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1.
Front Public Health ; 9: 753048, 2021.
Article in English | MEDLINE | ID: covidwho-1590788

ABSTRACT

Background: The rapidly growing imbalance between supply and demand for ventilators during the COVID-19 pandemic has highlighted the principles for fair allocation of scarce resources. Failing to address public views and concerns on the subject could fuel distrust. The objective of this study was to determine the priorities of the Iranian public toward the fair allocation of ventilators during the COVID-19 pandemic. Methods: This anonymous community-based national study was conducted from May 28 to Aug 20, 2020, in Iran. Data were collected via the Google Forms platform, using an online self-administrative questionnaire. The questionnaire assessed participants' assigned prioritization scores for ventilators based on medical and non-medical criteria. To quantify participants' responses on prioritizing ventilator allocation among sub-groups of patients with COVID-19 who need mechanical ventilation scores ranging from -2, very low priority, to +2, very high priority were assigned to each response. Results: Responses of 2,043 participants, 1,189 women, and 1,012 men, were analyzed. The mean (SD) age was 31.1 (9.5), being 32.1 (9.3) among women, and 29.9 (9.6) among men. Among all participants, 274 (13.4%) were healthcare workers. The median of assigned priority score was zero (equal) for gender, age 41-80, nationality, religion, socioeconomic, high-profile governmental position, high-profile occupation, being celebrities, employment status, smoking status, drug abuse, end-stage status, and obesity. The median assigned priority score was +2 (very high priority) for pregnancy, and having <2 years old children. The median assigned priority score was +1 (high priority) for physicians and nurses of patients with COVID-19, patients with nobel research position, those aged <40 years, those with underlying disease, immunocompromise status, and malignancy. Age>80 was the only factor participants assigned -1 (low priority) to. Conclusions: Participants stated that socioeconomic factors, except for age>80, should not be involved in prioritizing mechanical ventilators at the time of resources scarcity. Front-line physicians and nurses of COVID-19 patients, pregnant mothers, mothers who had children under 2 years old were given high priority.


Subject(s)
COVID-19 , Adult , Aged , Aged, 80 and over , Child, Preschool , Female , Health Care Rationing , Humans , Infant , Iran/epidemiology , Male , Middle Aged , Pandemics , Public Opinion , SARS-CoV-2 , Surveys and Questionnaires , Ventilators, Mechanical
2.
Front Public Health ; 9: 756360, 2021.
Article in English | MEDLINE | ID: covidwho-1581117

ABSTRACT

Suicide events may have a negative impact on all of society. The media plays a significant role in suicide prevention. Therefore, the aims of this study are (a) to understand the association between characteristics of suicide events and characteristics of who committed suicide, and event impact indexes (EIIs) of suicide reported on the internet; (b) to analyze violation of recommendations for reporting suicide by Weibo, and (c) to investigate the effect of online reports of suicide on public opinion. We carried out a content analysis of online reports of suicide. This study analyzed 113 suicide events, 300 news reports of suicide, and 2,654 Weibo comments about suicide collected from the WeiboReach between 2015 and 2020. We used a t-test and analysis of variance (ANOVA) to explore the potential factors associated with the EIIs of suicide events. The results found that (a) The suicide events reported on the internet during COVID-19 and those related to celebrities and students tend to have higher EIIs; (b) suicide reports on Weibo frequently violated WHO recommendations for suicide reporting in the media; and (c) public opinion of suicide reporting in the online media was mostly emotional and irrational, which is not beneficial for public mental health and suicide prevention. In conclusion, first, the situation of many people working from home or studying from home and spreading more time online during COVID-19 may lead to suicide events obtain more public attention. Online media could further improve public responsible reporting and daily media-content surveillance, especially taking particular care in those suicide events during COVID-19, and related to celebrities and students, which may have a higher event impact on the internet. Second, health managers should regular assessment of observance of the WHO recommendations for suicide reporting by online social media to prevent suicide. Third, health communication managers should use big data to identify, assess, and manage harmful information about suicide; and track anyone affected by suicide-related reports on social media to reduce the negative impact of public opinion to intervene suicide in the early stage of suicide.


Subject(s)
COVID-19 , Social Media , Suicide , Humans , Public Opinion , SARS-CoV-2 , Suicide/prevention & control
3.
Int J Environ Res Public Health ; 18(24)2021 12 20.
Article in English | MEDLINE | ID: covidwho-1580713

ABSTRACT

A series of mitigation efforts were implemented in response to the COVID-19 pandemic in Saudi Arabia, including the development of mobile health applications (mHealth apps) for the public. Assessing the acceptability of mHealth apps among the public is crucial. This study aimed to use Twitter to understand public perceptions around the use of six Saudi mHealth apps used during COVID-19: "Sehha", "Mawid", "Sehhaty", "Tetamman", "Tawakkalna", and "Tabaud". We used two methodological approaches: network and sentiment analysis. We retrieved Twitter data using specific mHealth apps-related keywords. After including relevant tweets, our final mHealth app networks consisted of a total of 4995 Twitter users and 8666 conversational relationships. The largest networks in size (i.e., the number of users) and volume (i.e., the conversational relationships) among all were "Tawakkalna" followed by "Tabaud", and their conversations were led by diverse governmental accounts. In contrast, the four remaining mHealth networks were mainly led by the health sector and media. Our sentiment analysis approach included five classes and showed that most conversations were neutral, which included facts or information pieces and general inquires. For the automated sentiment classifier, we used Support Vector Machine with AraVec embeddings as it outperformed the other tested classifiers. The sentiment classifier showed an accuracy, precision, recall, and F1-score of 85%. Future studies can use social media and real-time analytics to improve mHealth apps' services and user experience, especially during health crises.


Subject(s)
COVID-19 , Social Media , Telemedicine , Humans , Pandemics , Public Opinion , SARS-CoV-2 , Saudi Arabia/epidemiology
4.
J Med Internet Res ; 23(2): e23957, 2021 02 23.
Article in English | MEDLINE | ID: covidwho-1576022

ABSTRACT

BACKGROUND: During the COVID-19 pandemic in Canada, Prime Minister Justin Trudeau provided updates on the novel coronavirus and the government's responses to the pandemic in his daily briefings from March 13 to May 22, 2020, delivered on the official Canadian Broadcasting Corporation (CBC) YouTube channel. OBJECTIVE: The aim of this study was to examine comments on Canadian Prime Minister Trudeau's COVID-19 daily briefings by YouTube users and track these comments to extract the changing dynamics of the opinions and concerns of the public over time. METHODS: We used machine learning techniques to longitudinally analyze a total of 46,732 English YouTube comments that were retrieved from 57 videos of Prime Minister Trudeau's COVID-19 daily briefings from March 13 to May 22, 2020. A natural language processing model, latent Dirichlet allocation, was used to choose salient topics among the sampled comments for each of the 57 videos. Thematic analysis was used to classify and summarize these salient topics into different prominent themes. RESULTS: We found 11 prominent themes, including strict border measures, public responses to Prime Minister Trudeau's policies, essential work and frontline workers, individuals' financial challenges, rental and mortgage subsidies, quarantine, government financial aid for enterprises and individuals, personal protective equipment, Canada and China's relationship, vaccines, and reopening. CONCLUSIONS: This study is the first to longitudinally investigate public discourse and concerns related to Prime Minister Trudeau's daily COVID-19 briefings in Canada. This study contributes to establishing a real-time feedback loop between the public and public health officials on social media. Hearing and reacting to real concerns from the public can enhance trust between the government and the public to prepare for future health emergencies.


Subject(s)
COVID-19 , Federal Government , Natural Language Processing , Public Health , Public Opinion , Social Media , COVID-19 Vaccines , Canada , Emigration and Immigration , Financial Stress , Financing, Government , Government , Humans , Longitudinal Studies , Pandemics , Personal Protective Equipment , Public Policy , Quarantine , SARS-CoV-2 , Unsupervised Machine Learning
5.
J Med Internet Res ; 23(2): e25734, 2021 02 12.
Article in English | MEDLINE | ID: covidwho-1575972

ABSTRACT

BACKGROUND: In a fast-evolving public health crisis such as the COVID-19 pandemic, multiple pieces of relevant information can be posted sequentially on a social media platform. The interval between subsequent posting times may have a different impact on the transmission and cross-propagation of the old and new information that results in a different peak value and a final size of forwarding users of the new information, depending on the content correlation and whether the new information is posted during the outbreak or quasi-steady-state phase of the old information. OBJECTIVE: This study aims to help in designing effective communication strategies to ensure information is delivered to the maximal number of users. METHODS: We developed and analyzed two classes of susceptible-forwarding-immune information propagation models with delay in transmission to describe the cross-propagation process of relevant information. A total of 28,661 retweets of typical information were posted frequently by each opinion leader related to COVID-19 with high influence (data acquisition up to February 19, 2020). The information was processed into discrete points with a frequency of 10 minutes, and the real data were fitted by the model numerical simulation. Furthermore, the influence of parameters on information dissemination and the design of a publishing strategy were analyzed. RESULTS: The current epidemic outbreak situation, epidemic prevention, and other related authoritative information cannot be timely and effectively browsed by the public. The ingenious use of information release intervals can effectively enhance the interaction between information and realize the effective diffusion of information. We parameterized our models using real data from Sina Microblog and used the parameterized models to define and evaluate mutual attractiveness indexes, and we used these indexes and parameter sensitivity analyses to inform optimal strategies for new information to be effectively propagated in the microblog. The results of the parameter analysis showed that using different attractiveness indexes as the key parameters can control the information transmission with different release intervals, so it is considered as a key link in the design of an information communication strategy. At the same time, the dynamic process of information was analyzed through index evaluation. CONCLUSIONS: Our model can carry out an accurate numerical simulation of information at different release intervals and achieve a dynamic evaluation of information transmission by constructing an indicator system so as to provide theoretical support and strategic suggestions for government decision making. This study optimizes information posting strategies to maximize communication efforts for delivering key public health messages to the public for better outcomes of public health emergency management.


Subject(s)
COVID-19/epidemiology , Health Education , Information Dissemination , Public Health/statistics & numerical data , Public Opinion , Social Media/statistics & numerical data , Communication , Disease Outbreaks , Government , Humans , Pandemics , Time Factors
6.
J Med Internet Res ; 23(2): e26302, 2021 02 22.
Article in English | MEDLINE | ID: covidwho-1575865

ABSTRACT

BACKGROUND: The emergence of SARS-CoV-2 (ie, COVID-19) has given rise to a global pandemic affecting 215 countries and over 40 million people as of October 2020. Meanwhile, we are also experiencing an infodemic induced by the overabundance of information, some accurate and some inaccurate, spreading rapidly across social media platforms. Social media has arguably shifted the information acquisition and dissemination of a considerably large population of internet users toward higher interactivities. OBJECTIVE: This study aimed to investigate COVID-19-related health beliefs on one of the mainstream social media platforms, Twitter, as well as potential impacting factors associated with fluctuations in health beliefs on social media. METHODS: We used COVID-19-related posts from the mainstream social media platform Twitter to monitor health beliefs. A total of 92,687,660 tweets corresponding to 8,967,986 unique users from January 6 to June 21, 2020, were retrieved. To quantify health beliefs, we employed the health belief model (HBM) with four core constructs: perceived susceptibility, perceived severity, perceived benefits, and perceived barriers. We utilized natural language processing and machine learning techniques to automate the process of judging the conformity of each tweet with each of the four HBM constructs. A total of 5000 tweets were manually annotated for training the machine learning architectures. RESULTS: The machine learning classifiers yielded areas under the receiver operating characteristic curves over 0.86 for the classification of all four HBM constructs. Our analyses revealed a basic reproduction number R0 of 7.62 for trends in the number of Twitter users posting health belief-related content over the study period. The fluctuations in the number of health belief-related tweets could reflect dynamics in case and death statistics, systematic interventions, and public events. Specifically, we observed that scientific events, such as scientific publications, and nonscientific events, such as politicians' speeches, were comparable in their ability to influence health belief trends on social media through a Kruskal-Wallis test (P=.78 and P=.92 for perceived benefits and perceived barriers, respectively). CONCLUSIONS: As an analogy of the classic epidemiology model where an infection is considered to be spreading in a population with an R0 greater than 1, we found that the number of users tweeting about COVID-19 health beliefs was amplifying in an epidemic manner and could partially intensify the infodemic. It is "unhealthy" that both scientific and nonscientific events constitute no disparity in impacting the health belief trends on Twitter, since nonscientific events, such as politicians' speeches, might not be endorsed by substantial evidence and could sometimes be misleading.


Subject(s)
COVID-19/psychology , Data Analysis , Health Education/statistics & numerical data , Machine Learning , Natural Language Processing , Public Opinion , Social Media/statistics & numerical data , COVID-19/epidemiology , Humans , Pandemics
7.
J Med Internet Res ; 23(2): e25108, 2021 02 09.
Article in English | MEDLINE | ID: covidwho-1574667

ABSTRACT

BACKGROUND: The Centers for Disease Control and Prevention (CDC) is a national public health protection agency in the United States. With the escalating impact of the COVID-19 pandemic on society in the United States and around the world, the CDC has become one of the focal points of public discussion. OBJECTIVE: This study aims to identify the topics and their overarching themes emerging from the public COVID-19-related discussion about the CDC on Twitter and to further provide insight into public's concerns, focus of attention, perception of the CDC's current performance, and expectations from the CDC. METHODS: Tweets were downloaded from a large-scale COVID-19 Twitter chatter data set from March 11, 2020, when the World Health Organization declared COVID-19 a pandemic, to August 14, 2020. We used R (The R Foundation) to clean the tweets and retain tweets that contained any of five specific keywords-cdc, CDC, centers for disease control and prevention, CDCgov, and cdcgov-while eliminating all 91 tweets posted by the CDC itself. The final data set included in the analysis consisted of 290,764 unique tweets from 152,314 different users. We used R to perform the latent Dirichlet allocation algorithm for topic modeling. RESULTS: The Twitter data generated 16 topics that the public linked to the CDC when they talked about COVID-19. Among the topics, the most discussed was COVID-19 death counts, accounting for 12.16% (n=35,347) of the total 290,764 tweets in the analysis, followed by general opinions about the credibility of the CDC and other authorities and the CDC's COVID-19 guidelines, with over 20,000 tweets for each. The 16 topics fell into four overarching themes: knowing the virus and the situation, policy and government actions, response guidelines, and general opinion about credibility. CONCLUSIONS: Social media platforms, such as Twitter, provide valuable databases for public opinion. In a protracted pandemic, such as COVID-19, quickly and efficiently identifying the topics within the public discussion on Twitter would help public health agencies improve the next-round communication with the public.


Subject(s)
COVID-19 , Centers for Disease Control and Prevention, U.S. , Data Mining , Public Opinion , Social Media , Communication , Humans , Pandemics , Public Health , Public Policy , SARS-CoV-2 , United States
8.
Int J Environ Res Public Health ; 18(24)2021 12 10.
Article in English | MEDLINE | ID: covidwho-1572452

ABSTRACT

Vaccine hesitancy is an ongoing concern, presenting a major threat to global health. SARS-CoV-2 COVID-19 vaccinations are no exception as misinformation began to circulate on social media early in their development. Twitter's Application Programming Interface (API) for Python was used to collect 137,781 tweets between 1 July 2021 and 21 July 2021 using 43 search terms relating to COVID-19 vaccines. Tweets were analysed for sentiment using Microsoft Azure (a machine learning approach) and the VADER sentiment analysis model (a lexicon-based approach), where the Natural Language Processing Toolkit (NLTK) assessed whether tweets represented positive, negative or neutral opinions. The majority of tweets were found to be negative in sentiment (53,899), followed by positive (53,071) and neutral (30,811). The negative tweets displayed a higher intensity of sentiment than positive tweets. A questionnaire was distributed and analysis found that individuals with full vaccination histories were less concerned about receiving and were more likely to accept the vaccine. Overall, we determined that this sentiment-based approach is useful to establish levels of vaccine hesitancy in the general public and, alongside the questionnaire, suggests strategies to combat specific concerns and misinformation.


Subject(s)
COVID-19 , Social Media , COVID-19 Vaccines , Humans , Public Opinion , SARS-CoV-2 , Surveys and Questionnaires , Vaccination
9.
Comput Math Methods Med ; 2021: 4321131, 2021.
Article in English | MEDLINE | ID: covidwho-1553710

ABSTRACT

The COVID-19 pandemic has had a devastating effect on many people, creating severe anxiety, fear, and complicated feelings or emotions. After the initiation of vaccinations against coronavirus, people's feelings have become more diverse and complex. Our aim is to understand and unravel their sentiments in this research using deep learning techniques. Social media is currently the best way to express feelings and emotions, and with the help of Twitter, one can have a better idea of what is trending and going on in people's minds. Our motivation for this research was to understand the diverse sentiments of people regarding the vaccination process. In this research, the timeline of the collected tweets was from December 21 to July21. The tweets contained information about the most common vaccines available recently from across the world. The sentiments of people regarding vaccines of all sorts were assessed using the natural language processing (NLP) tool, Valence Aware Dictionary for sEntiment Reasoner (VADER). Initializing the polarities of the obtained sentiments into three groups (positive, negative, and neutral) helped us visualize the overall scenario; our findings included 33.96% positive, 17.55% negative, and 48.49% neutral responses. In addition, we included our analysis of the timeline of the tweets in this research, as sentiments fluctuated over time. A recurrent neural network- (RNN-) oriented architecture, including long short-term memory (LSTM) and bidirectional LSTM (Bi-LSTM), was used to assess the performance of the predictive models, with LSTM achieving an accuracy of 90.59% and Bi-LSTM achieving 90.83%. Other performance metrics such as precision,, F1-score, and a confusion matrix were also used to validate our models and findings more effectively. This study improves understanding of the public's opinion on COVID-19 vaccines and supports the aim of eradicating coronavirus from the world.


Subject(s)
COVID-19 Vaccines , COVID-19/prevention & control , Deep Learning , Social Media , Attitude , Attitude to Health , Databases, Factual , Humans , Language , Models, Statistical , Neural Networks, Computer , Public Opinion , Reproducibility of Results , Vaccination
10.
Stud Health Technol Inform ; 285: 67-75, 2021 Oct 27.
Article in English | MEDLINE | ID: covidwho-1502263

ABSTRACT

The Coronavirus pandemic has surprised the world and social media was extremely used to express frustrations and development of the cases found. Social media tools, such as Twitter, show a comparable impact with the number of tweets related to COVID-19 indicating remarkable development in a limited ability to focus time. The purpose of this paper is to investigate the impact of Coronavirus on the United States of America (USA) and New Zealand (NZ), and how that is reflected in a sentiment analysis through the examination of American and New Zealand tweets. We have gathered tweets from a March 2020 - August 2020 and used sentiment extraction on the tweets. The major finding of this sentiment extraction is the fact that the overall average sentiment over the 5-month period stayed in a negative range in the USA and NZ. This paper aims to analyze these trends, identify patterns, and determine whether these trends were caused by the COVID-19 pandemic or outside sources. One trend that was analyzed was the spike of COVID-19 results in relation to the number of protests occurring in the USA.


Subject(s)
COVID-19 , Social Media , COVID-19/prevention & control , Humans , New Zealand/epidemiology , Pandemics/prevention & control , Public Opinion , United States/epidemiology
11.
PLoS One ; 16(10): e0258781, 2021.
Article in English | MEDLINE | ID: covidwho-1496514

ABSTRACT

BACKGROUND: Novel viral pandemics present significant challenges to global public health. Non-pharmaceutical interventions (e.g. social distancing) are an important means through which to control the transmission of such viruses. One of the key factors determining the effectiveness of such measures is the level of public adherence to them. Research to date has focused on quantitative exploration of adherence and non-adherence, with a relative lack of qualitative exploration of the reasons for non-adherence. OBJECTIVE: To explore participants' perceptions of non-adherence to COVID-19 policy measures by self and others in the UK, focusing on perceived reasons for non-adherence. METHODS: Qualitative study comprising 12 focus groups conducted via video-conferencing between 25th September and 13th November 2020. Participants were 51 UK residents aged 18 and above, reflecting a range of ages, genders and race/ethnicities. Data were analysed using a thematic approach. RESULTS: Participants reported seeing an increase in non-adherence in others over the course of the pandemic. Reports of non-adherence in self were lower than reports of non-adherence in others. Analysis revealed six main themes related to participants' reported reasons for non-adherence in self and others: (1) 'Alert fatigue' (where people find it difficult to follow, or switch off from, information about frequently changing rules or advice) (2) Inconsistent rules (3) Lack of trust in government (4) Learned Helplessness (5) Resistance and rebelliousness (6)The impact of vaccines on risk perception. Participants perceived a number of systemic failures (e.g. unclear policy, untrustworthy policymakers) to strongly contribute to two forms non-adherence-violations and errors. CONCLUSION: Findings suggest that latent and systemic failures-in the form of policy decisions that are commonly experienced as too changeable, inconsistent and confusing, and policy makers that are commonly perceived as untrustworthy-may play a significant role in creating the conditions that enable or encourage non-adherence.


Subject(s)
COVID-19/psychology , Patient Compliance/psychology , Public Health/trends , Adult , Aged , Aged, 80 and over , COVID-19/prevention & control , Female , Focus Groups , Humans , Male , Middle Aged , Pandemics/prevention & control , Physical Distancing , Public Health/methods , Public Opinion , SARS-CoV-2/pathogenicity , Surveys and Questionnaires , United Kingdom
13.
Can J Public Health ; 112(5): 957-964, 2021 10.
Article in English | MEDLINE | ID: covidwho-1485587

ABSTRACT

SETTING: The Sendai Framework for Disaster Risk Reduction promotes an "all-of-society" approach to disaster risk reduction (DRR). Since 2013, the EnRiCH Research Lab has implemented a community-based, participatory program to promote youth development and engagement in DRR in Ottawa-Gatineau. The EnRiCH Youth Research Team used an existing community education program called the Enrichment Mini-Course Program as a framework to engage youth in DRR. We aim to share the implementation process and lessons learned from this innovative "all-of-society" approach to DRR. INTERVENTION: The EnRiCH Youth Research Team provides high school and university students with a platform to be heard on disaster and climate change issues. Youth are given opportunities to design and lead knowledge dissemination projects intended to educate members of the community about disaster prevention and preparedness. Students have opportunities to connect with academics, governmental and non-governmental organizations, and public health practitioners to share their ideas on youth participation in DRR in Canada. OUTCOMES: To date, this public health intervention has produced DRR training modules that can be used as curriculum support by teachers, a children's book on earthquake preparedness, an educational video about youth participation in DRR, and several conference presentations. Members of the team have become well versed in disaster preparedness strategies. IMPLICATIONS: This program has demonstrated that youth can contribute to DRR through knowledge mobilization, and support public education about disaster preparedness. Offering this opportunity at a grassroots level can support participation by youth by allowing flexibility in design and adaptation to individual environmental and social contexts.


Subject(s)
Community-Based Participatory Research , Disasters , Risk Reduction Behavior , Adolescent , COVID-19/epidemiology , COVID-19/prevention & control , Canada/epidemiology , Community-Based Participatory Research/organization & administration , Disasters/prevention & control , Humans , Program Evaluation , Public Opinion
14.
Int J Environ Res Public Health ; 18(20)2021 10 16.
Article in English | MEDLINE | ID: covidwho-1480731

ABSTRACT

Health inequities are systemic, avoidable, and unjust differences in health between populations. These differences are often determined by social and structural factors, such as income and social status, employment and working conditions, or race/racism, which are referred to as the social determinants of health (SDOH). According to public opinion, health is considered to be largely determined by the choices and behaviours of individuals. However, evidence suggests that social and structural factors are the key determinants of health. There is likely a lack of public understanding of the role that social and structural factors play in determining health and producing health inequities. Public opinion and priorities can drive governmental action, so the aim of this work was to determine the most impactful way to increase knowledge and awareness about the social determinants of health (SDOH) and health inequities in the province of Ontario, Canada. A study to test the effectiveness of four different messaging styles about health inequities and the SDOH was conducted with a sample of 805 adult residents of Ontario. Findings show that messages highlighting the challenges faced by those experiencing the negative effects of the SDOH, while still acknowledging individual responsibility for health, were the most effective for eliciting an empathetic response from Ontarians. These findings can be used to inform public awareness campaigns focused on changing the current public narrative about the SDOH toward a more empathetic response, with the goal of increasing political will to enact policies to address health inequities in Ontario.


Subject(s)
Racism , Social Determinants of Health , Adult , Health Status Disparities , Humans , Income , Ontario , Public Opinion
15.
BMC Public Health ; 21(1): 1545, 2021 08 12.
Article in English | MEDLINE | ID: covidwho-1477307

ABSTRACT

BACKGROUNDS: This study examined the dynamic association between risk communication and the public's risk perception and action across the COVID-19 outbreak timeline in China. METHODS: This study collected publicly available information on COVID-19 released on official channels (e.g., government websites and official media) by the Parehub tool. Also, the study used the Zhongyun Big Data Platform to search public datasets about released COVID-19 information on Chinese social media, such as TikTok and Weibo. An online survey was conducted via WeChat to Chinese citizens using a snowball sampling method. The questionnaire assessed changes in participants' risk perception and action against COVID-19. The data analysis examined information content and release-time trajectories against the public's risk perception and actions over time. RESULTS: Altogether, the collected data includes 1477 pieces of authorized information and 297,000 short videos on COVID-19. Of 1362 participants recruited from 33 provinces and municipalities of China, 1311 respondents (25-60 years, 42% male) were valid for future analysis. The study indicated that 85.7% of participants mainly relied on official channels to obtain information. Alongside the outbreak's progress, there was a gradual rise in information quantity, publishing frequency, and content variation. Correspondingly, the public's risk perception that "take it seriously" rose from 13 to 80%, 87.1% of those who took "multiple actions" compared to 25.9% initially. CONCLUSIONS: Our findings indicated that insufficient information freely-accessible at the early stages of the outbreak might lead to the lack of risk awareness and the public's inadequate protective actions. Given the current global situation of COVID-19, the study highlights authorized, transparent, and timely two-way risk communication is vital to guide public perception and actions. Furthermore, our study provides risk communication recommendations and may contribute to developing full measures to address future crises.


Subject(s)
COVID-19 , Social Media , China/epidemiology , Communication , Female , Humans , Male , Public Opinion , SARS-CoV-2
17.
J Prev Med Public Health ; 54(5): 360-369, 2021 Sep.
Article in English | MEDLINE | ID: covidwho-1471032

ABSTRACT

OBJECTIVES: The purpose of this study was to investigate public preferences regarding allocation principles for scarce medical resources in the coronavirus disease 2019 (COVID-19) pandemic, particularly in comparison with the recommendations of ethicists. METHODS: An online survey was conducted with a nationally representative sample of 1509 adults residing in Korea, from November 2 to 5, 2020. The degree of agreement with resource allocation principles in the context of the medical resource constraints precipitated by the COVID-19 pandemic was examined. The results were then compared with ethicists' recommendations. We also examined whether the perceived severity of COVID-19 explained differences in individual preferences, and by doing so, whether perceived severity helps explain discrepancies between public preferences and ethicists' recommendations. RESULTS: Overall, the public of Korea agreed strongly with the principles of "save the most lives," "Koreans first," and "sickest first," but less with "random selection," in contrast to the recommendations of ethicists. "Save the most lives" was given the highest priority by both the public and ethicists. Higher perceived severity of the pandemic was associated with a greater likelihood of agreeing with allocation principles based on utilitarianism, as well as those promoting and rewarding social usefulness, in line with the opinions of expert ethicists. CONCLUSIONS: The general public of Korea preferred rationing scarce medical resources in the COVID-19 pandemic predominantly based on utilitarianism, identity and prioritarianism, rather than egalitarianism. Further research is needed to explore the reasons for discrepancies between public preferences and ethicists' recommendations.


Subject(s)
COVID-19 , Health Resources/supply & distribution , Pandemics , Public Opinion , Adult , Aged , Ethicists , Female , Health Care Rationing/ethics , Health Resources/ethics , Humans , Male , Middle Aged , Republic of Korea , Surveys and Questionnaires , Young Adult
18.
Int J Environ Res Public Health ; 18(20)2021 10 17.
Article in English | MEDLINE | ID: covidwho-1470870

ABSTRACT

Various intelligent technologies have been applied during COVID-19, which has become a worldwide public health emergency and brought significant challenges to the medical systems around the world. Notably, the application of robots has played a role in hospitals, quarantine facilities and public spaces and has attracted much attention from the media and the public. This study is based on a questionnaire survey on the perception and reception of robots used for medical care in the pandemic among the Chinese population. A total of 1667 people participated in the survey, 93.6% of respondents were pursuing or had completed a bachelor, master or even doctorate degree. The results show that Chinese people generally held positive attitudes towards "anti-pandemic robots" and affirmed their contribution to reducing the burden of medical care and virus transmission. A few respondents were concerned about the issues of robots replacing humans and it was apparent that their ethical views on robots were not completely consistent across their demographics (e.g., age, industry). Nevertheless, most respondents tended to be optimistic about robot applications and dialectical about the ethical issues involved. This is related to the prominent role robots played during the pandemic, the Chinese public's expectations of new technologies and technology-friendly public opinion in China. Exploring the perception and reception of anti-pandemic robots in different countries or cultures is important because it can shed some light on the future applications of robots, especially in the field of infectious disease control.


Subject(s)
COVID-19 , Robotic Surgical Procedures , Robotics , Emergencies , Humans , Public Health , Public Opinion , SARS-CoV-2 , Surveys and Questionnaires
20.
J Med Ethics ; 46(8): 510-513, 2020 08.
Article in English | MEDLINE | ID: covidwho-1467730

ABSTRACT

During the COVID-19 pandemic, the media have repeatedly praised healthcare workers for their 'heroic' work. Although this gratitude is undoubtedly appreciated by many, we must be cautious about overuse of the term 'hero' in such discussions. The challenges currently faced by healthcare workers are substantially greater than those encountered in their normal work, and it is understandable that the language of heroism has been evoked to praise them for their actions. Yet such language can have potentially negative consequences. Here, I examine what heroism is and why it is being applied to the healthcare workers currently, before outlining some of the problems associated with the heroism narrative currently being employed by the media. Healthcare workers have a clear and limited duty to treat during the COVID-19 pandemic, which can be grounded in a broad social contract and is strongly associated with certain reciprocal duties that society has towards healthcare workers. I argue that the heroism narrative can be damaging, as it stifles meaningful discussion about what the limits of this duty to treat are. It fails to acknowledge the importance of reciprocity, and through its implication that all healthcare workers have to be heroic, it can have negative psychological effects on workers themselves. I conclude that rather than invoking the language of heroism to praise healthcare workers, we should examine, as a society, what duties healthcare workers have to work in this pandemic, and how we can support them in fulfilling these.


Subject(s)
Coronavirus Infections , Courage , Delivery of Health Care , Health Personnel , Mass Media , Pandemics , Pneumonia, Viral , Public Opinion , Attitude to Health , Betacoronavirus , COVID-19 , Communication , Coronavirus Infections/virology , Humans , Moral Obligations , Pneumonia, Viral/virology , SARS-CoV-2 , Social Responsibility
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