforms; requiring the patient to be informed about the benefits and drawbacks of TMT prior to consultation and to provide written or verbal informed consent; and requiring adherence to standards and recommendations during TMC, application, and in-person admission of the patient. The analysis demonstrates how commonplace TMC is abroad. However, there are currently no well-defined organizational or regulatory requirements for the use of TMC in the delivery of medical care, which calls for additional research into global experience and the identification of best practices that may be expanded throughout the Russian Federation.

Evaluasi Rancangan UI/UX SIAKAD STMIK Rosma dengan Metode User Experience Questionnaire (UEQ)

SIAKAD (Academic Information System) is a technology-based information platform used by educational institutions to manage various aspects of academic administration in an integrated manner. Rosma Academic Information System (SIAKAD) is a web-based academic information system used to manage academic administration at STMIK Rosma Karawang in an efficient and structured manner. The research process began by identifying weaknesses in the existing website design through observations and interviews with active users. Designing the SIAKAD Rosma Karawang Website Design is a study that aims to improve the quality of the Rosma Karawang Academic Information System (SIAKAD) website. This research uses the User Experience Questionnaire (UEQ) method with six measurement scales and cooperative evaluation methods to identify user problems. The process began with surveys and interviews, followed by redesign and testing of the prototype using UEQ. Based on the data and calculation results in this research, it is shown that the SIAKAD Rosma Karawang UI/UX plan has succeeded in providing a good user experience, in accordance with the hedonic and pragmatic aspects evaluated, and can be recommended with a positive evaluation value (Above Average) . Keywords: Website Redesign, SIAKAD, UEQ, User Experience, Web Design.

Tourism Technology with Ergo-Iconic Value Innovation for Sutainable Economic Development

Introduction: This research investigates the impact of tourist attraction resonance on sustainable tourism development in Indonesia, building upon previous work by incorporating technology variables. Following the COVID-19 pandemic, Indonesian tourism is recovering, with foreign tourist arrivals showing an increase in 2022. The government has implemented a sustainable tourism program emphasizing environmental, social, cultural, and economic sustainability. This study examines how tourist attraction resonance influences sustainable development, focusing on the importance of community-based tourism, where local communities actively participate in tourism planning and management. The research highlights the crucial role of education and awareness in driving behavioral change among stakeholders to promote sustainable tourism practices. Objectives: This study aims to complement and fill the gap of previous research related to sustainable tourism development by considering technological aspects as a measurable variable in supporting tourism after the COVID-19 pandemic. Theoretical Framework: This research is based on the concept of ergo-iconic value which emphasises comfort characterised by local icons, as well as the theory of sustainable tourism development which includes environmental, social, cultural, and economic aspects. This framework also includes the concept of community-based tourism that encourages the active participation of local communities in tourism planning and management. Methods: Using a quantitative approach with a survey technique of 50 respondents conducted during a joint holiday period in April 2024 at the Belitung Island tourist attraction. The research went through 8 systematic stages, starting from identifying practical problems to developing solutions through cycles of planning, action, observation, and reflection. Results: The research found five key aspects in sustainable tourism development: Marketing information media: TikTok dominates (50%) as a tourist information platform. Tourism objects: 90% of respondents chose nature tourism Accountability: Appropriate service and no extra cost 50% each Culinary: Halal food is the top priority (50%) Technology: Ease of use (30 points) is the most important factor, followed by technological origin and time saving (25 points each). Conclusions: This research underscores the pivotal role of technology integration with local values in fostering meaningful and sustainable tourism experiences. The dominance of TikTok as a key source of travel information signifies a paradigm shift towards visually driven and authentic travel experiences. This aligns with the growing preference for nature-based tourism (90%), emphasizing the urgency for environmentally conscious tourism models. Furthermore, the industry's growing accountability, reflected in service delivery alignment and pricing transparency, indicates a positive evolution towards more professional and sustainable tourism practices. The significant demand for halal food (50%) highlights the crucial need for inclusive and culturally sensitive tourism product development. Finally, the highest score for ease of use in technology adoption underscores the importance of user-friendly interfaces in supporting sustainable tourism initiatives, such as enhancing biodiversity conservation and minimizing environmental impact.

Assessment of MPOX infection-related knowledge levels, concerns, and associated factors: a community-based cross-sectional study

BackgroundMPOX (Monkeypox) is a zoonotic disease of increasing global concern due to its re-emergence and potential for human-to-human transmission. Effective public health interventions rely on understanding socio-demographic determinants of knowledge and perceptions of the disease. This study aimed to investigate MPOX-related knowledge and concerns among a diverse sample in Türkiye, identifying key factors influencing knowledge levels.MethodsA cross-sectional study was conducted with 509 participants aged 18–73 years (mean age: 33.8 ± 15.6) in all settlements of Türkiye. Socio-demographic data were collected, and MPOX knowledge and concerns were assessed using a structured data form. To assess knowledge levels regarding MPOX, 15 questions were defined with 1 point given for each correct answer, and these questions were then categorized as high knowledge (≥ 10 correct answers) and low knowledge (< 10 correct answers). Univariable and multivariable binary logistic regression analyses were conducted to identify factors associated with knowledge levels. Trust and reliance on information sources were also evaluated. The data form was distributed to participants via social media platforms.ResultsThe majority of participants (97.1%) were aware of MPOX, but only 6.5% believed adequate precautions were in place. The mean knowledge score was 7.6 ± 3.7, with 37.7% demonstrating high knowledge. In terms of concerns about MPOX, only 31.6% of participants were identified as concerned, whereas the majority (68.4%) of the participants reported no significant concerns. The most trusted source of information about MPOX among the participants was medical doctors and healthcare professionals, as indicated by 53.63% of respondents. Low knowledge was significantly associated with older age (p = 0.015), female gender (p = 0.002), lower education levels (p < 0.001), non-medical fields (p < 0.001), and lower income (p < 0.001). Social media (53.11%) was the most common information source, yet healthcare professionals (53.63%) were the most trusted. Multivariable logistic regression confirmed that being in non-medical fields (OR = 2.858, 95% CI: 1.809–4.515, p < 0.001), lower income (OR = 3.141, 95% CI: 2.015–4.896, p < 0.001), and perceived low immunity (OR = 2.264, 95% CI: 1.350–3.797, p = 0.002) independently predicted lower knowledge.ConclusionsDespite high awareness, significant gaps in MPOX knowledge exist, particularly among older adults, females, non-medical professionals, and those with low income. Public health strategies should prioritize these groups, leveraging trusted sources like healthcare professionals while improving the reliability of digital information platforms.

Combining Differential Privacy with FedAvgM to Optimize Data Access Control in the Information Platform

In response to the privacy leakage risk and low-efficiency issues of traditional data access control, especially in distributed environments, data transmission and centralized processing may expose sensitive information. To improve data access control in information platforms, ensure data security, and accelerate global model training, this article introduced a joint mechanism of differential privacy and FedAvgM (Federated Averaging with Momentum) optimization to enhance privacy protection and improve training efficiency. Firstly, the Laplacian noise mechanism was adopted to prevent user privacy information from being leaked during global model training by adding noise to the dataset, ensuring the privacy of each data access. Then, combined with the FedAvgM optimization algorithm, distributed nodes calculated local model parameters separately and merged these parameters through weighted averaging to reduce the training time of the global model and improve efficiency. Finally, layered encryption technology was adopted to add multiple layers of encryption during data transmission to ensure the security of the transmission link. At the same time, a dynamic permission allocation mechanism was introduced to limit the frequency of access to sensitive data. The experimental results demonstrate that when the privacy budget at ε=20, the privacy protection method of Laplace mechanism still maintains 73% accuracy in data transmission, with a privacy leakage risk of only 0.27. Under the same training epochs, the FedAvgM optimization algorithm achieved a correct data transmission accuracy of 97%. In comparison, the convergence speed increased by 0.29%/min in 1-20 training epochs, showing a faster convergence speed. Under four different noise attack methods, the anti-noise ability of layered encryption is the lowest at 15.4 dB, 18.5 dB, 17.1 dB, and 17.3 dB, respectively. This method effectively improves the problems of privacy leakage risk and low efficiency in traditional data access control.

Use of information technologies in the administrative justice of Ukraine

The article examines the conceptual foundations of the introduction of information technologies into the administrative justice system of Ukraine, in particular their role in increasing transparency and accessibility, which will contribute to increasing the effectiveness of the judicial system of Ukraine and strengthening its integration into European standards of justice. The main information platforms used in administrative courts are identified, such as the Unified Judicial Information and Telecommunication System and the «Electronic Court», which contribute to the automation of processes and reduce the bureaucratic burden, as well as significantly improve citizens’ access to justice, allowing to submit documents and appeal to the court regardless of physical location. This is especially important in martial law, when access to judicial institutions can be difficult or dangerous. An analysis of the current regulatory framework, which regulates the use of electronic evidence in administrative cases, is carried out, its development and the need for improvement are indicated, in particular in terms of ensuring the authenticity and integrity of electronic evidence. In addition, considerable attention was paid to the issues of information security and data protection, which are critically important for the stable functioning of electronic justice and ensuring trust in its work by citizens and the professional community. Problematic aspects of digitalization of the judicial system are identified and prospects for further development are outlined, in particular, the use of artificial intelligence to improve the efficiency of justice. It mentions the issue of the legal status of electronic evidence, which is critical for legal proceedings in the digital age, and the need to ensure information security in the face of growing cyber threats. The novelty of the study lies in the comprehensive approach to assessing the impact of information technologies on administrative justice. The article highlights the need for further development of the regulatory framework, which should provide support for innovative approaches, as well as the need to ensure the continuous improvement of the qualifications of court employees for the proper functioning of the judicial system in conditions of digitalization.

Cross-Cultural Perspectives on Fake News: A Comparative Study of Instagram Users in Greece and Portugal

As our society increasingly relies on digital platforms for information, the spread of fake news has become a pressing concern. This study investigates the ability of Greek and Portuguese Instagram users to identify fake news, highlighting the influence of cultural differences. The responses of 220 Instagram users were collected through questionnaires in Greece and Portugal. The data analysis investigates characteristics of Instagram posts, social endorsement, and platform usage duration. The results reveal distinct user behaviors: Greeks exhibit a unique inclination towards social connections, displaying an increased trust in friends’ content and investing more time on Instagram, reflecting the importance of personal connections in their media consumption. They also give less importance to a certain post’s characteristics, such as content opposing personal beliefs, emotional language, and poor grammar, spelling, or formatting when identifying fake news, compared to the Portuguese, suggesting a weaker emphasis on content quality in their evaluations. These findings show that cultural differences affect how people behave on Instagram. Hence, content creators, platforms, and policymakers need specific plans to make online spaces more informative. Strategies should focus on enhancing awareness of key indicators of fake news, such as linguistic quality and post structure, while addressing the role of personal and social networks in the spread of misinformation.

Does internet-based climate information improve non-fishing employment opportunities? Insights from smallholder fishermen in Indonesia

ABSTRACT In the face of climate change, smallholder fishermen increasingly turn to non-fishing employment to sustain their livelihoods. Diversifying sources of income becomes paramount to mitigate the impact of unpredictable weather patterns on fish populations and, hence, household livelihoods. However, fishermen need convenient climate information access to navigate this transition effectively. With information about weather trends and forecasts, they can make informed decisions about when to fish and when to focus on alternative sources of income. This study offers novelty by investigating the impact of Internet-based climate information on non-fishing employment among smallholder fishermen in Indonesia. The study employs cross-sectional data collected from 503 smallholder fishermen in Indonesia, analyzed using a conditional mixed process (CMP) approach to tackle the endogeneity issue that may arise in the estimation. The results highlight that adopting Internet-based climate information among smallholder fishermen was positively and significantly associated with education, distance to fishing ports, social activity, fishing location, adoption of multiple gears, and access to the Internet among family members and relatives. However, the age of fishermen shows otherwise. Furthermore, our empirical findings confirm that Internet-based climate information significantly increases opportunities for non-fishing employment among fishermen. Disaggregate estimation by fishing boat size category strengthens this finding by showing a positive and significant coefficient in all categories. Therefore, this finding implies a need to improve and develop Internet-based climate information platforms in fisheries.

Enhancing Public Support Through Modern Communication Strategies: A Study on Croatian Military Relations and Recruitment Dynamics

This study highlights the critical importance of aligning Croatian military relations with contemporary public communication trends. It advocates for establishing swift, transparent, and interactive communication strategies to cultivate and enhance public support for military endeavors. The traditional military public relations framework has exhibited rigidity, characterized by sporadic advertising initiatives confined to narrow domains. The initial focus involves conducting a scientific content analysis of social media platforms to identify emergent channels that significantly disseminate information and promote awareness of recruitment strategies. The second objective is to investigate the factors influencing the public perception of Croatian soldiers and the overall branding of the Armed Forces of Croatia. This exploration will scrutinize the methodologies and tools employed for effectively promoting domestic and international military structures by 2025. As Croatia anticipates the introduction of mandatory military service in January 2024, understanding the information platforms favored by younger generations becomes imperative. Future strategies should leverage innovative, competitive, and youth-oriented communication channels to engage the younger working-age demographic, thereby attracting potential recruits to the Armed Forces as a credible institution that offers advantageous working conditions and professional growth opportunities.

A Comparative Analysis of Artificial Intelligence Platforms: ChatGPT-4o and Google Gemini in Answering Questions About Birth Control Methods.

Background Birth control methods (BCMs) are often underutilized or misunderstood, especially among young individuals entering their reproductive years. With the growing reliance on artificial intelligence (AI) platforms for health-related information, this study evaluates the performance of ChatGPT-4o and Google Gemini in addressing commonly asked questions about BCMs. Methods Thirty questions, derived from the American College of Obstetrics and Gynecologists (ACOG) website, were posed to both AI platforms. Questions spanned four categories: general contraception, specific contraceptive types, emergency contraception, and other topics. Responses were evaluated using a five-point rubric assessing Relevance, Completeness, and Lack of False Information (RCL). Overall scores were calculated by averaging the rubric scores. Statistical analysis, including the Wilcoxon Signed-Rank test, Friedman test, and Kruskal-Wallis test, was performed to compare metrics. Results ChatGPT-4o and Google Gemini provided high-quality responses to birth control-related queries, with overall scores averaging 4.38 ± 0.58 and 4.37 ± 0.52, respectively, both categorized as "very good" to "excellent." ChatGPT-4o demonstrated higher scores in the lack of false information, based on descriptive statistics (4.70 ± 0.60 vs. 4.47 ± 0.73), while Google Gemini outperformed in relevance, with a statistically significant difference (4.53 ± 0.57 vs. 4.30 ± 0.70, p = 0.035, large effect size). Completeness scores were comparable (p = 0.655). Statistical analyses revealed no significant differences in overall performance (p = 0.548), though Google Gemini demonstrated a potential trend of stronger performance in the "Other Topics" category. Within-model variability showed ChatGPT-4o had more pronounced differences among metrics (moderate effect size, Kendall's W = 0.357), while Google Gemini exhibited smaller variability (Kendall's W = 0.165). These findings suggest that both platforms offer reliable and complementary tools for addressing knowledge gaps in contraception, with nuanced strengths that warrant further exploration. Conclusions ChatGPT-4o and Google Gemini provided reliable and accurate responses to BCM-related queries, with slight differences in strengths. These findings underscore the potential of AI tools, in addressing public health information needs, particularly for young individuals seeking guidance on contraception. Further studies with larger datasets may elucidate nuanced differences between AI platforms.