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3 tips to have an effective conversation about COVID-19

It can be challenging to have conversations with people who don’t share our own views or who see the world differently from us. However, it’s important to facilitate open conversations about the things we disagree on where both parties feel respected and understood. If you are having a conversation with someone about COVID-19, here are some tips to keep in mind. 

1: Approach the conversation with an open mind

Approach difficult conversations with empathy and understanding. It’s normal for people to have conflicting feelings about the pandemic and the changes they’ve had to make. Be empathetic and know that ambivalence is normal. It’s also important to keep in mind that your peers likely have different reasons motivating their actions and choices. Try to understand what is important to them, what they need and how that impacts their behaviors. Avoid making assumptions about why people are resistant to following the rules. Instead, take their perspective and life experiences into account. 

All of the information and expectations around COVID-19 can be overwhelming. Having meaningful conversations can provide you an opportunity to help your peers navigate changes and see how their choices can impact others. If you've tried to have a conversation and you're still concerned that the other person is not following campus health and safety guidelines, you can reach out to Student Conduct and Conflict Resolution for support and guidance.

2: Listen for understanding

One of the most important things we can do when having tough conversations is to really listen to and understand what someone is saying. Below are some things you can do to be an active listener and things to avoid that can interfere with communication. 

Things that get in the way of listening:

• Distractions
• Prejudging the person
• Rushing to solve a problem
• Dismissing or invalidating feelings

Things that can help us listen better:

• Be aware of your own feelings, and avoid projecting them onto the other person.
• Talk in the right environment, one that is neutral, free of distractions and allows us to remain present.
• Focus on listening to the person rather than what you will say next or how you want to respond.
• Meet them where they are at. We can’t change people.

3: Use effective communication skills

Listening is often the first step in having a successful conversation. However, it’s also important to practice good communication skills that can help direct the conversation in a healthy way. Here are some things to keep in mind when having difficult conversations with someone.

Not sure what to say?

Here are some things you can say if your friend, roommate or peer:

• Refuses to wear a face covering because they aren’t worried about getting sick:  “I know wearing a face covering may not be comfortable, but it’s a good way to protect us all.”  
• Wants to have a party or have more people over than you're comfortable with:  “Having lots of people in close contact can increase the spread of the virus, and the more it spreads the less likely we are to stay on campus this semester. Can we just have a few friends over instead?” ​
• Is angry about the public health guidelines and feels like they aren’t able to meet new friends or have the “college experience”:  “I’m sure this is different from what you were expecting. Would you be interested in looking at some different ways to get involved and meet new people?” 

If you and your friends disagree on how strictly you’re following health and safety guidelines, it’s better to defer to the person with a stronger boundary. So if one of you is more strict about guidelines, try to take that person’s lead when making decisions about what to do. 

• Be mindful. Sometimes when we’re passionate about a topic, it can bring up a lot of feelings that may lead us to escalate the situation. Learning how to notice and manage our emotions can help keep things from escalating. Be mindful of your tone and demeanor, and be aware of how you’re showing up in the conversation. Check in and ask yourself “does this still feel like a conversation or does it feel like an argument?”  
• Ask open-ended questions. Open-ended questions are questions that can’t be answered with a simple yes or no. These questions can help draw out ideas or thoughts from a friend or peer. For instance, you can ask “How are you feeling about the new campus policies?” Use them as a way to gain deeper understanding, but use them sparingly. If you ask too many open-ended questions, it may make the conversation feel more like an interrogation.  
• Use affirmations. Whether you agree with someone’s position or not, it’s important to use affirmations to highlight the strengths and values that someone is bringing to the conversation. For example, “I totally get wanting to have a normal social life despite everything that's going on”. Validate how they’re feeling, rather than how they’re behaving. You can be understanding that someone is upset, angry or worried, without saying it’s okay to yell.  
• Use reflections to clarify. Reflections are a great communication tool, but they can take some practice. Reflections give us a chance to state back what we think someone is hearing or saying without framing it as a question. This either confirms to someone that we are hearing what they are saying or gives them the opportunity to correct any misinterpretations. A friend may say to you, “I understand why it’s important not to have large parties, but it’s so boring to just sit at home on the weekends.” You can reflect back, “It sounds like you want to help do your part in reducing the spread of COVID-19, but you are needing another way to connect with friends and have fun.” Oftentimes people may experience stress when they feel that they don’t have a sense of control. Providing options for next steps can be a way to give them some agency in a situation. In this case, you can offer alternatives like, “What if we invite a few friends over for games or go to the park together?”  
• Summarize the conversation. Wrap up the conversation or a portion of the conversation by summarizing to highlight the positive aspects of what you discussed. Keep in mind that change doesn’t happen overnight, and this may be a topic that requires ongoing effort on the part of both people. At the end of the conversation, thank the person for their time and let them know that you appreciate their willingness to speak with you. If you feel like you need to revisit the subject, let them know that you would like to follow up at a later time.

Campus resources

The  Effective Conversations about COVID-19 Course  covers campus expectations for health and social behaviors, helps students build effective communication skills and includes ways to stay connected. It takes roughly 30 minutes to complete through Canvas.

Student Conduct and Conflict Resolution offers guidance, coaching and support to help students navigate conflict and difficult conversations. They also provide oversight and enforcement for the Student Code of Conduct.

Counseling and Psychiatric Services (CAPS) is offering a free virtual COVID-19 workshop series . This series is composed of 4 independent workshops designed to help you cope COVID-19 related changes in your life. This is a great option if you’re short on time, want quick coping skills or are feeling distressed. CAPS is also available for crisis support, brief individual counseling, psychiatry, consultations and other mental health services. If you’re concerned about the well-being of a friend, roommate, classmate or colleague, please call 303-492-2277. 

Learn more about campus expectations and health and safety guidelines by visiting the Protect Our Herd website . 

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• v.9(10); 2022 Oct

Using dialogues to increase positive attitudes towards COVID-19 vaccines in a vaccine-hesitant UK population

Charlotte o. brand.

Department of Psychology, University of Sheffield, Sheffield S1 1HD, UK

Tom Stafford

Associated data.

• Brand CO, Stafford T. 2022. Data from: Using dialogues to increase positive attitudes towards COVID-19 vaccines in a vaccine-hesitant UK population. Figshare . ( 10.6084/m9.figshare.c.6238182) [ CrossRef ]

All of our data, code and analysis scripts are available at https://github.com/lottybrand/clickbot_analysis .

Data are also provided in the electronic supplementary material [ 18 ].

Recently, Altay et al. (Altay et al . 2021. J. Exp.Psychol.: Appl. ( doi:10.1037/xap0000400 )) showed that 5 min of interaction with a chatbot led to increases in positive COVID-19 vaccination attitudes and intentions in a French population. Here we replicate this effect in a vaccine-hesitant, UK-based population. We attempt to isolate what made the chatbot condition effective by controlling the amount of information provided, the trustworthiness of the information and the level of interactivity. Like Altay et al. , our experiment allowed participants to navigate a branching dialogue by choosing questions of interest about COVID-19 vaccines. Our control condition used the same questions and answers but removed participant choice by presenting the dialogues at random. Importantly, we also targeted those who were either against or neutral towards COVID-19 vaccinations to begin with, screening-out those with already positive attitudes. Replicating Altay et al. , we found a similar size increase in positive attitudes towards vaccination, and in intention to get vaccinated. Unlike Altay et al. , we found no difference between our two conditions: choosing the questions did not increase vaccine attitudes or intentions any more than our control condition. These results suggest that the attitudes of the vaccine hesitant are modifiable with exposure to in-depth, trustworthy and engaging dialogues.

1.  Introduction

Communicating the effectiveness, safety and necessity of vaccination is arguably one of science communication's most important and emblematic challenges. Appropriately, huge amounts of attention and research effort have been directed towards how to increase COVID-19 vaccination uptake. Owing to the urgency and impact of the problem, a multi-pronged attack is warranted, and thus research rightly spans many different strategies, from pre-empting misinformation on social media [ 1 ], presenting information on the comparison of COVID-19 symptoms to vaccination side-effects [ 2 ], presenting information on the timeline of vaccine development [ 2 ], different styles of myth-busting [ 3 ], the use of social norms [ 4 ], framing messaging in terms of individual risk preferences [ 5 ], and even chatbots [ 6 ], all with varying levels of success.

Although chatbots are usually used for aiding the completion of tasks, for example navigating website frequently asked questions (FAQs) or purchasing personalized items (train tickets and flights), interest is growing in their ability to create engaging, human-like dialogue. One way in which chatbots could be used for attitude change is their ability to deliver counterarguments to common questions or concerns. The use of chatbots to change attitudes has previously been explored in the context of genetically modified organism (GMO) attitudes [ 7 ]. The authors found, that the chatbot increased positive attitudes towards GMO foods compared to two comparisons: (i) a short description of GMOs, and (ii) a description of the consensus scientific view, but, it did not have a positive effect compared to a third condition: a counterargument condition. In this counterargument condition, participants were exposed to all GMO beliefs and counterarguments at once, rather than choosing which counterarguments to interact with. This suggested that providing access to counterarguments, rather than the choice of information, was the driving factor behind the success of the chatbot. The authors also found that the positive attitudes were mediated by time spent in the conditions, and that people spent on average longer in the counterargument condition. Crucially, they also found that in the chatbot condition, for three out of four arguments, the best predictor for selecting a given argument was how negative their initial view towards it was, suggesting participants did seem to select arguments based on their concerns.

The idea that the choice of information is important chimes with research into people's apparent preference for choosing their own actions, making their own decisions and choosing what path to take, even foregoing monetary rewards to retain agency [ 8 ]. Domains as diverse as animal learning and robotic control have shown the importance of intrinsic motivations for agency, curiosity and control for understanding and enabling complex behaviour [ 9 ]. It is reasonable, therefore, to assume that a chatbot experience may be engaging and by turn convincing because it supports the participant in playing an active role in the dialogue, making choices about the aspects of the topic they explore.

As well as ensuring the information aligns with participant's interests, it is also crucial to communicate trust for successful public health communication [ 10 ]. Eiser et al . [ 11 ] studied public attitudes in response to communication about pollution where they lived, and found that those who didn't trust scientific communication tended to doubt that the scientists had their own interests at heart, rather than doubt their expertise. Furthermore, high trust in information from other sources, such as family and friends, was not based on a misperception of greater expertise, but on the (arguably accurate) perception that these groups had their interests at heart. Indeed, low trust in government is consistently one of the strongest predictors of vaccine hesitancy [ 12 ]. Evidently the effectiveness of communication interventions to increase vaccination intentions may be affected by how trustworthy the intervention is deemed to be.

This paper replicates recent success in increasing positive attitudes towards, and intentions to take, COVID-19 vaccines by using a chatbot [ 6 ]. The chatbot study included participants from a random sample of French adults, whereas here we recruit vaccine-hesitant, UK-based adults only, and attempt to dissect what in particular it was about the chatbot that was effective. In particular, we wanted to test if the choice of information is a crucial factor driving the effectiveness of the chatbot. The French chatbot enabled participants to select frequently asked questions about COVID-19 vaccinations and then presented participants with answers to those questions. The chatbot would then present follow-up questions and further counterarguments. This was compared to a control condition in which the participants read 90 words of standard information from a government website. We wanted to investigate a variety of factors that may have been responsible for the increase in vaccination attitudes and intentions, such as (i) the amount of information, (ii) the time spent with the information, (iii) the interactivity or choice of information, and (iv) the trustworthiness of the information. The ‘chatbot’ condition manifestly allowed participants greater choice, but it also exposed participants to a greater amount of information, and they tended to spend more time engaged as a consequence. The chatbot condition also included content on the trustworthiness of the information being presented, whereas the control condition did not. As such, it is not clear which underlying factors drive the observed effect.

To address our question of what drove the increase in positive vaccination attitudes and intentions, the current study uses the same information as Altay et al . [ 6 ] but deploys two conditions in which the only difference is the interactivity of the information, i.e. the ability to choose which information to view. This allows us to directly test whether the interactivity of the information was a driving factor behind the success of the chatbot, by comparing the results of our control and choice conditions. The amount of information (number of words), time spent on the information and indicators of the trustworthiness of the information are the same in both our control and our choice conditions, allowing us to indirectly test whether these affect the success of the intervention, by comparing our results to Altay et al .'s [ 6 ] results.

2.1. Pre-registered hypotheses

Our hypotheses, predictions and analyses were pre-registered before data collection at https://osf.io/t4gav . All of our data, code and analysis scripts are available at https://github.com/lottybrand/clickbot_analysis .

We hypothesized that the choice condition would show a greater increase in positive attitudes towards COVID-19 vaccines, owing to the ability of participants to choose the information most interesting or important to them. A difference between conditions would be strong evidence that one of the important aspects of chatbots in changing attitudes is that they allow the participant to choose what information to engage with, aside from the trustworthiness and amount of information presented. This logic led to the following three pre-registered predictions:

• (i) increase in willingness to have a vaccine will be predicted by condition (those in the choice condition will be more likely to show an increase in their intention to take the vaccine);
• (ii) there will be an interaction between condition and time of ratings, in that vaccine attitudes will be most positive in the choice condition in the post-experiment ratings compared to the pre-experiment ratings; and
• (iii) the choice condition will be rated as more engaging than the control condition.

2.2. Participants

Based on [ 6 ], we recruited 716 adult participants from the UK. Using the recruitment platform Prolific, we were able to prescreen for UK-based participants aged between 18 and 65 who had previously answered that they were either ‘against’ the COVID-19 vaccinations, or ‘neutral’ towards the COVID-19 vaccinations (as opposed to ‘for’ COVID-19 vaccinations). As there were 657 participants registered to Prolific who answered ‘against’ at the time of recruitment, we attempted to recruit as many from this pool as possible. We only recruited participants who answered ‘against’ for the first seven days of data collection, as per our pre-registration. This led to 479 participants who answered ‘against’ in total, and a remaining 237 who answered ‘neutral’. The mean age was 35, and 207 participants were male (502 female, two non-binary, two other, three prefer-not-to-say). Ten pilot participants were recruited on 26 April 2021 and their data used for pre-registering our analysis script only (they do not contribute data to the analyses presented here). The remaining participants were recruited between 14 and 24 May 2021.

2.3. Materials

The baseline questionnaire was almost identical to Altay et al . except that we opted to use a 7-point Likert scale as opposed to five points [ 13 ]. We asked participants to rate how strongly they agree with the following statements (from 1 = strongly disagree to 7 = strongly agree): I think COVID-19 vaccines are safe, I think COVID-19 vaccines are effective, I think we've had enough time to develop COVID-19 vaccines, I think we can trust those who produce COVID-19 vaccines, I think it is important to be vaccinated against COVID-19 . We also asked participants if they had yet taken a dose of any COVID-19 vaccine (yes, no) and whether they would consider taking any future dose of an approved COVID-19 vaccine offered to them (yes, no, undecided).

The information we used for our two conditions was taken from the Altay et al . study. We translated the information into English using automated translation via Google Docs, proof-read it, updated it with the most recent information at the time using official UK National Health Service and Government sources (e.g. regarding the Astra-Zeneca blood clot news), and had the information verified and fact-checked again by an independent epidemiologist.

To mimic the main features of their chatbot—interactive choice of questions and appropriate follow-up answers—we grouped the vaccine information into five main questions: (i) is the vaccine safe? (ii) is the vaccine effective? (iii) has the vaccine been rushed? (iv) can we trust who makes the vaccine? and (v) is the vaccine necessary? Within each of the five main questions were four sub-questions. Thus, there were 20 question–answer dialogues altogether, and each participant was presented with four out of those 20. We modified each sub-question to consist of a short dialogue of between 200–500 words largely avoiding repetition. Each dialogue included a short answer and two or three follow-up question–answer pairs. (These documents along with a document recording the main changes made to each section compared to the Altay paper can be found in the electronic supplementary material and on the online repository.) Thus, our participants experienced almost identical information to Altay et al. , in dialogue format. As with Altay et al. 's study, the participant experience lacked some features of full interactive chat: in both Altay et al. and our study, participants were not able to freely type but chose questions from a given selection, and replies were not individually or uniquely composed. However, Altay et al. 's study did contain bot-like features, such as a symbol that the bot was ‘typing’, and a chat-like window, which was not present in our study.

Crucially, participants in both our control and our chatbot condition were presented with the following information about the trustworthiness of the study at the start of the condition:

‘Why should I trust you? - We are two independent researchers, Lotty Brand and Tom Stafford, funded by a research council, with no links to pharmaceutical companies or other competing interests.

We are interested in learning about people's vaccine attitudes, in providing reliable information about vaccines, and learning about people's engagement with this information.

All of the information in this study has been gathered via scientific articles and reports from the past 30 years of vaccine research, as well as the most recent studies on COVID-19. The information has been checked by experts in immunology and epidemiology as of May 12th 2021.’

By contrast, Altay et al. 's chatbot featured trust as one of the main question options in their chatbot condition (why should I trust you?), with a response similar to our wording above. If trust drives effectiveness of vaccine interventions then this could have driven the difference between their conditions, rather than the presence/absence of a chatbot per se. We therefore removed this question and answer from the dialogue options and inserted it at the beginning of both conditions, to ensure all participants would see it regardless of condition or choice of information. This ensured the communication of trustworthiness of our information was consistent across both conditions.

Our post-experiment questionnaire consisted of the same COVID attitude questions as the pre-experiment questionnaire, as well as questions on how engaging the experience was and how clear the information was. We also asked how often participants discuss vaccination with those who disagree with them and how often they actively learn about vaccines (e.g. via reading articles, listening to podcasts). Participants were finally asked if they would recommend our study to a friend (if yes, they were given the option to share a link via Twitter or Facebook and we recorded the proportion that did), whether they would take part again in a month's time, their age, gender and education level.

We included an attention check question among both the pre-experiment questionnaire and our post-experiment questionnaire ('We would like to check that you are paying careful attention to the information in this study. Please respond to the following item with 'somewhat agree'.). We used both of these attention check answers alongside a free-response answer to check that participants were attending to the study information, i.e. we only included those that passed both attention checks and provided coherent, relevant information in the free-response text boxes (free-response text boxes were used to collect data for a different study question).

2.4. Procedure

Participants were randomly assigned to either the control or choice condition. Participants in both conditions provided informed consent (ethical approval provided by the University of Sheffield) before answering the pre-exposure questionnaire, interacting with the experimental material, and finally answering a post-exposure questionnaire.

In our control condition, participants viewed four randomly chosen dialogues of between 200–500 words each, one from each of the five possible domains of vaccination concern: (i) is the vaccine safe? (ii) is the vaccine effective? (iii) has the vaccine been rushed? (iv) can we trust who makes the vaccine? and (v) is the vaccine necessary?

In our choice condition, participants were able to choose four dialogues in total of between 200 and 500 words each, one from each of the five possible domains of vaccination concern, as above. Each of the five domains contained four sub-questions. Thus, participants had four choices, with one choice from each of the five main domains each time. This ensured the amount of information that the participants were exposed to was the same as in the control condition. The information is displayed identically between the two conditions, in 200–500 word chunks at a time, so the information should be equally engaging and easy to read. This was also to ensure a similar engagement time across both conditions. These controls attempt to isolate any effect of choice of information (interactivity) as a cause of difference between the conditions.

2.5. Analysis

All models were run using the Rethinking package in R for Bayesian models [ 14 ]. We include model parameters based on a priori pre-registered hypotheses. Throughout the manuscript, we report mean model coefficients with their 89% credible intervals (CIs). Model parameters were said to have an effect on the model outcome if their 89% CI did not cross zero. Eighty-nine per cent intervals are the default CI setting for the Rethinking package, as they discourage interpreting results in terms of binary null hypothesis significance testing [ 14 ]. Ninety-five per cent intervals would not alter the interpretation of our results. When relevant, we used model comparison to aid the interpretation of results. Models were said to be a better fit to the data if their widely applicable, or Wanatabe-Aike information criterion value held the most weight out of all models tested.

Priors were chosen to be weakly regularizing, in order to control for both under- and overfitting the model to the data [ 14 ]. All models were checked for convergence using convergence criteria such as Rhat values and effective sample sizes, as well as visual inspection of trace plots.

In line with our pre-registration, we analysed whether participants increased their intention to be vaccinated using a Bayesian binomial regression model with an increase (either from ‘no’ to ‘undecided’, or from ‘undecided’ to ‘yes’) coded as a 1 (did increase intention), and all other instances as 0 (did not increase intention). We also analysed whether there was a reduction in the number of participants reporting that they would not get vaccinated, by modelling ‘no’ as 1, and all other responses as 0. The second approach was included after observing an increase in the percentage of participants changing from a ‘no’ to another category that was similar to the increase that Altay et al. found. Our analysis strategy differed slightly from Altay et al. 's, thus after we failed to find the condition effect they found, we performed an equivalent analysis to theirs. Both of these approaches are reported below.

In line with our pre-registration, when modelling Likert scale vaccination attitude responses, as well as Likert scale engagement ratings, we used ordinal categorical multi-level models, with varying intercepts for who the rater was, and for Likert scale item. This allowed us to use each Likert scale item as the unit of analysis, rather than average over several items, in accordance with recommendations on how to treat Likert scale data Liddell & Kruschke [ 15 ]. It also allows us to preserve and use all of the information and variation, and account for data clustering within items and individuals [ 14 ].

3.  Results

3.1. pre-registered hypotheses.

We found that participants reporting that they did not intend to get the vaccine decreased after our experiment, regardless of condition, as the number of those reporting they would not get the vaccine decreased in the post-exposure measure (mean model estimate: −0.3630144; 89% CI: −0.5304443, −0.1945209). Against prediction 1, those in the choice condition were not more likely to increase their intention to have the vaccine compared to the control condition (mean: −0.2151165; 89% CI: −0.5657521, 0.144324). These shifts in intention can be seen in table 1 and are equivalent to those found in Altay et al. 's chatbot condition; in Altay et al. 's chatbot condition, 36% of participants reported that they did not intend to get vaccinated, and this dropped to 29% afterwards. Across both our conditions, 53% reported that they did not intend to get vaccinated and this dropped to 44% afterwards (figures ​ (figures1 1 and ​ and2 2 ).

Number of participants reporting that they do not (no), are undecided, or do (yes) intend to get vaccinated pre- and post-exposure to the dialogues in each condition.

Density plot of raw vaccination attitudes before and after the experiment. 7 = strongly agree and 1 = strongly disagree with the five vaccination items: (i) vaccines are safe, (ii) vaccines are effective, (iii) they have not been rushed, (iv) those who make them can be trusted; and (v) they are necessary.

Violin plot of average vaccination attitudes before and after the experiment. 1 = negative attitudes, 7 = positive attitudes. The green lines show an increase in positive vaccination attitudes, and red lines show a decrease.

We also found that vaccine attitudes increased across both conditions (mean: 1.9968003; 89% CI: 1.9261025, 2.0663825). Against prediction 2, as there was not an interaction between condition and time of ratings in our full model (mean: −0.0922423; 89% CI: −0.2379752, 0.0436521); vaccine attitudes were not most positive in the post-treatment ratings of the choice condition, but increased similarly in both conditions. This interpretation was confirmed by a model comparison approach, in which we compared models including parameters for condition, post-treatment rating, and an interaction between condition and post-treatment rating. The best-fitting model included only the experiment effect, with the worst fitting models containing the interaction effect, and just varying intercepts (null model), suggesting that the experiment effect (change across both conditions) was most informative in predicting the difference in vaccination attitudes (see the electronic supplementary material). Increase in average vaccination attitudes can be seen in the violin plot in figure 2 .

This change in vaccination attitudes is displayed in figure 1 , which shows the raw vaccination attitude ratings before and after the experiment. Figures displaying the differences in vaccination attitudes within different scale items (e.g. are they safe, are they effective, have they been rushed, can we trust those who makes them, are they necessary) can be found in the electronic supplementary material. These figures suggest that the majority of our sample agreed that vaccines are effective, but were undecided as to whether they are safe, and disagreed that we can trust those who produce them, that there has been enough time to produce them, and that they are necessary.

Against prediction 3, we did not find that the choice condition was rated as more engaging than the control condition (mean: 0.1581001; 89% CI: −0.0310088, 0.3483672).

3.2. Exploratory analysis

Overall, we found that the number of people who said that ‘no’ they would not get a vaccination when one was offered to them decreased after taking part in our experiment. Out of 571 participants reporting that they either would not, or were undecided, about getting the vaccine, 93 reported being more likely to get vaccinated after the experiment (16% increase). Out of these 93, six changed directly from a ‘no’ to a ‘yes,’ 25 went from an ‘undecided’ to a ‘yes’ and 62 went from a ‘no’ to an ‘undecided’.

As [ 6 ] found a stronger effect for those who spent the most time with the chatbot, we wanted to check whether a condition effect was present in those who spent more time with the information. The median amount of time spent viewing the information was 4 min, and we found that participants who spent above the median amount of time viewing the information (between 4 and 16 min, so between 1 and 4 min per dialogue) were more likely to increase their vaccination attitudes compared to those who spent less time viewing the information. We found a positive interaction between those who spent above the average amount of time and their post-treatment rating (mean: 0.4778941; 89% CI: 0.3416222, 0.6054146). This was confirmed by model comparison, in which the model including the interaction effect, as well as a main effect for post-treatment rating, was the best-fitting model (details in the electronic supplementary material).

When looking only at those who spent above the median amount of time with the information, we again found no effect of condition on intention to get vaccinated (mean: −0.165634; 89% CI: −0.6404874, 0.2988044).

By contrast, participants who spent above the average amount of time viewing the information were not more likely to show an increase in their intention to get vaccinated compared to the rest of the participants (mean: 0.2068541; 89% CI: −0.1461917, 0.5704015).

4.  Discussion

We ran an experiment to test if the choice of information is a crucial factor driving the effectiveness of a COVID-19 vaccination chatbot. We recruited 716 adults based in the UK who had previously said they were ‘against’ or ‘neutral’ towards COVID-19 vaccines. Based on a chatbot experiment conducted with French participants [ 6 ], we created 20 dialogues split across the five topics: how safe the vaccines are, how effective they are, whether there has been enough time to develop them, whether we can trust who makes them, and whether they are necessary for young and healthy people. Participants were randomly assigned to two conditions; in one, they could choose the dialogues they saw (choice condition), in the other, the dialogues were randomly displayed (control condition). Overall, we found that, in both conditions, participants' vaccination attitudes and intentions shifted in a more positive direction after reading the dialogues; we found no difference between the choice and control condition. Crucially we found that participants who spent above the average (median) amount of time viewing the information (between 4 and 16 min, or between 1–4 min per dialogue) were more likely to increase their vaccination attitudes than those who spent below the average (median) amount of time viewing the information. This association between viewing time and increased change was not found for intentions.

Our results have implications in the light of recent interest in using chatbots or other interventions to increase vaccination uptake. We conclude that creating an engaging experience for participants that encourages them to spend quality time with the information is key for increasing positive attitudes towards vaccination.

The size of the shift in intentions we observed was similar to the results of the Altay et al . chatbot condition. In this sense, we provide a conceptual replication of their results. This is reassuring as we used identical information to theirs, only editing the information to be more appropriate for a UK-based audience and with the latest epidemiological information at the time. In both their and our experiment, we found an effect of time spent with the information, in that those who spent longer with the chatbot were more likely to increase their vaccination attitudes. This has potentially important implications for those designing public health information interventions, in that how engaging the material is (and therefore how long participants are willing to attend to the information) is crucial.

In contrast with the Altay experiment, we found no difference in effectiveness between our conditions. However, there were crucial differences between our conditions and those of Altay et al . The most obvious is that all of our participants saw information of the same length and quality. The fact that our conditions were equally effective then suggests that Altay et al .'s chatbot may have been more effective than their control condition not because there is something inherently effective about chatbots per se, but simply because it delivered more information than the control condition, as supported by Altay et al . [ 7 ]. The second crucial difference between our experiment and Altay et al .'s is that we controlled for trustworthiness of information across both of our conditions. In Altay et al .'s chatbot experiment, the chatbot included a question ‘why should I trust you?’ in which, if participants chose it, they saw information about who the researchers were and what their motives were. Previous research suggests trust plays a huge role in how effective science communication is [ 16 ]. The information in Altay's control condition was therefore implicitly less trustworthy than the chatbot information, given the control condition had no source and was anonymous. By contrast, both of our conditions included the ‘Why should I trust you?’ information at the start of the experiment, before any of the other dialogues were displayed. This included who we (the authors) are, where the information came from and what our motives are. We also stated that we had no links to pharmaceutical companies or any other vested interests. The fact that both of our conditions included this information on trustworthiness, and that both of our conditions were similarly effective at increasing positive attitudes and intentions, implicitly suggests that being transparent about the source of information could be a crucial component for shifting vaccine attitudes and intentions. Of course, because our conditions did not differ in this way, this needs to be experimentally verified in future work. Nevertheless, the indirect comparison to Altay et al .'s results, in which the chatbot contained trust information and was more effective than the control that did not, further suggests communicating trust could be an important factor.

By design, the only difference between the current study's two conditions was that in the experimental condition participants had a choice over which information they saw, whereas in the control condition the information was shown at random. This suggests that having agency or ‘choice’ over the information one engages with may not be the most crucial aspect of why chatbots are effective. It suggests that addressing the concerns that are of most importance or interest to the participant may not be as crucial as previously thought, although it is important to note that all information was originally chosen to address common concerns of the vaccine-hesitant. Previous research suggests participants prefer choice and agency over information when given the choice, but perhaps this preference isn't enough to override the effectiveness of accurate and relevant information in general. Importantly, we did not find a difference in engagement ratings between our conditions, and participants spent a similar amount of time across both conditions. Again, when we compare to Altay et al. 's chatbot, we see that participants spent longer with their chatbot on average than with our information, and that time spent on the task is related to change in attitudes. These comparisons suggest that chatbots are most effective because of their ability to hold the attention of the participant and thus spend more time engaging with the information. Seemingly unimportant details of chatbots may account for their being engaging, than standard text, for example, the ‘social’ element of interacting with another ‘agent’ may be inherently more engaging, or simply the display of the information, which is often more ‘bitesize’ and delivered one sentence at a time.

It could be argued that our results are simply demonstrating a regression to the mean, particularly because we recruited from one end of the vaccination attitude spectrum, and we saw similar effects across both conditions. However, after investigating this possibility, it seems unlikely given that those who were rated as ‘against’ vaccination as opposed to ‘neutral’ were actually more likely to stay the same in their reported intention to get the vaccine than the neutral participants and were less likely than the neutral participants to increase their intention to get vaccinated (i.e. the opposite of what you would expect with regression to the mean). A plot displaying this is included in the electronic supplementary material. Furthermore, not only are our percentage changes very similar to Altay et al .'s chatbot condition effects, who were recruited from the general population and not specifically against vaccination, but also much greater than those in previous studies, for example when influenced by norms, participants only showed a 5% decline in rating themselves as ‘undecided’ or ‘against’ vaccination [ 4 ], whereas we found a 16% decline. Previous research also suggests that using question and answer (Q&A) style information is more effective than presenting pure fact-based information, again reporting similar effects to ours [ 3 ].

One aspect of our study worth noting is how the information was framed and how the participants were addressed throughout the study. Participants were asked if they were either against, for or neutral towards the COVID-19 vaccines, as it is worded in Prolific's pre-screening criteria. We thus used this as our wording and advertised the study as ‘Your opinions on COVID-19 vaccinations’. Part of our study (results not included for this publication) was to ask participants to imagine and put forward the opposite side's reasons for and against vaccination (this was conducted after their second round of attitude and intention measures, so would not influence the results of this study). We also offered participants an opportunity to provide any other feedback they had in an ‘anything else’ box. These comments were insightful, and often hinted that participants were keen to have an outlet for their views. Anonymity perhaps allowed them to be honest, and we also noted many thanked us for not referring to them, or anyone, as ‘anti-vaxxers’. We refrained from using this term throughout as this term is often used to stereotype or villainize those who hold those views. We wish to follow-up these comments, respond where necessary and share them with the rest of the research community to help further destigmatize those who are vaccine hesitant and help create an atmosphere of constructive dialogue and conversational receptiveness about these issues [ 17 ]. Comments are included in the Shiny App available at https://lottybrand.shinyapps.io/vaccineComments/ .

Overall, we suggest it is important when designing science communication interventions to control for the amount of information, time spent with the information, trustworthiness of information and consequently to ensure a high level of engagement with the information. Simply providing Q&A style dialogues appeared to be as effective as delivering the same information via a chatbot, and more effective than previous studies using norms or simple fact-based interventions.

Acknowledgements

Thank you to Dr Andrew Lee from the University of Sheffield who kindly donated his epidemiological expertise and proof-read our updated COVID-19 vaccination FAQs. Thank you also to Dr Sacha Altay for openly sharing the data and analysis scripts, and being forthcoming with more details and extra information to help run this replication.

This study was granted ethical approval by the University of Sheffield's ethics board on 21 April 2021 (application number 038906).

Data accessibility

Authors' contributions.

C.O.B.: conceptualization, data curation, formal analysis, investigation, methodology, project administration, visualization and writing—original draft; T.S.: conceptualization, funding acquisition, supervision and writing—review and editing.

Both authors gave final approval for publication and agreed to be held accountable for the work performed therein.

Conflict of interest declaration

We declare we have no competing interests.

The authors are funded by the EPSRC project EP/T024666/1 ‘Opening Up Minds: Engaging Dialogue Generated From Argument Maps’.

Restoring community dialogue and resilience: The next COVID-19  emergency

Associate Professor, Faculty of medicine and health sciences, Université de Sherbrooke

Associate professor, School of applied politics, Scientific codirector, CIDIS (Centre interdisciplinaire de développement international en santé), Université de Sherbrooke

Professeur associé, Faculté de médecine et des sciences de la santé, Université de Sherbrooke

Disclosure statement

The authors do not work for, consult, own shares in or receive funding from any company or organisation that would benefit from this article, and have disclosed no relevant affiliations beyond their academic appointment.

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COVID-19 is not the first health crisis to affect Canada. Previous emergencies, like the Lac-Mégantic train tragedy in 2013 , showed the importance of including the affected communities to promote better adherence to preventive measures and build resilient communities. Our research shows this is largely missing for COVID-19, with high costs on society as a whole .

Resilience is the ability of a community to continue to live, function, develop and thrive after a crisis. Key elements of enhancing resilience include maximizing social cohesion, collaboration, empowerment, participation and consideration of local characteristics and issues. This means dialogue with, and inputs from, the affected communities.

There is a major risk of a community becoming “ corrosive ” if these elements are not appropriately taken into account. Corrosive communities are at risk of division, polarization and psychological impacts such as anxiety and depression. These are the costs Canadians may have to pay for the divisive approach used in response to COVID-19.

Our multidisciplinary research team at the University of Sherbrooke has been using surveys to evaluate and compare the different effects of the COVID-19 pandemic since February 2020. Different waves of national and international surveys confirm our original findings: the psychosocial impact of the pandemic and responses to it are immense.

Unfortunately, the governmental approach is still divisive, using arguments such as the 90 per cent vaccinated are paying for the inaction of the 10 per cent unvaccinated , that some might be subject to more restrictive measures than others , or that vaccine hesitancy is only prompted by conspiracy seekers and non-believers of science, which is contradicted by our data showing that one-third of unvaccinated people do not hold these beliefs .

This “us against them” strategy is amplifying social division and has major psychosocial impacts, including stress and mental health issues. Our data indicates that this strategy has resulted in a significant decrease in trust toward public health authorities and governments.

Pandemic fatigue

We conducted our most recent survey online from Oct. 1-17, 2021 among 10,368 adults from all regions of Québec and 1,001 adults in the rest of Canada. The results showed half of the adults from across Canada (and, in Québec, nearly two-thirds of young adults) suffer from “pandemic fatigue.”

Pandemic fatigue is a normal and expected response to chronic adversity, but when exacerbated, it can jeopardize not only how we, as communities, respond to the current crisis, as shown by our data , but also how we will react to future ones — a key ingredient in building resilient communities.

Our results showed pandemic fatigue manifests itself through anxiety, depression and suicidal thoughts, issues affecting 21.9 per cent, 25.6 per cent and 9.4 per cent of Canadians, respectively.

The ‘public’ in public health

There is an urgent need to rebuild a safe public space. The population and its representatives (including opposition parties, citizens’ groups and community leaders) need access to sufficient information to monitor the government’s actions, including real-time and raw COVID-19 data. They need to be able to offer criticism and propose alternative solutions, but also feel accepted despite their different viewpoints on the crisis. We must allow a return of the “public” in public health.

As underlined by the World Health Organization (WHO), governments should act in such a way that citizens and communities can regain some form of power and autonomy in their daily lives. They must feel and perceive that they are seen as legitimate citizens, even when they disagree with the government. This should be guided by five major principles: transparency, consistency, predictability, fairness and co-ordination .

The most important challenge, we argue, is one of coherence, where citizens’ questions and criticisms must be addressed directly rather than ignored, deemed irrelevant or used against those asking them . This will help increase the “sense of coherence” of affected populations, a key factor in building resilient communities.

We define a sense of coherence as a “ psychological resource that helps to understand a stressful event, to give meaning to it, and to manage it .” The higher the sense of coherence is, the better we can face adversity and stressful events.

For example, our data shows that those with a high sense of coherence are three times less likely to experience anxiety and depression . The sense of coherence can be directly affected by the strategies put in place by governments and authorities to respond to crises. Our data suggests that, overall, Canadians’ sense of coherence decreased during the pandemic.

Dialogue with communities

The health emergency Canada still faces should not be underestimated, and as the WHO reiterates, the pandemic is far from over. However, not all policies and measures need to be implemented through “emergency” procedures or justified by the state of emergency, as seen widely in Canada right now. The response to COVID-19 must rely on a stronger democracy, where citizens and communities can express themselves, exchange and reflect and, by doing so, bring back meaning and coherence in their daily lives.

Dialogue with affected communities is still left aside in responses to the pandemic, amplifying skepticism and beliefs in erroneous information . Our research also underlines an increase in political polarization, deepening already existing gaps between communities .

The spectrum of citizen participation can be quite diverse, but our data suggest that the current COVID-19 strategy based on the information moving only in one direction — in which citizens and communities assume very little responsibility — is a wrong one. The recognition of past mistakes, humility and better community involvement should be the cornerstones of our responses to this crisis, with citizen and community inclusion.

Bringing back dialogues between authorities and communities affected by the pandemic is a real emergency. The long-term health of individuals and communities is at stake.

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Two Minute Tips

Tips on writing about COVID-19

Susan Johnston Taylor

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As readers cope with new realities in response to COVID-19, providing them with clear and accurate information is more important than ever. AP Stylebook held a Twitter chat on March 23 to share style tips on writing about the pandemic, and other experts like Mignon Fogarty of Grammar Girl and Roy Peter Clark of Poynter have shared their own tips. 

Savvy reporters use many of these strategies already, but it’s a worth a refresher, especially when they’re writing under tight deadlines or with little sleep. Here’s an overview of their advice with links to additional information. 

Use punctuation 

Many people feel overwhelmed right now, so the last thing they need is a series of long, complex sentences, especially when that information is crucial to public safety. “Think of the period as a stop sign,” writes Clark in his  recent article for Poynter  (worth a read in its entirety). “The more stop signs, the slower the pace, which is good if you are trying to make something clear.” 

Clark also suggests keeping the subject and verb together near the beginning of the sentence, as overly complex sentences can confuse readers. 

Avoid unfamiliar acronyms 

Readers may not know that  PPE stands for personal protective equipment  or that  WHO stands for the World Health Organization , so remember to write out the full name on first reference. The AP Stylebook says WHO or the WHO are both acceptable on second reference.  

Choose correct terms 

Coronavirus is technically a family of viruses, but in the current context, it clearly references a specific virus.  Coronavirus and COVID-19  are both correct, according to Fogarty and other sources (never use  geographic labels  when referencing the name of the virus). 

A pandemic is more serious than an epidemic, and the WHO has declared  COVID-19 a pandemic , so pandemic is correct. No need to say “global pandemic” as pandemic means the outbreak has spread to several countries or continents. 

Merriam-Webster defines some  terms recently added to the dictionary  such as socially distance. 

Know when to hyphenate

When you’re using to-go as an adjective (as in “we placed a to-go-order”), it should be hyphenated, according to Fogarty. However, AP Stylebook says  no hyphen  in telecommute or videoconference. Ditto on  N95 masks . Read  AP Stylebook’s coronavirus topical guide  for more guidance.  

UPDATE: The Canadian Association of Journalists published a list of tips and best practices including safety precautions and tools for working remotely.

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How to talk to your friends and family about vaccines, tips for handling tough conversations with your loved ones..

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Vaccines save 2 to 3 million lives each year and are amongst the greatest advances of modern medicine. However,  there are still some people who are skeptical or hesitant about vaccines. Chances are you know a person who falls into this category – maybe among your group of friends or in your family.

If you are unsure of how to approach conversations about vaccines with vaccine skeptics you know, you’re not alone. We spoke to epidemiologist Dr. Saad Omer, Dean of the O'Donnell School of Public Health at UT Southwestern, about the do’s and don’ts of navigating these difficult discussions.

Do connect with their values.

Even if you are feeling frustrated, it is important to be empathetic. “Make them feel heard,” advises Omer. Attempt to connect with their underlying sentiment. For example, if they are worried about protecting their loved ones, connect with them on the fact that vaccines have been proven to keep people safe against diseases that used to take millions of lives before they were developed – one example being measles.

Don’t interrupt.

Make sure not to cut off, speak over or jump into correcting your loved one. Listen to the person you are talking to and meet them where they are. “You shouldn’t agree with any false information, but you should empathize and continue the process rather than ending your relationship or ending the conversation,” says Omer.

Do help them feel empowered.

If you're speaking to someone especially scared of illnesses, Omer suggests giving them an empowering message: You can do something about protecting yourself and others from disease. “[They] can do something about it. These vaccines work.”

Don’t focus on the myths.

“Be careful about countering a misperception too directly,” says Omer. The discussion shouldn’t be all or mostly about addressing a specific myth because there will always be more myths that follow. Calling attention to a myth can also backfire by making the myth more memorable than the facts. But sometimes, you cannot get out of addressing misinformation. If you find yourself in that position, Omer suggests the following approach: fact, warning, fallacy, fact. Here’s how it works:

• Start with the fact. Vaccines are extremely safe and effective.
• Warn before the myth is coming. Say, “there is misinformation about______.”
• Mention the fallacy (myth) that you are addressing.
• End with the fact. Show why the myth is not true.

The most important thing is to “replace the misinformation with the correct information,” explains Omer.

Do assume they are going to get vaccinated.

Simply say to your friend or family member, “Let’s go get vaccinated!” This method is called presumptive communication. “The announcement approach or presumptive approach has been shown to be successful in the clinic and is likely to work in personal communication,” says Omer. You’re not taking away someone’s autonomy, all you are doing is establishing a verbal default.

Don’t get discouraged.

Convincing someone who is opposed to vaccines is a long process. “It’s extremely tough,” says Omer. Remember that for those who are strongly opposed to vaccines in general, their opinions will not likely be changed in one conversation. The important thing? “Maintain a connection with them.”

Interview and article by Mandy Letterii, Digital Content Writer, UNICEF

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Is it a Cold, the Flu, or COVID-19?

The common cold, flu, and COVID-19 all have similar symptoms. Knowing the signs of a cold, the flu, and COVID-19 can help keep you and your loved ones safe.

These are common signs, but your symptoms may be more or less severe, or you may only have a few. If you feel sick, stay home and call your doctor to discuss how you’re feeling and whether you need to get tested. Older adults are more likely to become seriously ill from the flu and COVID-19. Getting vaccinated is the best way to prevent some of these diseases.

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[Note: The content is in a chart of four columns and 11 rows that describes the common symptoms of a cold, the flu, and COVID-19. Symptoms may vary based on new COVID-19 variants and vaccination status.]

Common symptoms of a cold include sore throat, runny or stuffy nose, sneezing, and cough.

Common symptoms of the flu include fever and/or chills, headache, muscle pain or body aches, feeling tired or weak, sore throat, runny or stuffy nose, sneezing, cough, shortness of breath or difficulty breathing, vomiting, and diarrhea.

Common symptoms of COVID-19 include fever and/or chills, headache, muscle pain or body aches, feeling tired or weak, sore throat, runny or stuffy nose, sneezing, cough, shortness of breath or difficulty breathing, vomiting and diarrhea, and change in or loss of taste or smell.

Learn more about the flu and older adults .

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• Paragraph Writing On Covid 19

Paragraph Writing on Covid 19 - Check Samples for Various Word Limits

The Covid-19 pandemic has been a deadly pandemic that has affected the whole world. It was a viral infection that affected almost everyone in some way or the other. However, the effects have been felt differently depending on various factors. As it is a virus, it will change with time, and different variants might keep coming. The virus has affected the lifestyle of human beings. The pandemic has affected the education system and the economy of the world as well. Many people have lost their lives, jobs, near and dear, etc.

Table of Contents

Paragraph writing on covid-19 in 100 words, paragraph writing on covid-19 in 150 words, paragraph writing on covid-19 in 200 words, paragraph writing on covid-19 in 250 words, frequently asked questions on covid-19.

Check the samples provided below before you write a paragraph on Covid-19.

Coronavirus is an infectious disease and is commonly called Covid-19. It affects the human respiratory system causing difficulty in breathing. It is a contagious disease and has been spreading across the world like wildfire. The virus was first identified in 2019 in Wuhan, China. In March, WHO declared Covid-19 as a pandemic that has been affecting the world. The virus was spreading from an infected person through coughing, sneezing, etc. Therefore, the affected people were isolated from everyone. The affected people were even isolated from their own family members and their dear ones. Other symptoms noticed in Covid – 19 patients include weariness, sore throat, muscle soreness, and loss of taste and smell.

Coronavirus, often known as Covid-19, is an infectious disease. It affects the human respiratory system, making breathing difficult. It’s a contagious disease that has been spreading like wildfire over the world. The virus was initially discovered in Wuhan, China, in 2019. Covid-19 was declared a global pandemic by the World Health Organization in March. The virus was transferred by coughing, sneezing, and other means from an infected person. As a result, the people who were affected were isolated from the rest of society. The folks who were afflicted were even separated from their own family members and loved ones. Weariness, sore throat, muscle stiffness, and loss of taste and smell are among the other complaints reported by Covid-19 individuals. Almost every individual has been affected by the virus. A lot of people have lost their lives due to the severity of the infections. The dropping of oxygen levels and the unavailability of oxygen cylinders were the primary concerns during the pandemic.

The Covid-19 pandemic was caused due to a man-made virus called coronavirus. It is an infectious disease that has affected millions of people’s lives. The pandemic has affected the entire world differently. It was initially diagnosed in 2019 in Wuhan, China but later, in March 2020, WHO declared that it was a pandemic that was affecting the whole world like wildfire. Covid-19 is a contagious disease. Since it is a viral disease, the virus spreads rapidly in various forms. The main symptoms of this disease were loss of smell and taste, loss of energy, pale skin, sneezing, coughing, reduction of oxygen level, etc. Therefore, all the affected people were asked to isolate themselves from the unaffected ones. The affected people were isolated from their family members in a separate room. The government has taken significant steps to ensure the safety of the people. The frontline workers were like superheroes who worked selflessly for the safety of the people. A lot of doctors had to stay away from their families and their babies for the safety of their patients and their close ones. The government has taken significant steps, and various protocols were imposed for the safety of the people. The government imposed a lockdown and shut down throughout the country.

The coronavirus was responsible for the Covid-19 pandemic. It is an infectious disease that has affected millions of people’s lives. The pandemic has impacted people all across the world in diverse ways. It was first discovered in Wuhan, China, in 2019. However, the World Health Organization (WHO) proclaimed it a pandemic in March 2020, claiming that it has spread throughout the globe like wildfire. The pandemic has claimed the lives of millions of people. The virus had negative consequences for those who were infected, including the development of a variety of chronic disorders. The main symptoms of this disease were loss of smell and taste, fatigue, pale skin, sneezing, coughing, oxygen deficiency, etc. Because Covid-19 was an infectious disease, all those who were infected were instructed to segregate themselves from those who were not. The folks who were affected were separated from their families and locked in a room. The government has prioritised people’s safety. The frontline personnel were like superheroes, working tirelessly to ensure the public’s safety. For the sake of their patients’ and close relatives’ safety, many doctors had to stay away from their families and babies. The government had also taken significant steps and implemented different protocols for the protection of people.

What is meant by the Covid-19 pandemic?

The Covid-19 pandemic was a deadly pandemic that affected the lives of millions of people. A lot of people lost their lives, and some people lost their jobs and lost their entire families due to the pandemic. Many covid warriors, like doctors, nurses, frontline workers, etc., lost their lives due to the pandemic.

From where did the Covid-19 pandemic start?

The Covid-19 pandemic was initially found in Wuhan, China and later in the whole world.

What are the symptoms of Covid-19?

The symptoms of Covid-19 have been identified as sore throat, loss of smell and taste, cough, sneezing, reduction of oxygen level, etc.

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Global dialogue on responding to the covid-19 pandemic and economic crisis: building back better aligned to the sdgs and the paris agreement, attachments.

Summary for Policymaking

An early glimpse of the future

The COVID-19 crisis provides an early glimpse of how the climate and biodiversity crises will afect the world. The impacts of the pandemic and economic lockdown have led to a stark decline in development gains, disproportionately afecting low-income and vulnerable households, communities and countries. Disparities have sharpened within countries and between developed and developing countries; the latter has experienced a “perfect storm” of unemployment, capital fight, loss of remittances, and increasing debt leading to the largest economic contraction in decades.

Though slower in onset, the climate and biodiversity crises will ultimately be deeper and broader in impact, undercutting our ability to achieve the Sustainable Development Goals (SDGs). Moreover, these crises are interlinked; the shrinking space between natural and human systems is one of the root causes for zoonotic pandemics.

Yet the lockdown demonstrated extraordinary interventions are possible. Safeguarding human health was put at the center of policymaking and public investment. And we experienced a diferent world, a postcard from the future: cleaner air and water, less trafc and noise, and often more engagement with community, family and nature. While the severe pain of the crisis must not be underestimated, these experiences can help us envision the future we want. Building an inclusive, green and resilient recovery is now an urgent and shared global challenge. We must build back in a way that addresses the very signifcant near-term challenges of unemployment, food insecurity and jump-starting the economy, while tackling the underlying drivers of climate change and biodiversity loss. Because stimulus packages are emerging at lightning speed and the power of incumbency and inertia is strong, we need to quickly build public and political support for change.

It is essential to shift from snapshot to transition thinking. We should consider three categories for the recovery: the industries and technologies of the future (such as renewable energy, electric vehicles, and sustainable agriculture) that must be accelerated; those of the past (such as coal power) that must be phased out; and those in transition (such as steel, automotive and aviation) that must be shifted toward transformation. While recovery eforts will likely be uneven and extend over several years, the critical timeframe for action is the next 15 months, as countries invest $10-20 trillion or more for relief and recovery. How countries and the international community pursue the recovery will determine the climate and sustainable development trajectory for the coming decade.

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#Contoh Dialog Tentang Covid 19

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Contoh-Contoh Dialog Tentang COVID-19 dalam Bahasa Inggris Lengkap dengan Terjemahannya

Bobo.id - Pandemi COVID-19 masih berlangsung di hampir seluruh negara di dunia.

Akibatnya, hampir setiap orang akan membicarakan tentang COVID-19. Apakah teman-teman sudah pernah mendengar percakapan mengenai COVID-19 dari orang-orang di sekitarmu?

Kali ini, simak contoh dialog mengenai COVID-19 dalam Bahasa Inggris , yuk!

Baca Juga: Contoh Dialog Asking and Giving Opinion dalam Bahasa Inggris, Serta Kalimat yang Tepat untuk Digunakan

Contoh Dialog 1

Buyer: Hello, I would like to buy two boxes of mask, please.

Seller: Sure. What kind of mask do you want?

Buyer: The green one, Ma'am.

Seller: Here you go. You buy a lot of masks, I think.

Buyer: Yes, Ma'am. This COVID-19 pandemic makes me use a lot of mask.

Seller: Of course, mask can protect us from the virus.

Buyer: Yes, that's why I buy a lot of mask today. Here's the money, Ma'am. Thank you.

Seller: Of course, you're welcome.

Bahasa inggris

Contoh dialog covid-19 dalam bahasa inggris.

Kamu tim belanja online atau offline?

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Contoh Dialog COVID-19 dalam Bahasa Inggris dan Terjemahannya

adjar.id -  Pandemi COVID-19 masih berlangsung di hampir seluruh negara di dunia, Adjarian.

Kita harus selalu menjaga kesehatan kita dan juga keluarga kita.

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Apakah Adjarian pernah, mendengar percakapan mengenai COVID-19 dari orang-orang di sekitar?

Di bawah ini, terdapat beberapa contoh dialog yang sedang membicarakan tentang COVID-19 yang bisa dijadikan contoh percakapan, Adjarian.

Yuk, kita simak bersama contoh dialog tentang COVID-19 dalam bahasa Inggris beserta terjemahannya di bawah ini!

"Dalam melakukan dialog menggunakan bahasa Inggris tentang COVID-19 usahakan untuk selalu memberi informasi yang akurat atau state of fact."

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Sunnylands Statement on Enhancing Cooperation to Address the Climate Crisis

Office of the Spokesperson

November 14, 2023

Recalling the meeting between President Xi Jinping and President Joseph R. Biden in Bali, Indonesia, the United States and China reaffirm their commitment to work jointly and together with other countries to address the climate crisis. In this regard, U.S. Special Presidential Envoy for Climate John Kerry and China Special Envoy for Climate Change Xie Zhenhua met in Beijing from 16 to 19 July 2023 and at Sunnylands, California, from 4 to 7 November 2023 and released the following: 

1. The United States and China recall, reaffirm, and commit to further the effective and sustained implementation of the April 2021 U.S.-China Joint Statement Addressing the Climate Crisis and the November 2021 U.S.-China Joint Glasgow Declaration on Enhancing Climate Action in the 2020s. 

2. The United States and China recognize that the climate crisis has increasingly affected countries around the world. Alarmed by the best available scientific findings including the IPCC Sixth Assessment Report, the United States and China remain committed to the effective implementation of the UNFCCC and the Paris Agreement, reflecting equity and the principle of common but differentiated responsibilities and respective capabilities, in light of different national circumstances, to achieve the Paris Agreement’s aim in accordance with its Article 2 to hold the global average temperature increase to well below 2 degrees C and to pursue efforts to limit it to 1.5 degrees C, including efforts to keep 1.5 degrees C within reach. 

3. The United States and China remain committed to the effective implementation of the Paris Agreement and decisions thereunder, including the Glasgow Climate Pact and the Sharm el-Sheikh Implementation Plan. Both countries stress the importance of COP 28 in responding meaningfully to the climate crisis during this critical decade and beyond. They are aware of the important role they play in terms of both national responses and working together cooperatively to address the goals of the Paris Agreement and promote multilateralism. They will work together and with other Parties to the Convention and the Paris Agreement to rise up to one of the greatest challenges of our time for present and future generations of humankind. 

4. The United States and China decide to operationalize the  Working Group on Enhancing Climate Action in the 2020s , to engage in dialogue and cooperation to accelerate concrete climate actions in the 2020s. The Working Group will focus on the areas of cooperation that have been identified in the Joint Statement and the Joint Declaration, including on energy transition, methane, circular economy and resource efficiency, low-carbon and sustainable provinces/states & cities, and deforestation, as well as any agreed topics. The Working Group will carry out information exchanges on policies, measures, and technologies for controlling and reducing emissions, share their respective experiences, identify and implement cooperative projects, and evaluate the implementation of the Joint Statement, the Joint Declaration, and this Statement. The Working Group is co-led by the two special envoys on climate change, with the appropriate participation of officials from the relevant ministries and government agencies of the two countries. 

5. The United States and China will, on the road to COP 28 and beyond, accelerate, inter alia, the following concrete actions, including practical and tangible collaborative programs and projects under the Working Group. 

Energy Transition 

6. Both countries support the G20 Leaders Declaration to pursue efforts to triple renewable energy capacity globally by 2030 and intend to sufficiently accelerate renewable energy deployment in their respective economies through 2030 from 2020 levels so as to accelerate the substitution for coal, oil and gas generation, and thereby anticipate post-peaking meaningful absolute power sector emission reduction, in this critical decade of the 2020s. 

7. Both sides agree to restart the U.S.-China Energy Efficiency Forum to deepen policy exchanges on energy-saving and carbon-reducing solutions in key areas including industry, buildings, transportation, and equipment. 

8. The United States and China intend to recommence bilateral dialogues on energy policies and strategies, carry out exchanges on mutually agreed topics, and facilitate track II activities to enhance pragmatic cooperation. 

9. The two countries aim to advance at least 5 large-scale cooperative CCUS projects each by 2030, including from industrial and energy sources. 

Methane and Other Non-CO2 GHG Emissions 

10. The two countries will implement their respective national methane action plans and intend to elaborate further measures, as appropriate. 

11. The two countries will immediately initiate technical working group cooperation on policy dialogue, technical solutions exchanges, and capacity building, building on their respective national methane action plans to develop their respective methane reduction actions/targets for inclusion in their 2035 NDCs and support each country’s methane reduction/control progress. 

12. The two countries intend to cooperate on respective measures to manage nitrous oxide emissions. 

13. The two countries intend to work together under the Kigali Amendment to phase down HFCs and commit to ensure application of ambitious minimum efficiency standards for all cooling equipment manufactured.  

Circular Economy and Resource Efficiency 

14. Recognizing the importance of developing circular economy and resource efficiency in addressing the climate crisis, relevant government agencies of the two countries intend to conduct a policy dialogue on these topics as soon as possible and support enterprises, universities, and research institutions of both sides to engage in discussions and collaborative projects. 

15. The United States and China are determined to end plastic pollution and will work together and with others to develop an international legally binding instrument on plastic pollution, including the marine environment. 

Subnational Cooperation 

16. The United States and China will support climate cooperation among states, provinces, and cities with regard to areas including, inter alia, the power, transportation, buildings, and waste sectors. Both sides will facilitate subnational governments, enterprises, think tanks, and other stakeholders to actively participate in the cooperation. The two countries will meet periodically, as agreed, for policy dialogue, best practices sharing, information exchange, and to facilitate cooperative programs.  

17. The United States and China intend to hold a high-level event on subnational climate action in the first half of 2024. 

18. Both sides welcome with appreciation existing subnational cooperation between the two countries and encourage states, provinces, and cities to promote practical climate cooperation. 

Forests 

19. Both sides commit to advance efforts to halt and reverse forest loss by 2030, including by fully implementing through regulation and policy, and effectively enforcing, their respective laws on banning illegal imports. They intend to engage in discussions and exchanges, including under the Working Group, on ways to improve efforts to strengthen implementation of this commitment.  

GHG and Air Pollutant Reduction Synergy 

20. Both countries intend to cooperate in promoting relevant policies and measures and the deployment of technologies to enhance synergy of controlling GHG emissions and air pollutants, including NOx, VOCs, and other tropospheric ozone precursors. 

2035 NDCs 

21. Reaffirming the nationally determined nature of NDCs, and recalling Article 4.4 of the Paris Agreement, both countries’ 2035 NDCs will be economy-wide, include all greenhouse gases, and reflect the reductions aligned with the Paris temperature goal of holding the increase in global average temperature to well below 2 degrees C and pursuing efforts to limit the temperature increase to 1.5 degrees C. 

COP 28 

22. The United States and China, with the United Arab Emirates, invite countries to a Methane and Non-CO2 Greenhouse Gases Summit at COP 28. 

23. The United States and China look forward to the first Global Stocktake under the Paris Agreement, which is a vital opportunity for the Parties to reflect on ambition, implementation, and cooperation, in line with the Paris temperature goal to hold the global average temperature increase to well below 2 degrees C and pursue efforts to limit it to 1.5 degrees C, and the Parties’ resolve to keep a 1.5 degree C limit on temperature rise within reach. 

24. Both countries are committed to working with each other and with other Parties to adopt a consensus Global Stocktake decision. In the view of both countries, the decision: 

• should reflect that there has been substantial positive progress toward achieving the objectives of the Paris Agreement, including that the Agreement has catalyzed action by both Parties and non-Party stakeholders and that the world is considerably better off in terms of its temperature trajectory than it would have been in the absence of the Agreement; 
• should take account of equity and be informed by the best available science, including the most recent IPCC reports; 
• should be balanced across thematic areas, include both retrospective and responsive elements, and be consistent with the design of the Paris Agreement; 
• should reflect that substantially more ambition and implementation on action and support will be needed to achieve the Paris Agreement’s goals, recognizing different national circumstances; 
• should send signals with respect to the energy transition (renewable energy, coal/oil/gas), carbon sinks including forests, non-CO2 gases including methane, and low-carbon technologies, etc.; 
• recognizing the nationally determined nature of NDCs and recalling Article 4.4 of the Paris Agreement, should encourage economy-wide 2035 NDCs covering all greenhouse gases; 
• should reflect the critical importance of adaptation and be accompanied by a robust decision that delivers an ambitious framework for the global goal on adaptation — one that accelerates adaptation, including developing targets/indicators to enhance adaptation effectiveness; delivering early warning systems for developing country Parties; and strengthening adaptation efforts in key areas (e.g. food, water, infrastructure, health, and ecosystems); 
• should note the expectation of the developed countries that the $100b goal will be met in 2023, reaffirm the urging of developed country Parties to at least double their provision of adaptation finance; anticipate the adoption by COP 29 of the new collective quantified goal; and make finance flows consistent with the Paris Agreement goals; 
• should welcome with appreciation the recommendations of the Transitional Committee with respect to establishing funding arrangements to address loss and damage, including the establishment of a fund; and 
• should emphasize the important role of international cooperation, including that the global nature of the climate crisis calls for the widest possible cooperation and that such cooperation is a critical enabler for achieving ambitious mitigation action and climate-resilient development. 

25. The United States and China are committed to further their dialogues, efforts, and collaboration to support the UAE Presidency for the success of COP 28. 

For media inquiries, please contact [email protected]. 

U.S. Department of State

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