The public's perception of the nursing profession: Validity and reliability of the Chinese version of the nursing image scale in Taiwan.

The public's perception of the nursing image deeply influences nurses' work and professional development. However, the Taiwanese public's perception of nursing remains unclear. This study aimed to determine the validity and reliability of a Chinese version of the Nursing Image Scale (NIS) in Taiwan. This was a psychometric study using a cross-sectional survey. Participants were recruited via the snowball sampling method through the online community software LINE from August 1 to 13, 2019. After data collection, the construction and validation of the NIS to measure public opinion were assessed, including content validity, corrected item-total correlation, exploratory factor analysis (EFA), and reliability. A total of 1331 valid responses were included in the analysis. After EFA analysis, the 20 scale items were divided across the four domains of prudence and care, innovation and cooperation, efficiency and division, and professionalism and respect. The NIS (Chinese version) was valid and reliable for measuring public opinion and may be used to examine changes in public perceptions of nursing.

Positive public attitudes towards agricultural robots.

Robot technologies could lead to radical changes in farming. But what does the public know and think about agricultural robots? Recent experience with other agricultural technologies-such as plant genetic engineering-shows that public perceptions can influence the pace and direction of innovation, so understanding perceptions and how they are formed is important. Here, we use representative data from an online survey (n = 2269) to analyze public attitudes towards crop farming robots in Germany-a country where new farming technologies are sometimes seen with skepticism. While less than half of the survey participants are aware of the use of robots in agriculture, general attitudes are mostly positive and the level of interest is high. A framing experiment suggests that the type of information provided influences attitudes. Information about possible environmental benefits increases positive perceptions more than information about possible food security and labor market effects. These insights can help design communication strategies to promote technology acceptance and sustainable innovation in agriculture.

Public Perceptions of the Australian Health System During COVID-19: Findings From a 2021 Survey Compared to Four Previous Surveys.

BACKGROUND: This study examines the perceptions of the Australian public canvassed in 2021 during the COVID-19 pandemic about their health system compared to four previous surveys (2008, 2010, 2012 and 2018). METHODS: In 2021, a nationwide online survey was conducted with a representative sample of Australians (N = 5100) recruited via market research panels. The results were compared to previous nationwide Australian survey samples from 2018 (N = 1024), 2012 (N = 1200), 2010 (N = 1201) and 2008 (N = 1146). The survey included questions consistent with previous polls regarding self-reported health status and overall opinions of, and confidence in, the Australian health system. RESULTS: There was an increase in the proportion of respondents reporting positive perceptions at each survey between 2008 and 2021, with a significantly higher proportion of respondents expressing a more positive view of the Australian healthcare system in 2021 compared to previous years (χ2(8, N = 9645) = 487.63, p < 0.001). In 2021, over two-thirds of respondents (n = 3949/5100, 77.4%) reported that following the COVID-19 pandemic, their confidence in the Australian healthcare system had either remained the same (n = 2433/5100, 47.7%) or increased (n = 1516/5100, 29.7%). Overall, respondents living in regional or remote regions, younger Australians (< 45 years) and women held less positive views in relation to the system. In 2021, the most frequently identified area for urgent improvement was the need for more healthcare workers (n = 1350/3576, 37.8%), an area of concern particularly for Australians residing in regional or remote areas (n = 590/1385, 42.6%). CONCLUSIONS: Irrespective of disruptions to the Australian healthcare system caused by the COVID-19 pandemic, Australians' perceptions of their healthcare system were positive in 2021. However, concerns were raised about inadequate workforce capacity and the cost of healthcare, with differences identified by age groups and geographical location. PATIENT OR PUBLIC CONTRIBUTION: Health consumer representatives from the Consumers Health Forum of Australia contributed to the co-design, deployment, analysis and interpretation of the results of this survey. J.A. and L.W. from the Consumers Health Forum of Australia contributed to the development of the paper.

The influence of 'Golden Age' authors of detective fiction, in particular, D. L. Sayers, on the public opinion of dentists and dentistry.

With her wide readership and sympathetic and intelligent handling of dentists and dentistry in her detective novels and short stories, it is probable that Dorothy Leigh Sayers(1893-1957) influenced the public perception of dentists favourably, as being true professionals, particularly after the 1921 Act made it a fully closed specialty. Her encounter with a dental automaton seen when she was very young is acknowledged by her as a formative influence.

Public Views on Whether the Use of Pharmaceutical Neuroenhancements Should Be Allowed.

BACKGROUND AND OBJECTIVES: Some individuals are using drugs to try to enhance cognitive and social-affective functioning and asking physicians for off-label prescriptions for neuroenhancement (e.g., stimulants). Several medical societies released guidance on prescribing neuroenhancers, some of which refer to potential societal effects of neuroenhancement (e.g., distributive justice), besides risks and benefits to users. Which institutions (e.g., medical societies, government, universities) should make decisions on allowing neuroenhancement and whether they should consider potential societal effects are unclear. We examined whether and how much support for allowing pharmaceutical neuroenhancers was influenced by the institution and potential individual and societal effects of neuroenhancers. METHODS: We conducted a discrete-choice experiment using a constructed representative sample of the US adult public. Multinomial logit models were used to analyze the data. RESULTS: Participants (n = 927) demographically resembled the US population. Risks of serious side effects (OR 0.20, CI 0.18-0.22) and a lack of benefits for users (OR 0.31, CI 0.26-0.38) had the largest negative effect on participants' support for allowing neuroenhancers. A risk of mild side effects had a moderate negative effect on participants' support for allowing neuroenhancers (OR 0.67, CI 0.62-0.74) and the prospect of more meaningful, long-lasting benefits for users a moderate positive effect (OR 1.74, CI 1.61-1.87). Positive or negative effects of neuroenhancers on the average well-being of people in society and on equality had moderate effects on participants' support for allowing neuroenhancers. For example, the odds of participants' support for allowing enhancers with a negative effect on societal well-being were around half (OR 0.45, CI 0.40-0.50) and the odds of allowing enhancers that worsen inequality were approximately 40% lower compared with enhancers without such effects (OR 0.62, CI 0.55-0.71). The odds of participants allowing neuroenhancers were slightly (10%) lower if enhancers reduced users' authenticity (OR 0.90, CI 0.84-0.97). The institution regulating neuroenhancers and neuroenhancers providing users with an unfair advantage did not affect participants' decisions. DISCUSSION: When presented with both individual and societal considerations, the public seems to support medical societies and other institutions making policy decisions about neuroenhancers based on risks and benefits for users, as well as, but to a lesser extent, effects on equality and societal well-being.

Understanding public perceptions and discussions on diseases involving chronic pain through social media: cross-sectional infodemiology study.

BACKGROUND: Chronic pain is a highly prevalent medical condition that negatively impacts quality of life and is associated with considerable functional disability. Certain diseases, such as fibromyalgia, headache, paraplegia, neuropathy, and multiple sclerosis, manifest with chronic pain. OBJECTIVE: The aim of this study is to examine the number and type of tweets (original or retweet) related to chronic pain, as well as to analyze the emotions and compare the societal impact of the diseases under study. METHODS: We investigated tweets posted between January 1, 2018, and December 31, 2022, by Twitter users in English and Spanish, as well as the generated retweets. Additionally, emotions were extracted from these tweets and their diffusion was analyzed. Furthermore, the topics most frequently discussed by users were collected. RESULTS: A total of 72,874 tweets were analyzed, including 44,467 in English and 28,407 in Spanish. Paraplegia represented 23.3% with 16,461 of the classified tweets, followed by headache and fibromyalgia with 15,337 (21.7%) and 15,179 (21.5%) tweets, respectively. Multiple sclerosis generated 14,781 tweets (21%), and the fewest tweets were related to neuropathy with 8,830 tweets (12.5%). The results showed that the primary emotions extracted were "fear" and "sadness." Additionally, the reach and impact of these tweets were investigated through the generated retweets, with those related to headaches showing the highest interest and interaction among users. CONCLUSION: Our results underscore the potential of leveraging social media for a better understanding of patients suffering from chronic pain and its impact on society. Among the most frequently encountered topics are those related to treatment, symptoms, or causes of the disease. Therefore, it is relevant to inform the patient to prevent misconceptions regarding their illness.

The Saudi Community View of the Use of Artificial Intelligence in Health Care.

OBJECTIVES: Artificial intelligence (AI) holds the promise to revolutionize the field of medicine and enhance the well-being of countless patients. Its capabilities span various areas, including disease prevention, accurate diagnosis, and the development of innovative treatments. Moreover, AI has the potential to streamline health-care delivery and lower expenses. The community should be aware of the potential applications of AI in health care, so that they can advocate for its development and adoption. Hence, the objective of this study is to assess the community's perspectives regarding the utilization of AI in health care. METHODS: A cross-sectional, questionnaire-based study was conducted in Saudi Arabia during the period of June to October 2023. The questionnaire was distributed to people on various social media platforms using a convenience sampling method. The collected data were analyzed using Statistical Package for the Social Sciences. RESULTS: The study included 771 individuals, with 42.5% having a positive outlook on the use of AI in health care, 31.8% having a neutral view, and 7.5% having a negative view. The only factor associated with a positive opinion was regional differences (P = 0.006). Moreover, participants who used medical apps or programs (P = 0.026), wearables (P = 0.027), felt more confident in using technology (P < 0.001), enjoyed using technology (P < 0.001), found it easier to familiarize themselves with new devices or programs (P < 0.001), and had more knowledge about AI (P < 0.001) had more positive opinions regarding the use of AI in health care. CONCLUSIONS: The study found that most Saudis, especially those who were familiar with the use of technology, support the use of AI in health care, with a positive or neutral view. Yet, targeted campaigns in certain regions are needed to educate the entire community about AI's potential benefits.

Résumé Objectifs:L'intelligence artificielle (IA) promet de révolutionner le domaine de la médecine et d'améliorer le bien-être d'innombrables patients. Ses capacités couvrent divers domaines, notamment la prévention des maladies, le diagnostic précis et le développement de traitements novateurs. En outre, l'IA a le potentiel de rationaliser la prestation de soins de santé et de réduire les dépenses. La communauté devrait être consciente des applications potentielles de l'IA dans les soins de santé, afin qu'elle puisse promouvoir son développement et son adoption. Par conséquent, l'objectif de cette étude est d'évaluer les perspectives de la communauté concernant l'utilisation de l'IA dans les soins de santé.Méthodes:Une étude transversale, fondée sur des questionnaires, a été menée en Arabie Saoudite entre juin et octobre 2023. Le questionnaire a été distribué à des personnes sur diverses plateformes de médias sociaux en utilisant une méthode d'échantillonnage de commodité. Les données recueillies ont été analysées à l'aide du package statistique pour les sciences sociales.Résultats:771 personnes ont participé à l'étude, dont 42,5 % avaient un point de vue positif sur l'utilisation de l'IA dans les soins de santé, 31,8 % étaient neutres et 7,5 % étaient négatifs. Le seul facteur associé à une opinion positive était les différences régionales ( P = 0,006). En outre, les participants qui utilisaient des applications ou des programmes médicaux ( P = 0,026), des appareils portatifs (P = 0,027), se sentaient plus confiants dans l'utilisation de la technologie ( P < 0,001), particulièrement en utilisant la technique ( P < 0,001), ont trouvé qu'il était plus facile de se familiariser avec les nouveaux dispositifs ou programmes ( P < 0,001), et avaient plus de connaissances sur l'IA ( P > 0,001).Conclusions:L'étude a révélé que la plupart des Saoudiens, en particulier ceux qui étaient familiers avec l'utilisation de la technologie, soutiennent l'emploi de l'IA dans les soins de santé, avec un point de vue positif ou neutre. Néanmoins, des campagnes ciblées dans certaines régions sont nécessaires pour éduquer l'ensemble de la communauté sur les avantages potentiels de l'IA.

Citizen Views on an Opt-Out Approach to National Electronic Health Records in Germany: A Small-Scale Qualitative Study.

Objectives: Electronic health records (German: elektronische Patientenakte - ePA) are an important healthcare tool. However, in Germany, current participation remains low for their national ePA. To rectify this, the German government recently adopted an opt-out approach to their national ePA system. The objective of this study is to investigate and provide a brief overview of German public attitudes towards this approach to inform policymakers with evidence-based insights. Methods: Four public focus groups were conducted with 12 German citizens to discuss their opinions on the German governments new opt-out approach to the ePA. Results: Three major thematic categories were identified (Contributors to Opt-Out Implementation, Barriers to Opt-Out Implementation, and Contingent Factors) to describe citizen views on the opt-out approach for the ePA. Conclusion: The public is generally supportive of an opt-out approach to ePAs in Germany, as they see the benefits ePAs can provide to German society; but they are skeptical on how successful this approach might be due to extant issues that policymakers must be aware of in order to successfully implement an opt-out approach for Germany's national ePA system.

Research on risk assessment model and simulation of online group polarization in emergencies.

This study proposes a framework for the risk structure of group polarization in the online information of sudden public health incidents, as well as the causes and constraints of group polarization distributed in the ternary space. Then, combining the above two and based on the concept of phase change space, a simulation model of group polarization of online information in sudden public health incidents was constructed by reflecting the risk of group polarization with the number of network users who hold extreme views. The system dynamics simulation of the model is carried out by using the software Anylogic, and predict the evolution trend of the model from the perspective of simulation. The research results indicate that the model built in this study can effectively simulate the formation and dissemination of extreme opinions in the online public opinion of public health emergencies. The vital factors or constraints on the group polarization include emotional guidance, heat reduction, as well as and life pressure.

Attention and sentiment of Chinese public toward rural landscape based on Sina Weibo.

Rural landscapes, as products of the interaction between humans and nature, not only reflect the history and culture of rural areas but also symbolize economic and social progress. This study proposes a deep learning-based model for Weibo data analysis aimed at exploring the development direction of rural landscapes from the perspective of the Chinese public. The research reveals that the Chinese public's attention to rural landscapes has significantly increased with the evolution of government governance concepts. Most people express a high level of satisfaction and happiness with the existing rural landscapes, while a minority harbor negative emotions towards unreasonable new rural construction. Through the analysis of public opinion regarding rural landscapes, this study will assist decision-makers in understanding the mechanisms of public discourse on social media. It will also aid relevant scholars and designers in providing targeted solutions, which hold significant importance for policy formulation and the exploration of specific development patterns.

Assessment of Saudi Public Perceptions and Opinions towards Artificial Intelligence in Health Care.

Background and Objectives: The healthcare system in Saudi Arabia is growing rapidly with the utilization of advanced technologies. Therefore, this study aimed to assess the Saudi public perceptions and opinions towards artificial intelligence (AI) in health care. Materials and Methods: This cross-sectional web-based questionnaire study was conducted between January and April 2024. Data were analyzed from 830 participants. The perceptions of the public towards AI were assessed using 21-item questionnaires. Results: Among the respondents, 69.4% were males and 46% of them were aged above 41 years old. A total of 84.1% of the participants knew about AI, while 61.1% of them believed that AI is a tool that helps healthcare professionals, and 12.5% of them thought that AI may replace the physician, pharmacist, or nurse in the healthcare system. With regard to opinion on the widespread use of AI, 45.8% of the study population believed that healthcare professionals will be improved with the widespread use of artificial intelligence. The mean perception score of AI among males was 38.4 (SD = 6.1) and this was found to be higher than for females at 37.7 (SD = 5.3); however, no significant difference was observed (p = 0.072). Similarly, the mean perception score was higher among young adults aged between 20 and 25 years at 38.9 (SD = 6.1) compared to other age groups, but indicating no significant association between them (p = 0.198). Conclusions: The results showed that the Saudi public had a favorable opinion and perceptions of AI in health care. This suggests that health management recommendations should be made regarding how to successfully integrate and use medical AI while maintaining patient safety.

The racialization of pit bulls: What dogs can teach us about racial politics.

Many have argued that discrimination against pit bulls is rooted in the breed's association with Black owners and culture. We theoretically and empirically interrogate that argument in a variety of ways and uncover striking similarities between the racialization of pit bulls and other racialized issues (e.g., poverty and crime) in public opinion and policy implementation. After detailing the reasons to expect pit bulls to be racialized as Black despite dog ownership in the U.S. generally being raced as white, the article shows: (1) Most Americans associate pit bulls with Black people. (2) Anti-Black attitudes, in general, are significant, independent, predictors of both anti-pit views and of preferring other breeds over them; (3) stereotypes of Black men as violent, in particular, are significant, independent, predictors of both anti-pit views and of preferring other breeds over them. (4) Implicit racialization through a national survey experiment further eroded support for legalizing pits, with the treatment effect significantly conditioned by respondent's race. And (5) state-level racial prejudice is a significant negative predictor of enacting legislation to preempt breed-specific bans. We conclude with our findings' broader insights into the nature of U.S. racial politics. Michael Tesler, mtesler@uci.edu, corresponding author, is Professor of Political Science at UC Irvine; Mary McThomas, mary.mcthomas@uci.edu, is Associate Professor of Political Science at UC Irvine. An earlier version of this paper was presented at the American Political Science Association's annual meeting. We thank Maneesh Arora, Rachel Bernhard, Nathan Chan, Louis Pickett, David Sears, DeSipio, Adam Duberstein, Jane Junn, Claire Kim, Jessica Manforti, J. Scott Matthews, Justin.

When medical assistance in dying is not a last resort option: survey of the Canadian public.

OBJECTIVES: What are the Canadian public's understanding of and views toward medical assistance in dying (MAID) in persons refusing recommended treatment or lacking access to standard treatment or resources? DESIGN/SETTING: An online survey assessed knowledge of and support for Canadian MAID law, and views about four specific scenarios in a two (medical or psychiatric) by two (treatment refusal or lack of access) design. PARTICIPANTS: A quota sample (N=2140) matched to the 2021 Canadian census by age, gender, income, education and province. MAIN OUTCOMES: Participants' level of support for MAID in general and in the four specific scenarios. RESULTS: Only 12.1% correctly answered ≥4 of 5 knowledge questions about the MAID law; only 19.2% knew terminal illness is not required and 20.2% knew treatment refusal is compatible with eligibility. 73.3% of participants expressed support for the MAID law in general, matching a nationally representative poll that used the same question. 40.4% of respondents supported MAID for mental illnesses. Support for MAID in the scenarios depicting refusal or lack of access to treatment ranged from 23.2% (lack of access in medical condition) to 32.0% (treatment refusal in medical illness). Older age, more education, higher income, lower religious attendance or being white was associated with greater support for MAID in general but was either negatively associated or not associated with support for MAID in the four refusal or lack of access scenarios. CONCLUSIONS: Most Canadians support the current MAID law but appear unaware that MAID cases they do not support are compatible with that law. The lower support for MAID in the four scenarios cuts across sociodemographics. The gap between current policy and public opinion warrants further study. For jurisdictions debating MAID, opinion surveys may need to go beyond assessing general attitudes, and target knowledge and views regarding implications of legalisation.

Public preferences and willingness to pay for a net zero NHS: a protocol for a discrete choice experiment in England and Scotland.

INTRODUCTION: Climate change poses a major threat to our health, livelihoods and the planet. In 2020, the UK National Health Service (NHS) committed to reducing its Scope 1, 2 and 3 emissions to reach net zero by 2045. Although a net zero NHS would help to limit the consequences of climate change, little is known about the UK general public's values and preferences for the proposed service changes needed to reach net zero. METHODS: This study will elicit the public's preferences for actions to help achieve net zero NHS in England and Scotland using a discrete choice experiment (DCE). The DCE attributes and levels describe actions that can be taken by the NHS across key areas: buildings and estates, outdoor space, travel and transport, provision of care, goods and services and food and catering. The survey was designed using online think-aloud interviews with 17 members of the public. Two versions of the survey will be administered to a sample of up to 2200 respondents. One will include a payment vehicle as income tax increases. We will estimate the relative importance of each attribute and, for the former survey, the monetary trade-offs which individuals are willing to make between attributes. Where possible, we will match both samples to gauge preference robustness with the inclusion of the monetary payment. We will test whether respondents' preferences differ based on their socioeconomic circumstances and attitudes toward the NHS and climate change. ETHICS AND DISSEMINATION: The University of Aberdeen's School of Medicine, Medical Sciences and Nutrition Ethics Research Board has approved the study (reference: SERB/690090). All participants will provide informed consent. Results will be submitted to peer-reviewed publications and presented at relevant conferences and seminars. A lay summary of the research will be published on the Health Economics Research Unit website.

Bubble reachers and uncivil discourse in polarized online public sphere.

Early optimism saw possibilities for social media to renew democratic discourse, marked by hopes for individuals from diverse backgrounds to find opportunities to learn from and interact with others different from themselves. This optimism quickly waned as social media seemed to breed ideological homophily marked by "filter bubbles" or "echo chambers." A typical response to the sense of fragmentation has been to encourage exposure to more cross-partisan sources of information. But do outlets that reach across partisan lines in fact generate more civil discourse? And does the civility of discourse hosted by such outlets vary depending on the political context in which they operate? To answer these questions, we identified bubble reachers, users who distribute content that reaches other users with diverse political opinions in recent presidential elections in Brazil, where populism has deep roots in the political culture, and Canada, where the political culture is comparatively moderate. Given that background, this research studies unexplored properties of content shared by bubble reachers, specifically the quality of conversations and comments it generates. We examine how ideologically neutral bubble reachers differ from ideologically partisan accounts in the level of uncivil discourse they provoke, and explore how this varies in the context of the two countries considered. Our results suggest that while ideologically neutral bubble reachers support less uncivil discourse in Canada, the opposite relationship holds in Brazil. Even non-political content by ideologically neutral bubble reachers elicits a considerable amount of uncivil discourse in Brazil. This indicates that bubble reaching and incivility are moderated by the national political context. Our results complicate the simple hypothesis of a universal impact of neutral bubble reachers across contexts.

Useful or not? The discussion of traditional Chinese medicine to treat COVID-19 on a Chinese social networking site.

The use of traditional medicine is a global phenomenon, and the WHO advocated its appropriate integration into modern healthcare systems. However, there is a hot debate about the legitimacy of traditional medicine among the general public. Here, we investigated the debate in the Chinese digital context by analysing 1954 responses related to 100 questions about traditional Chinese medicine (TCM) treatment against COVID-19 on the Zhihu platform. Attitude function theory was applied to understand the reasons underlying public attitudes.Results showed that Zhihu users generally held a supportive attitude toward TCM. Their attitudes mainly came from their own experience and traditional media. The general users were more negative while medical professionals were more positive toward TCM. Ego defence (eg, derogating evidence sources) was used the most to support attitudes, followed by value expression (eg, believing in science). Supporters showed fewer expressions of faith (eg, the use of TCM is a kind of faith), politics (eg, supporting TCM is about politics) and science value (eg, TCM is a field of science), fewer ego defence, more patriotism and cultural confidence expressions (eg, TCM is a cultural pride) and more knowledge explanation (eg, TCM accelerates the metabolism of phlegm) than expected. Opposers showed fewer utilitarian and knowledge functions, fewer expressions of patriotism and more expressions of faith, politics and economics, but more ego defence functions than expected. Opposing posts were more likely to attract engagement than supporting and neutral posts. Posts that mentioned attitude functions generally attracted more engagement.Our findings indicate that TCM debate in modern China is not only relevant to medical science and health, but also rooted deeply in cultural ideology, politics and economics. The findings can provide global insights into the development of proactive policies and action plans that will help the integration of traditional medicine into modern healthcare systems.

Sentiment analysis of the Hamas-Israel war on YouTube comments using deep learning.

Sentiment analysis aims to classify text based on the opinion or mentality expressed in a situation, which can be positive, negative, or neutral. Therefore, in the world, a lot of opinions are available on various social media sites, which must be gathered and analyzed to assess the general public's opinion. Finding and monitoring comments, as well as manually extracting the information contained in them, is a difficult task due to the vast diversity of ideas on YouTube. Identifying public opinion on war topics is crucial for offering insights to opposing sides based on popular opinion and emotions about the ongoing war. To address the gap, we build a model on YouTube comment sentiment analysis of the Hamas-Israel war to determine public opinion. In this study, we address the gaps by developing a deep learning-based approach for sentiment analysis. We have collected 24,360 comments from popular YouTube News Channels including BBC, WION, Aljazeera, and others about the Hamas-Israel War using YouTube API and Google spreadsheet and labeled them by linguistic experts into three classes: positive, negative, and neutral. Then, textual comments were preprocessed using natural language processing (NLP) techniques, and features were extracted using Word2vec, FastText, and GloVe. Moreover, we have used the SMOTE data balancing technique and used different data splits, but the 80/20 train-test split ratio has the highest accuracy. For classification model building, commonly used classification algorithms LSTM, Bi-LSTM, GRU, and Hybrid of CNN and Bi-LSTM were applied, and their performance is compared. As a result, the Hybrid of CNN and Bi-LSTM with Word2vec achieved the highest performance with 95.73% accuracy for comments classifications.