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Summary: Objective: The main objective of this study was to examine the relationship between mobility patterns during the coronavirus disease 2019 (COVID-19) pandemic and orthopedic trauma patients in Austria. Utilizing global positioning system (GPS)-based mobility data, the attempt was to assess both the impact of COVID-19 lockdowns on reducing orthopedic trauma patients and the degree of compliance to the imposed movement restrictions. Methods: This retrospective analysis included all patients (283,501) treated at 3 major level I trauma centers in Austria. Analyzed time periods were 1 January 2019 to 8 February 2021. Freely available GPS-based mobility data from Google and Apple Inc. was gathered. Results: A moderate to strong correlation between the cumulative average outpatients and the assessed mobility index was observed for all cities (Google: r = 0.70 p < 0.001, 95% confidence interval, CI: 0.67–0.73; Apple: r = 0.64 p < 0.001, 95% CI: 0.61–0.67). A significant linear regression equation was found for Vienna (adjusted r2 = 0.48; F(1, 350) = 328,05; p < 0.01). During the first lockdown there was a drastic decline in mobility (up to −75.36%) and in numbers of orthopedic trauma outpatients (up to −64%, from 153 patients/day 2019 to 55 patients/day 2020) in comparison to the prepandemic era. The decline diminished as time passed. Conclusion: Analyses of GPS-based mobility patterns show a correlation with trauma patient numbers. These findings can be used to develop prediction models, leading to better resource planning and public health policy, enhancing patient care and cost-effectiveness, especially in the event of future pandemics. Furthermore, the results suggest that compliance to mobility restrictions decreased over time during the COVID-19 pandemic, resulting in increased mobility and trauma patients. [ABSTRACT FROM AUTHOR]

Study Design: Comparative study. Objectives: This study aims to compare Google and GPT-4 in terms of (1) question types, (2) response readability, (3) source quality, and (4) numerical response accuracy for the top 10 most frequently asked questions (FAQs) about anterior cervical discectomy and fusion (ACDF). Methods: "Anterior cervical discectomy and fusion" was searched on Google and GPT-4 on December 18, 2023. Top 10 FAQs were classified according to the Rothwell system. Source quality was evaluated using JAMA benchmark criteria and readability was assessed using Flesch Reading Ease and Flesch-Kincaid grade level. Differences in JAMA scores, Flesch-Kincaid grade level, Flesch Reading Ease, and word count between platforms were analyzed using Student's t-tests. Statistical significance was set at the.05 level. Results: Frequently asked questions from Google were varied, while GPT-4 focused on technical details and indications/management. GPT-4 showed a higher Flesch-Kincaid grade level (12.96 vs 9.28, P =.003), lower Flesch Reading Ease score (37.07 vs 54.85, P =.005), and higher JAMA scores for source quality (3.333 vs 1.800, P =.016). Numerically, 6 out of 10 responses varied between platforms, with GPT-4 providing broader recovery timelines for ACDF. Conclusions: This study demonstrates GPT-4's ability to elevate patient education by providing high-quality, diverse information tailored to those with advanced literacy levels. As AI technology evolves, refining these tools for accuracy and user-friendliness remains crucial, catering to patients' varying literacy levels and information needs in spine surgery. [ABSTRACT FROM AUTHOR]

Geographical research is increasingly focused on how digital technology shapes human-nature relations. This article explores how internet search engines and their associated algorithms and indexing technologies order and produce homogenising accounts of forest places. We put forward 'un-indexing' as a critical and inventive method for un-ordering and re-ordering search engine results to complicate digital perspectives on forest-society relations. We present Everything at the Forest Park, a series of four speculative catalogues we created to invite collective inquiries into the digital mediation of a forested area in Scotland – Queen Elizabeth Forest Park. Fostering a slower form of engagement with web material, the catalogues suggest how geographers and other scholars might critically repurpose, reappropriate and interrogate the algorithmically curated and advertising-oriented orderings of search engines to foster more careful and convivial forest-society relations. [ABSTRACT FROM AUTHOR]

Facing a policy issue, citizens use search engines such as Google to seek political information. Although some scholars have expressed concern that higher user control, and high choice might induce selectivity, existing literature has neglected the role of search terms in the echo chamber debate. This study applied two cross-section surveys during two referendum votes to expose respondents to mock Google webpages (N = 728; N = 820). With thematic coding analysis and logistic regressions, the study indicates that citizens rarely use the search bar to search for only like-minded information sources and that individual-level characteristics are not drivers of search terms. Though search terms foresee self-selection in the results' page for some motivated citizens, ranking remains the main driver of self-selection for most citizens. [ABSTRACT FROM AUTHOR]

When women receive a diagnosis of a gynecologic malignancy, they can have questions about their diagnosis or treatment that can result in voice queries to virtual assistants for more information. Recent advancement in artificial intelligence (AI) has transformed the landscape of medical information accessibility. The Google virtual assistant (VA) outperformed Siri, Alexa and Cortana in voice queries presented prior to the explosive implementation of AI in early 2023. The efforts presented here focus on determining if advances in AI in the last 12 months have improved the accuracy of Google VA responses related to gynecologic oncology. Previous questions were utilized to form a common basis for queries prior to 2023 and responses in 2024. Correct answers were obtained from the UpToDate medical resource. Responses related to gynecologic oncology were obtained using Google VA, as well as the generative AI chatbots Google Bard/Gemini and Microsoft Bing-Copilot. The AI narrative responses varied in length and positioning of answers within the response. Google Bard/Gemini achieved an 87.5% accuracy rate, while Microsoft Bing-Copilot reached 83.3%. In contrast, the Google VA's accuracy in audible responses improved from 18% prior to 2023 to 63% in 2024. While the accuracy of the Google VA has improved in the last year, it underperformed Google Bard/Gemini and Microsoft Bing-Copilot so there is considerable room for further improved accuracy. [ABSTRACT FROM AUTHOR]

In early 2017, a journalist and search engine expert wrote about "Google's biggest ever search quality crisis." Months later, Google hired him as the first Google "Search Liaison" (GSL). By October 2021, when someone posted to Twitter a screenshot of misleading Google Search results for "had a seizure now what," users tagged the Twitter account of the GSL in reply. The GSL frequently publicly interacts with people who complain about Google Search on Twitter. This article asks: what functions does the GSL serve for Google? We code and analyze 6 months of GSL responses to complaints on Twitter. We find that the three functions of the GSL are: (1) to naturalize the logic undergirding Google Search by defending how it works, (2) perform repair in responses to complaints, and (3) boundary drawing to control critique. This advances our understanding of how dominant technology companies respond to critiques and resist counter-imaginaries. [ABSTRACT FROM AUTHOR]

The effectiveness of implementing various audience response system (ARS) formats in large classes to engage students, positively influence academic outcomes, and align with their preferences for different types of activities lacked sufficient information. This study aimed to (1) evaluate students' perspectives on different ARS formats, including Kahoot for individual (Kahoot-I) and team (Kahoot-T) play, and Google Forms and Google Sheets for multiple-choice questions (Google-MCQ) and for open-ended questions (Google-OEQ) on different aspects with/without subgroup analysis of students into quartiles (Q1-lowest-Q4-highest) of summative scores, and (2) determine correlations between students' perspectives and academic outcomes. At the course's final class, a QR code for a 5-point Likert scale questionnaire was displayed for all enrolled medical students. 269/312 students (86.22%) provided written consent and completed the questionnaire constructed based on dimensions of student engagement. Across all ARS formats, students rated scores ≥ 4 for almost all aspects, with participation in class receiving the highest scores. For each aspect, Kahoot-T was rated highest in various aspects and Google-MCQ for "understanding" and Google-OEQ for "asking/answering question". Higher academic achievers expressed more "enthusiasm" for Kahoot-I than lower achievers (P < 0.05). Formative and summative scores were positively correlated with many aspects. Students reported high engagement with all ARS formats, with higher academic achievers rating them with higher scores. Kahoot-T was the most favored for multiple aspects, Google-MCQ for "understanding" and Google-OEQ for interactive "asking/answering question". Thus, selecting ARS in alignment with learning objective could enhance students' engagement and learning outcomes. [ABSTRACT FROM AUTHOR]

Samuel A Cohen,1 Nikhita Yadlapalli,2 Jonathan D Tijerina,3 Chrisfouad R Alabiad,3 Jessica R Chang,4 Benyam Kinde,5 Nicholas R Mahoney,6 Kelsey A Roelofs,1 Julie A Woodward,7 Andrea L Kossler5 1Department of Ophthalmology, Stein Eye Institute at University of California Los Angeles David Geffen School of Medicine, Los Angeles, CA, USA; 2Department of Ophthalmology, FIU Herbert Wertheim College of Medicine, Miami, FL, USA; 3Department of Ophthalmology, Bascom Palmer Eye Institute at University of Miami Miller School of Medicine, Miami, FL, USA; 4Department of Ophthalmology, USC Roski Eye Institute at University of Southern California Keck School of Medicine, Los Angeles, CA, USA; 5Department of Ophthalmology, Byers Eye Institute at Stanford University School of Medicine, Palo Alto, CA, USA; 6Department of Ophthalmology, Wilmer Eye Institute at Johns Hopkins University School of Medicine, Baltimore, MD, USA; 7Department of Ophthalmology, Duke Eye Center at Duke University School of Medicine, Durham, NC, USACorrespondence: Andrea L Kossler, Department of Ophthalmology, Byers Eye Institute at Stanford University School of Medicine, 2452 Watson Court, Palo Alto, CA, USA, Email akossler@stanford.eduPurpose: To compare the accuracy and readability of responses to oculoplastics patient questions provided by Google and ChatGPT. Additionally, to assess the ability of ChatGPT to create customized patient education materials.Methods: We executed a Google search to identify the 3 most frequently asked patient questions (FAQs) related to 10 oculoplastics conditions. FAQs were entered into both the Google search engine and the ChatGPT tool and responses were recorded. Responses were graded for readability using five validated readability indices and for accuracy by six oculoplastics surgeons. ChatGPT was instructed to create patient education materials at various reading levels for 8 oculoplastics procedures. The accuracy and readability of ChatGPT-generated procedural explanations were assessed.Results: ChatGPT responses to patient FAQs were written at a significantly higher average grade level than Google responses (grade 15.6 vs 10.0, p < 0.001). ChatGPT responses (93% accuracy) were significantly more accurate (p < 0.001) than Google responses (78% accuracy) and were preferred by expert panelists (79%). ChatGPT accurately explained oculoplastics procedures at an above average reading level. When instructed to rewrite patient education materials at a lower reading level, grade level was reduced by approximately 4 (15.7 vs 11.7, respectively, p < 0.001) without sacrificing accuracy.Conclusion: ChatGPT has the potential to provide patients with accurate information regarding their oculoplastics conditions. ChatGPT may also be utilized by oculoplastic surgeons as an accurate tool to provide customizable patient education for patients with varying health literacy. A better understanding of oculoplastics conditions and procedures amongst patients can lead to informed eye care decisions.Keywords: oculoplastics, google, ChatGPT, readability, accuracy

Australia's News Media Bargaining Code requires Google and Facebook to negotiate payments with news publishers for news content appearing on the platforms. Facebook and Google lobbied against the code through a highly visible public-facing campaign which included a series of blogs, videos and pop-up communications across their interfaces including News Feeds, Google Search and Home Page, and You Tube, and culminated in Facebook banning Australian users from accessing Australian news and related content. This article presents the findings of a detailed study of platform discourse in response to the News Media Bargaining Code, using critical discourse analysis, and drawing on theoretical frameworks from Althusser, Foucault and Chun. It also investigates the role of the user interface in platform power, particularly how platform users are interpellated by digital platforms. The findings suggest Facebook and Google's discursive strategies were deployed to protect, strengthen and enforce platform sovereignty. The case study offers lessons for platform regulation globally in understanding how platforms respond to legislation. [ABSTRACT FROM AUTHOR]

Interest in intangibles has become increasingly important in recent decades. Intangible assets have gradually become the most important sources of competitive advantage. The traditional factors of production (material resources, labor, capital) have gradually diminished in importance. At the same time the importance of intangible assets such as knowledge, information, creativity has increased. Before the organization can grow it has to learn. For most organizations, the ability to create a sustainable future is directly proportional to how well they develop the intellectual capabilities of their employees. Organizations that use their knowledge as a source of competitive advantage are referred to as "knowledge-based organizations". In this paper we analyze the performance of Alphabet Inc. (Google)-a company with a global footprint in the development of the knowledge-based economy.

We have chosen this theme because the knowledge-based economy is already the present, and the ICT sector is the optimal infrastructure for its development, so that knowledge, creativity and innovation, as well as intellectual capital, are accessible to all. In this context, we considered it necessary to analyze the extent to which individuals have been concerned to develop their knowledge, skills and creativity with the services of Alphabet Inc. (Google).

E-commerce has nowadays become a type of business that the younger generation prefer rather than industrial business due to the little costs of developing. The efficiency and fast turnaround time of a little investment in an e-commerce business makes people oriented towards. The reason for choosing the beauty industry for our analysis is that the industry gained numerous new white label products launched by various brands lately therefore e-commences embrace them and promote them properly. The beauty industry has now a greater number of consumers due to the various benefits such as skin care routine and careful face hygiene. Korea is one of the countries that produce and develop a strong industry having advanced technology for producing high quality beauty products. Romania is an import country of beauty products with an enormous interest in Korean skin care products and cosmetics. The two countries developed the idea of cosmetics business using online methods and e-commerce platforms. The purpose of this analysis is to find the differences of promoting an e-commerce of beauty products on different platforms such as Naver and Google while outlining the tendency of customers behaviors in two totally different societies but also the way businesses can develop online while optimizing using specific platform engines so they can improve their performances. Therefore, we analyze a literature review on the beauty industry development, how this proceeds online during and after pandemic and how important are those for the Romania and Korea population. The research will ensure a better view of both platforms Naver and Google and how those are helping the e-commerce business in the beauty industry. We will see a proper comparison between the society’s approach for e-commerce in both countries and an applied example on how this works for each culture.

When women receive a diagnosis of a gynecologic malignancy, they can have questions about their diagnosis or treatment that can result in voice queries to virtual assistants for more information. Recent advancement in artificial intelligence (AI) has transformed the landscape of medical information accessibility. The Google virtual assistant (VA) outperformed Siri, Alexa and Cortana in voice queries presented prior to the explosive implementation of AI in early 2023. The efforts presented here focus on determining if advances in AI in the last 12 months have improved the accuracy of Google VA responses related to gynecologic oncology. Previous questions were utilized to form a common basis for queries prior to 2023 and responses in 2024. Correct answers were obtained from the UpToDate medical resource. Responses related to gynecologic oncology were obtained using Google VA, as well as the generative AI chatbots Google Bard/Gemini and Microsoft Bing-Copilot. The AI narrative responses varied in length and positioning of answers within the response. Google Bard/Gemini achieved an 87.5% accuracy rate, while Microsoft Bing-Copilot reached 83.3%. In contrast, the Google VA’s accuracy in audible responses improved from 18% prior to 2023 to 63% in 2024. While the accuracy of the Google VA has improved in the last year, it underperformed Google Bard/Gemini and Microsoft Bing-Copilot so there is considerable room for further improved accuracy.

New Panjang Jiwo Resort, located in Bogor Regency, is a popular tourist destination for both domestic and foreign tourists. Despite various marketing activities, the resort has faced negative reviews and fluctuating visitor numbers. To maintain its sustainability, the resort needs to employ a right marketing strategy and good service quality to increase customer satisfaction and loyalty. This research aims to identify customer characteristics of New Panjang Jiwo Resort, analyze the effect of the marketing mix and service quality on customer satisfaction and loyalty. The sample consisted of 155 respondents aged 26-35 years, with 60% being women. The average domicile of visitors at the resort is from Jabodetabek and works as a private employee with a lower middle income income category. The results showed that the overall variables of the marketing mix, service quality, customer satisfaction, and consumer loyalty at New Panjang Jiwo Resort are included in the high category. The marketing mix variable had the highest average value compared to other variables. SEM-PLS analysis showed that the marketing mix and service quality had a positive and significant effect on customer satisfaction and satisfaction had a positive and significant effect on loyalty. The managerial implications of this research include building road directions at every crossroads along the main road to the resort, regular maintenance of room air conditioning, and conducting training to improve the quality of work provided by employees. This will help ensure that the resort's services are more tailored to the wishes and needs of customers. [ABSTRACT FROM AUTHOR]

Purpose: Patients may seek online information to better understand medical imaging procedures. The purpose of this study was to assess the accuracy of information provided by 2 popular artificial intelligence (AI) chatbots pertaining to common imaging scenarios' risks, benefits, and alternatives. Methods: Fourteen imaging-related scenarios pertaining to computed tomography (CT) or magnetic resonance imaging (MRI) were used. Factors including the use of intravenous contrast, the presence of renal disease, and whether the patient was pregnant were included in the analysis. For each scenario, 3 prompts for outlining the (1) risks, (2) benefits, and (3) alternative imaging choices or potential implications of not using contrast were inputted into ChatGPT and Bard. A grading rubric and a 5-point Likert scale was used by 2 independent reviewers to grade responses. Prompt variability and chatbot context dependency were also assessed. Results: ChatGPT's performance was superior to Bard's in accurately responding to prompts per Likert grading (4.36 ± 0.63 vs 3.25 ± 1.03 seconds, P <.0001). There was substantial agreement between independent reviewer grading for ChatGPT (κ = 0.621) and Bard (κ = 0.684). Response text length was not statistically different between ChatGPT and Bard (2087 ± 256 characters vs 2162 ± 369 characters, P =.24). Response time was longer for ChatGPT (34 ± 2 vs 8 ± 1 seconds, P <.0001). Conclusions: ChatGPT performed superior to Bard at outlining risks, benefits, and alternatives to common imaging scenarios. Generally, context dependency and prompt variability did not change chatbot response content. Due to the lack of detailed scientific reasoning and inability to provide patient-specific information, both AI chatbots have limitations as a patient information resource. [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI) and large language models (LLMs) offer a new potential resource for patient education. The answers by Chat Generative Pre-Trained Transformer (ChatGPT), a LLM AI text bot, to frequently asked questions (FAQs) were compared to answers provided by a contemporary Google search to determine the reliability of information provided by these sources for patient education in upper extremity arthroplasty. "Total shoulder arthroplasty" (TSA) and "total elbow arthroplasty" (TEA) were entered into Google Search and ChatGPT 3.0 to determine the ten most FAQs. On Google, the FAQs were obtained through the "people also ask" section, while ChatGPT was asked to provide the ten most FAQs. Each question, answer, and reference(s) cited were recorded. A modified version of the Rothwell system was used to categorize questions into 10 subtopics: special activities, timeline of recovery, restrictions, technical details, cost, indications/management, risks and complications, pain, longevity, and evaluation of surgery. Each reference was categorized into the following groups: commercial, academic, medical practice, single surgeon personal, or social media. Questions for TSA and TEA were combined for analysis and compared between Google and ChatGPT with a 2 sample Z-test for proportions. Overall, most questions were related to procedural indications or management (17.5%). There were no significant differences between Google and ChatGPT between question categories. The majority of references were from academic websites (65%). ChatGPT produced a greater number of academic references compared to Google (80% vs. 50%; P =.047), while Google more commonly provided medical practice references (25% vs. 0%; P =.017). In conjunction with patient-physician discussions, AI LLMs may provide a reliable resource for patients. By providing information based on academic references, these tools have the potential to improve health literacy and improved shared decision making for patients searching for information about TSA and TEA. With the rising prevalence of AI programs, it is essential to understand how these applications affect patient education in medicine. [ABSTRACT FROM AUTHOR]

The article analyzes search queries in the Russian and American segments of Google. The author proposes a methodology for selecting and classifying search queries that reflect investor sentiment, which potentially influence the activity of the population in the financial market. The article calculates sentiment indices for the United States and Russia, demonstrating a high correlation with the national stock indices S&P500 and IMOEX. The author summarizes that financial market quotes can be determined as indicators of investor sentiment, which in turn are formed on the basis of a complex of economic and noneconomic factors. [ABSTRACT FROM AUTHOR]

Creativity is a key source of strategic advantage in nowadays knowledge-based society. However, reactive paradigms still dominate innovation theory: solutions follow well-known problems. But triggering radical innovation requires proactive creativity, implying using information to constantly find new problems to solve. Led by Google, personalization strategies use algorithms to obtain filtered information, meeting particular users’ expected preferences. This phenomenon, largely neglected by management research and practice, has received substantial criticism, around the concerns that it impoverishes social discourse, slows down human brain thinking capability, and eventually reduces creativity and innovation potential. Indeed, research has confirmed the positive impact that exposure to numerous and divergent external stimuli has on individual creativity. Thought-provoking questions emerge: Will customized search outputs affect companies’ competitive capabilities? If personalized search results reduce the diversity and overall wealth of stimuli received, will creativity and innovation in organizations be diminished? How can managers prevent this potentially dangerous process? [ABSTRACT FROM AUTHOR]

At the onset of the first wave of COVID‐19 in the United States, the pandemic's effect on future birthrates was unknown. In this paper, we assess whether digital trace data—often touted as a panacea for traditional data scarcity—held the potential to accurately predict fertility change caused by the COVID‐19 pandemic in the United States. Specifically, we produced state‐level, dynamic future predictions of the pandemic's effect on birthrates in the United States using pregnancy‐related Google search data. Importantly, these predictions were made in October 2020 (and revised in February 2021), well before the birth effect of the pandemic could have possibly been known. Our analysis predicted that between November 2020 and February 2021, monthly United States births would drop sharply by approximately 12 percent, then begin to rebound while remaining depressed through August 2021. While these predictions were generally accurate in terms of the magnitude and timing of the trough, there were important misses regarding the speed at which these reductions materialized and rebounded. This ex post evaluation of an ex ante prediction serves as a powerful demonstration of the "promise and pitfalls" of digital trace data in demographic research. [ABSTRACT FROM AUTHOR]

While communication infrastructure was under development in the country, modern communication tools in the form of cyberspace gained increased usability and hence spread rapidly. In this process, while facilitating communication, new opportunities and threats are being inherently introduced, though this has not been followed up with regard to the ability of this process to create and exacerbate security crises in the country, commensurate with the technical development of these infrastructures. This puts Google in a peculiar position, since it has several services and products online. Their importance relates to the emphasis that the Supreme Leader has put on this research, and it relates to the national security of the Islamic Republic of Iran. The general question of this research is that, "What are the capabilities of the cyberspace Google to create and expand security crises in the country?" This research, owing to its nature, is an applied research based on a descriptive-analytical method where in the collection of data; the method of a documentary study has been used. It also used a qualitative method for data analysis and did thematic analysis. Among various products of Google, which have been used by people in Iran, several instances that are most consumed are selected and analyzed based on the question of the research. Some of the most significant threats which are connected with the most known and widely used Google services include: espionage, directing subversive actions, assassination, contamination, disruption of crisis management, undermining of the moral grounds in society, creation of conditions for, initiation, expansion, and acceleration of public discontent, determination of the grounds for crises and disturbances, acting as a catalyst and accelerator of crises, providing anti-security training, identifying the supply and demand market and critical inventories. [ABSTRACT FROM AUTHOR]

Scientists and regular citizens alike search for ways to manage the widespread effects of the COVID-19 pandemic. While scientists are busy in their labs, other citizens often turn to online sources to report their experiences and concerns and to seek and share knowledge of the virus. The text generated by those users in online social media platforms can provide valuable insights about evolving users' opinions and attitudes. The objective of this research is to analyze text of such user disclosures to study human communication during a pandemic in four primary ways. First, we analyze Twitter tweet information, generated throughout the pandemic, to understand users' communications concerning COVID-19 and how those communications have evolved during the pandemic. Second, we analyze linguistic sentiment concepts (analytic, authentic, clout, and tone concepts) in different Twitter settings (sentiment in tweets with pictures or no pictures and tweets versus retweets). Third, we investigate the relationship between Twitter tweets with additional forms of internet activity, namely, Google searches and Wikipedia page views. Finally, we create and use a dictionary of specific COVID-19-related concepts (e.g., symptom of lost taste) to assess how the use of those concepts in tweets are related to the spread of information and the resulting influence of Twitter users. The analysis showed a surprisingly lack of emotion in the initial phases of the pandemic as people were information seeking. As time progressed, there were more expressions of sentiment, including anger. Further, tweets with and without pictures and/or video had statistically significant differences in text sentiment characteristics. Similarly, there were differences between the sentiment in tweets and retweets and tweets. We also found that Google and Wikipedia searches were predictive of sentiment in the tweets. Finally, a variable representing a dictionary of COVID-related concepts was statistically significant when related to users' Twitter influence score and number of retweets, illustrating the general impact of COVID-19 on Twitter and human communication. Overall, the results provide insights into human communication as well as models of human internet and social media use. These findings could be useful for the management of global challenges beyond, or different from, a pandemic. [ABSTRACT FROM AUTHOR]

How important is Google for scientific research? This paper exploits the exogenous shock represented by Google's sudden withdrawal of its services from mainland China to assess the importance of access to information for the knowledge production function of scientific scholars in the field of economics. For economists, a type of scholar with a simple knowledge production function, results from difference-in-difference analyses, which compare their scientific output to scholars located in the neighbouring regions, show that the scientific productivity declines by about 28% in volume and 30% in terms of citations. These results are consistent with the view that information accessibility is an important driver of scientific progress. Considering that the negative effect of the shock is stronger for top scholars located in China, Google's sudden exit bears the risk that researchers lose touch with the research frontier and persistently lag behind their foreign peers. [ABSTRACT FROM AUTHOR]

The development of the Internet, though it brings many benefits, it also brings threats to rights and freedoms and the right to privacy is usually its 'prey'. Internet search engines are one of the most important factors that shape the web. The establishment of the right to be forgotten as an element allowing to profile one's image on the web was a milestone in granting protection in the on-line world. The problem of the autocomplete function stays on the margin of mainstream discussions. This function, seemingly only technical, in practice affects web traffic directions. This article presents the impact of the autocomplete function on privacy and a discussion on whether this impact is significant enough for relevant protection mechanisms to be necessary. The research aim is to answer the following question: should the right to control the autocomplete function be protected and if yes, on what terms? [ABSTRACT FROM AUTHOR]

This paper provides an analysis of working from home patterns in England using data from the 2021 Census to understand (1) how patterns of working from home (WFH) in England have shifted since the COVID‐19 pandemic and (2) whether human mobility indicators, specifically Google Community Mobility Reports, provide a reliable proxy for WFH patterns recorded by the 2021 Census, providing a formal evaluation of the reliability of such datasets, whose applications have grown exponentially over the COVID‐19 pandemic. We find that WFH patterns recorded by the 2021 Census were unique compared with previous UK censuses, reflecting an unprecedented increase likely caused by persistent changes to employment during the COVID‐19 pandemic, with a clear social gradient emerging across the country. We also find that Google mobility in 'Residential' and 'Workplace' settings provides a reliable measurement of the distribution of WFH populations across Local Authorities, with varying uncertainties for mobility indicators collected in different settings. These findings provide insights into the utility of such datasets to support population research in intercensal periods, where shifts may be occurring, but can be difficult to quantify empirically. [ABSTRACT FROM AUTHOR]

Background & Aims: Chronic liver diseases belong to the most common diseases worldwide and are associated with increased morbidity and mortality. Although more than one in three adults are estimated to have metabolic dysfunction-associated steatotic liver disease (MASLD), awareness of this condition is low amongst the general public, health care professionals and policy makers. However, meaningful knowledge transfer is essential for raising awareness and improving prevention and treatment. This study set out to investigate the use of the major internet search engine to understand how knowledge transfer has evolved by analyzing liver-related searches trends. Methods: We investigated Google search trends by measuring the number of hits relating to liver diseases between 2004 and 2021 in seven languages and European countries but also worldwide. All analyses were performed in R using the R Google trends package gtrendsR. Results: We found that interest in MASLD [formerly non-alcoholic fatty liver disease (NAFLD)] has generally increased over time, but that interest in metabolic associated steatohepatitis (MASH) - the most severe form of MASLD - has decreased. Interest in viral hepatitis C has decreased, whereas the number of queries regarding viral hepatitis B have been stable but dominated by interest in vaccination for it. Recent medical developments (in viral hepatitis) did not lead to a noticeable change in overall search behavior. Users preferred searching using their native language and less complex medical terms and acronyms (e.g., fatty liver instead of NAFLD). Conclusions: In the last two decades, Google search trends have followed the general development in the field of hepatology. Searches were dominated by non-experts and are not being rapidly influenced by novel scientific developments. Also, users preferred search terms in their native languages rather than English and tended to avoid complex medical search terms. Awareness and communication strategies around MASLD should consider these preferences when addressing the general public. [ABSTRACT FROM AUTHOR]

The ship tracking and mapping capabilities that geospatial technology provides create an opportunity to observe fishing vessels as they move through established maritime boundaries. This paper connects data availability to ground-truthing research and explores the limits of vessel movement mapping in representing worker abuse at sea through three related themes. First, a conceptual background links the advancements in maritime remote sensing to critical GIS scholarship and provides a background on worker abuse aboard Taiwanese fishing vessels. Second, the paper examines the potential of machine learning algorithms to represent worker abuse at sea, arguing that more extensive ground-truthing research with workers could help address variations in the data and limited data sets. Third, I use remote sensing data to identify and unpack Taiwanese fishing across the three EEZs with the most concentrated Taiwanese fishing activity: starting with Taiwan, followed by the Falkland Islands, and Seychelles. I argue that fishing activity and the digital representation of vessel movements are governed by terrestrial geopolitics and subject to manipulation by ship captains. Finally, the conclusion offers recommendations for how future research can capitalize on the capabilities of AIS, particularly with respect to addressing problems of working conditions and abuse at sea. [ABSTRACT FROM AUTHOR]

Objective: To quantitatively compare online patient education materials found using traditional search engines (Google) versus conversational Artificial Intelligence (AI) models (ChatGPT) for benign paroxysmal positional vertigo (BPPV). Study Design: The top 30 Google search results for "benign paroxysmal positional vertigo" were compared to the OpenAI conversational AI language model, ChatGPT, responses for 5 common patient questions posed about BPPV in February 2023. Metrics included readability, quality, understandability, and actionability. Setting: Online information. Methods: Validated online information metrics including Flesch‐Kincaid Grade Level (FKGL), Flesch Reading Ease (FRE), DISCERN instrument score, and Patient Education Materials Assessment Tool for Printed Materials were analyzed and scored by reviewers. Results: Mean readability scores, FKGL and FRE, for the Google webpages were 10.7 ± 2.6 and 46.5 ± 14.3, respectively. ChatGPT responses had a higher FKGL score of 13.9 ± 2.5 (P <.001) and a lower FRE score of 34.9 ± 11.2 (P =.005), both corresponding to lower readability. The Google webpages had a DISCERN part 2 score of 25.4 ± 7.5 compared to the individual ChatGPT responses with a score of 17.5 ± 3.9 (P =.001), and the combined ChatGPT responses with a score of 25.0 ± 0.9 (P =.928). The average scores of the reviewers for all ChatGPT responses for accuracy were 4.19 ± 0.82 and 4.31 ± 0.67 for currency. Conclusion: The results of this study suggest that the information on ChatGPT is more difficult to read, of lower quality, and more difficult to comprehend compared to information on Google searches. [ABSTRACT FROM AUTHOR]

This study aimed to identify the quality of web pages retrieved by Parsijoo and Parseek Persian search engines and Google to respond to students' course-related queries. This study was conducted using evaluations from a researcher-made checklist. The first population included Persian and Google search engines selected from Persian, Parsijoo, and Parseek search engines. The second population included the keywords of the section for collecting data from the fourth, fifth, and sixth-grade elementary school science textbooks, from which 12 keywords were selected by simple random sampling. The third population included web pages retrieved for each keyword by search engines. From the first ten results retrieved in each keyword search, relevant pages were selected as the sample. The results revealed that the quality of the web pages retrieved by all three search engines, i.e., Parsijoo, Parseek, and Google, was above average. However, there was a significant difference between the engines regarding the number of relevant results. This is the first study investigating the quality of web pages retrieved by search engines to respond to students' course-related queries. The study results can help the designers and developers of Persian search engines improve their performance in retrieving high-quality pages and gaining a proper place among Iranian users.

Abstract Due to the ongoing global debate regarding the relationship between fintech and banks, including developing countries, this study aims to investigate this relationship in the case of Vietnam, an emerging nation. The study analyzes the relationship between fintech search and bank stock returns, which are measures of fintech and banks, respectively. The time series data for fintech and bank stock returns were obtained from Google Trends and Vietstock, respectively. Exploratory factor analysis was utilized to derive the fintech variables, while the bank stock return variable was calculated using a basket of eight listed banks from 2017w46 to 2021w46. The results were estimated using the vector autoregression and Granger causality method and validated with the copula method. A key finding of this study is the presence of a simultaneous negative change and bidirectional causality between bank stock returns and fintech lending. Furthermore, several other interesting findings were discovered: (1) the causal relationship from fintech to bank stock returns is weaker compared with the opposite direction; (2) unidirectional causality exists between different types of fintech, such as influence from FinFintech to FinLending, from FinPayment to FinLending and FinWallet, from FinMoney to FinFintech, from FinWallet to FinLending, and from FinProduct to FinFintech; and (3) there is an equal occurrence of simultaneous increase or decrease between bank stock returns and certain types of fintech, specifically between BankReturn and FinPayment, BankReturn and FinLending, as well as BankReturn and FinWallet. These findings shed light on the complex relationship between fintech and banks, offering insights that contribute to our understanding of this dynamic interplay in the context of Vietnam’s emerging fintech landscape.

Background: Patients use the Internet to learn information about injuries, yet online content remains largely unstudied. This study analyzed patient questions posed online regarding ulnar collateral ligament (UCL) tears or UCL surgical management. Methods: Three separate search strings about UCL tear and UCL surgery were queried on the Google search engine. The 300 most commonly asked questions were compiled for each topic and associated webpage information was collected from the “People also ask” section. Questions were categorized using the Rothwell classification and webpages by Journal of the American Medical Association (JAMA) benchmark criteria. Results: The most frequent UCL tear questions were “how long does it take to heal a torn UCL?” and “what is nonsurgical treatment for the UCL?” The most frequent UCL surgery question was “can you retear your UCL after surgery?” The Rothwell classification of questions for UCL tear/UCL surgery was 55%/32% policy, 38%/57% fact, and 7%/11% value with highest subcategories being indications/management (46%/25%) and technical details (24%/25%). The most common webpages were academic (39%/29%) and medical practice (24%/26%). Mean JAMA score for all 600 webpages was low (1.2), with journals (mean = 3.4) having the highest score. Medical practice (mean = 0.5) and legal websites (mean = 0.0) had the lowest JAMA scores. Only 30% of webpages provided UCL-specific information. Conclusion: Online UCL patient questions commonly pertain to technical details and injury management. Webpages suggested by search engines contain information specific to UCL tears and surgery only one-third of the time. The quality of most webpages provided to patients is poor, with minimal source transparency.

Anxiety is a widespread and serious mental health issue that has been exacerbated by the COVID-19 pandemic and other stressors. In this study, we explore how online behavior data from Google and YouTube can be used to infer anxiety levels in individuals. We collected and processed digital traces from nearly 100 participants over eight weeks and applied various machine learning techniques to extract features and build predictive models. We found that combining data from multiple media modalities can yield highly accurate predictive models for anxiety as self-reported by a clinical GAD-7 scale (AUC > 0.86). We also found that the semantic categories of online engagement can affect the predictive performance of the models. This study contributes to the field of computational social science and digital mental health and demonstrates the potential of using online behavior data to monitor psychological well-being and design interventions for anxiety.