To stop the coronavirus, the most successful countries slammed their doors shut to visitors. It worked. Until they let outsiders back in.

To Beat the Coronavirus, Build a Better Fence

Confidence has increased in many of the continent’s countries but declined in other parts of the world

Public trust in vaccines rises in most of Europe, study says

Wouldn’t it be nice, as you go about your confusing, nerve-wracking, Covid-19-avoiding days, to have an epidemiologist on call to answer your many questions? Consider the Covid Audit the next best thing. This project, a collaboration between Elemental and the Epidemiologic Covid-19 Response Corps at the Boston University School of Public Health, asks real people to document what they’re doing to avoid Covid-19 and gives friendly feedback on actions you can take to support both public health — and your own. This week’s reviewers are response corps members Sarah Lincoln and Ivanna Rocha, graduate students in the Master of Public Health program at BU.

This Lawyer Ran Errands for His High-Risk Wife. Then an Epidemiologist Rated His Every Move.

People frequently ask us what high-impact research in different disciplines might look like. This might be because they’re already working in a field and want to shift their research in a more impactful direction. Or maybe they’re thinking of pursuing an academic research career and they aren’t sure which discipline is right for them.Below you will find a list of disciplines and a handful of research questions and project ideas for each one.

Research questions that could have a big social impact, organised by discipline

10.31234/osf.io/qtrpf

Background: The novel coronavirus disease 2019 (COVID-19) has negatively impacted mortality, economic conditions, and mental health. A large scale study on psychological reactions to the pandemic to inform ongoing population-level symptom tracking and response to treatment is currently lacking. Methods: Average intake scores for standard depression and anxiety symptom scales were tracked from January 1, 2017 to June 9, 2020 for patients seeking treatment from a digital mental health service to gauge the relationship between COVID-19 and self-reported symptoms. We applied natural language processing (NLP) to unstructured therapy transcript data from patients seeking treatment during the height of the pandemic in the United States between March 1, 2020 and June 9, 2020 to identify words associated with COVID-19 mentions. This analysis was used to identify symptoms that were present beyond those assessed by standard depression and anxiety measures. Results: Depression and anxiety symptoms reported by 169,889 patients between January 1, 2017 and June 9, 2020 were identified. There was no detectable change in intake depression symptom scores. Intake anxiety symptom scores increased 1.42 scale points [95% CI: 1.18, 1.65] between March 15, 2020 and April 1, 2020, when scores peaked. In the transcript data of these 169,889 patients, plus an expanded sample of 49,267 patients without symptom reports, term frequency-inverse document frequency (tf-idf) identified 2,377 positively correlated and 661 negatively correlated terms that were significantly (FDR<.01) associated with mentions of the virus. These terms were classifiable into 24 symptoms beyond those included in the diagnostic criteria for anxiety or depression. Conclusions: The COVID-19 pandemic may have increased intake anxiety symptoms for individuals seeking digital mental health treatment. NLP analyses suggest that standard symptom scales for depression and anxiety alone are inadequate to fully assess and track psychological reactions to the pandemic. Symptoms of grief, trauma, obsession-compulsion, agoraphobia, hypochondriasis, panic, and non- suicidal self-injury should be monitored as part of a new COVID-19 Syndrome category.

Digital phenotyping of complex psychological responses to the COVID-19 pandemic

10.31234/osf.io/vdw2e

The COVID-19 pandemic poses significant threat to humans’ physical and mental wellbeing. In response, there has been an urgent “call to action” for psychological interventions that enhance positive emotion and psychological resilience. Extending upon past research documenting the wellbeing benefits of generous action, we conducted two online pre-registered experiments (N =1,623) during the pandemic in which participants were randomly assigned to engage in other- or self-beneficial action. Specifically, participants made charitable donations or gained money for themselves (Experiment 1); purchased COVID-19-related or COVID-19-unrelated items for someone else or for themselves (Experiment 2). Results showed that prosocial behavior led to greater positive affect, meaningfulness, empathy and social connectedness. Affect benefits were detectable whether prosocial spending was COVID-19-related or not. These findings provide support for one strategy to bolster wellbeing during the pandemic – generous action – which may also promote cooperation and social cohesiveness needed to contain and overcome the virus.

Prosocial behavior promotes positive emotion during the COVID-19 pandemic

10.31234/osf.io/5b3cn

Exposure to right-wing media has been shown to relate to lower perceived threat from COVID-19, lower compliance with prophylactic measures against it, and higher incidence of infection and death. What features of right-wing media messages account for these effects? In a preregistered cross-sectional study (N = 554) we test a model that differentiates perceived consequences of two CDC recommendations—washing hands and staying home—for basic human values. People who consumed more right-wing media perceived these behaviors as less beneficial for their personal security, for the well-being of close ones, and the well-being of society at large. Perceived consequences of following the CDC recommendations mediated the relationship between media consumption and compliance with recommendations. Implications for public health messaging are discussed.

Why is right-wing media consumption associated with lower compliance with COVID-19 measures?

10.31234/osf.io/z2avp

In response to the coronavirus disease 2019 (COVID-19) schools around the world have been closed to protect against the spread of coronavirus. In several countries, homeschooling has been introduced to replace classroom schooling. With a focus on individual differences, the present study examined 138 schoolers (age range = 6 to 21 years) regarding their self-control and boredom proneness. The results showed that both traits were important in predicting adherence to homeschooling. Schoolers with higher levels of self-control perceived homeschooling as less difficult, which in turn increased homeschooling adherence. In contrast, schoolers with higher levels of boredom proneness perceived homeschooling as more difficult, which in turn reduced homeschooling adherence. These results partially hold when it comes to studying in the classroom. However, boredom threatened adherence only in the homeschooling context. Our results indicate that boredom proneness is a critical construct to consider when educational systems switch to homeschooling during a pandemic.

High trait self-control and low boredom proneness help COVID-19 homeschoolers

We're now more than seven months into the coronavirus pandemic that has upended the lives of most of Earth's inhabitants. And while it is true that the scientific community has learned many things about the SARS-CoV-2 virus and the disease it causes, Covid-19, there are also many gaps in our understanding.

Do some people have protection against the coronavirus?

10.1001/jamanetworkopen.2020.12469

Importance  Interpreting randomized clinical trials (RCTs) and their clinical relevance is challenging when P values are either marginally above or below the P = .05 threshold.Objective  To use the concept of reverse fragility index (RFI) to provide a measure of confidence in the neutrality of RCT results when assessed from the clinical perspective.Design, Setting, and Participants  In this cross-sectional study, a MEDLINE search was conducted for RCTs published from January 1, 2013, to December 31, 2018, in JAMA, the New England Journal of Medicine (NEJM), and The Lancet. Eligible studies were phase 3 and 4 trials with 1:1 randomization and statistically nonsignificant binary primary end points. Data analysis was performed from August 1, 2019, to August 31, 2019.Exposures  Single vs multicenter enrollment, total number of events, private vs government funding, placebo vs active control, and time to event vs frequency data.Main Outcomes and Measures  The primary outcome was the median RFI with interquartile range (IQR) at the P = .05 threshold. Secondary outcomes were the number of RCTs in which the number of participants lost to follow-up was greater than the RFI; the median RFI with IQR at different P value thresholds; the median reverse fragility quotient with IQR; and the correlation between sample sizes, number of events, and P values of the RCT and RFI.Results  Of the 167 RCTs included, 76 (46%) were published in the NEJM, 50 (30%) in JAMA, and 41 (24%) in The Lancet. The median (IQR) sample size was 970 (470-3427) participants, and the median (IQR) number of events was 251 (105-570). The median (IQR) RFI at the P = .05 threshold was 8 (5-13). Fifty-seven RCTs (34%) had an RFI of 5 or lower, and in 68 RCTs (41%) the number of participants lost to follow-up was greater than the RFI. Trials with P values ranging from P = .06 to P = .10 had a median (IQR) RFI of 3 (2-4). When compared, median (IQR) RFIs were not statistically significant for single-center vs multicenter enrollment (5 [4-13] vs 8 [5-13]; P = .41), private vs government-funded studies (9 [5-13] vs 8 [5-13]; P = .34), and time-to-event primary end points vs frequency data (9 [5-14] vs 7 [4-13]; P = .43). The median (IQR) RFI at the P = .01 threshold was 12 (7-19) and at the P = .005 threshold was 14 (9-21).Conclusions and Relevance  This cross-sectional study found that a relatively small number of events (median of 8) had to change to move the primary end point of an RCT from nonsignificant to statistically significant. These findings emphasize the nuance required when interpreting trial results that did not meet prespecified significance thresholds.

Application of the Reverse Fragility Index to Statistically Nonsignificant Randomized Clinical Trial Results

10.31234/osf.io/g93zw

Preventing the spread of COVID-19 requires persuading the vast majority of the public to significantly change their behavior in numerous, costly ways. However, it is unclear which persuasive strategies are most effective at convincing people who are not fully compliant to take recommended actions, such as wearing a mask and staying home more often. In five studies (N = 5,351) conducted from March - July 2020, we evaluated 56 short messages aimed at convincing people to comply with public health guidelines. In two within-subjects studies, participants rated the persuasiveness of many short messages drawn from both past research on persuasion and original crowdsourcing. In three pre-registered, between-subjects experiments, we tested whether the four top-rated messages from the previous studies led people who were not fully compliant to increase their intentions to comply. We do not find consistent effects of any message, though a message emphasizing civic responsibility to reciprocate healthcare workers’ sacrifices performed best in three of five studies. Overall, these findings suggest that short messages are largely ineffective in increasing compliance with public health guidelines during advanced stages of the pandemic.

Five Experimental Tests of the Effects of Short Messages on Compliance with COVID-19 Public Health Guidelines

10.31234/osf.io/2gkht

The home advantage (HA) is a robust phenomenon in soccer whereby the home team wins more games and scores more goals than the away team. One explanation is that the home crowd spurs on home team performance and causes the referee to unconsciously favour the home team. The Covid-19 pandemic provided a unique opportunity to assess this explanation for HA, as European soccer leagues played part of the 2019/2020 season with crowds present and concluded with crowds absent. Using multi-level modelling we compared team performance and referee decisions pre-Covid (crowd present) and post-Covid (crowd absent) across 9,528 games from 15 leagues in 11 countries. HA (goals scored and points gained) was significantly reduced post pandemic, which reflected the inferior performance of the home team. In addition, referees awarded significantly fewer sanctions against the away teams, and home teams created significantly fewer attacking opportunities when they played without fans.

Home Advantage during the COVID-19 Pandemic in European football

Despite a tentative easing of the national lockdown, the lingering economic and social consequences of this policy are frighteningly obvious to anyone venturing into central London.

London calling: Why it's time to bring life back to our city's streets

Weirdly, some of the "it's just the flu" folks moved to the opposite end of the spectrum, and are amplifying an exaggerated sense of doom and helplessness. Not saying things are great; just saying groupthink will groupthink. It really is hard to fight.

Anyway, I do wish them luck, honestly, because we need them to do the job right. So much depends on it. At a minimum, I hope there is some reflection on why some people were so wrong. Being wrong is normal and reasonable and even helpful as long as one learns from it, and openly.

I'm NOT saying appoint me. I'm doing what I can already. But just noting that if you're a famous Harvard professor, track record is no barrier. I doubt a woman and/or a person of color would survive such a track record, even if they managed to get the Harvard brand behind them.

Anyway, if I were leading an already battered organization through perhaps the most important phase of its existence, I'd look for people who had a good track-record and were openly critical when necessary but aligned with the mission, to save lives. Then again, famous professor!

At every step of the way, WHO has gotten major chunks of social science of pandemics wrong. Not everything for sure, but some of the most important things, like clinging to baseless claims of false sense of security to block progress on masks and maybe even aerosols.

What the person just chosen to lead the "Technical Advisory Group on Behavioural Insights and Sciences for Health" for WHO wrote on February 28 ("if you're worried about COVID, it's irrational panic") and what I wrote one day before ("We have to get ready so we can lessen risk").

BackgroundEradicating food insecurity is necessary for achieving global health goals. Liberal trade policies might increase food supplies but how these policies influence individual-level food insecurity remains uncertain. We aimed to assess the association between liberal trade policies and food insecurity at the individual level, and whether this association varies across country-income and household-income groups.MethodsFor this observational analysis, we combined individual-level data from the Food and Agricultural Organization of the UN with a country-level trade policy index from the Konjunkturforschungsstelle Swiss Economic Institute. We examined the association between a country's trade policy score and the probability of individuals reporting moderate-severe or severe food insecurity using regression models and algorithmic weighting procedures. We controlled for multiple covariates, including gross domestic product, democratisation level, and population size. Additionally, we examined heterogeneity by country and household income.ResultsOur sample comprised 460 102 individuals in 132 countries for the period of 2014–17. Liberal trade policy was not significantly associated with moderate-severe or severe food insecurity after covariate adjustment. However, among households in high-income countries with incomes higher than US$25 430 per person per year (adjusted for purchasing power parity), a unit increase in the trade policy index (more liberal) corresponded to a 0·07% (95% CI −0·10 to −0·04) reduction in the predicted probability of reporting moderate-severe food insecurity. Among households in the lowest income decile (<$450 per person per year) in low-income countries, a unit increase in the trade policy index was associated with a 0·35% (0·06 to 0·60) increase in the predicted probability of reporting moderate-severe food insecurity.InterpretationThe relationship between liberal trade policy and food insecurity varied across countries and households. Liberal trade policy was predominantly associated with lower food insecurity in high-income countries but corresponded to increased food insecurity among the world's poorest households in low-income countries.

10.1016/S2214-109X(20)30263-1

Liberal trade policy and food insecurity across the income distribution: an observational analysis in 132 countries, 2014–17

Promising findings and very wide confidence intervals: the truth is usually not somewhere in the middle

Update: New Mexico crushed its resurgence. Closed indoor dining and enforced mask mandate about two weeks ago.

I wrote about COVID-19 & racial disparities in April with @rapclassroom https://discoversociety.org/2020/04/15/covid-19-racism-and-health-outcomes/… /17

Here’s Macpherson’s definition of institutional racism in the Stephen Lawrence Inquiry, 1999 (first described by Carmichael & Hamilton in 1967) ./16

Finally I think it’s vital that we empower the affected communities to lead on research & interventions for their own communities. They are best placed to know what may work. Funding should reflect this. /15

Reasons for racial disparities health are complex. Here is Angela Saini on the topic in @theLancet https://tinyurl.com/y8bn5we3. I hope COVID-19 has highlighted the need to address racial inequalities in health, & will lead to long lasting changes in many areas. /14

UK born people from Black and Asian communities are more likely to be diagnosed with asthma. /13

The highest rates of hypertension (high blood pressure) are in Black groups. This is a risk factor for many health issues including stroke, chronic kidney disease, cardiovascular disease, retinopathy. /12

Rates of Type 2 diabetes are approximately three to five times higher than in BAME groups than the white British population. Diagnosis is more likely to occur at a younger age. /11

People from South Asian and Black backgrounds are three to five times more likely to start kidney dialysis than people from white backgrounds. /10 https://tinyurl.com/y6u8ec8z

Black people were more likely to have severe mental health symptoms, but were the least likely to receive treatment for mental illness https://tinyurl.com/ybxxxd9v. They are more likely to be detained in hospital https://tinyurl.com/y9dx2w8x /9

Black men are 2x as likely to be diagnosed with prostate cancer in the UK than white men and proportionately more Black men die of prostate cancer than other groups. /8

Jo’s Trust @JoTrust conducted a survey showing that women from BAME backgrounds are more likely to have never attended cervical screening. /7

The British National Survey of Sexual Attitudes and Lifestyles showed emergency contraception use was most commonly reported by Black Caribbean (30%) and mixed ethnicity women (28%) than White British women (23%) /6

74% heterosexual people receiving HIV care in the UK in 2018 were BAME of which 57% were of Black African ethnicity. The highest rate of late diagnosis (the most important predictor of HIV-related illness and death) was in heterosexual Black men (65%) /5 https://tinyurl.com/ybqmffsb

Rates of sexually transmitted infections are highest in Black communities especially Black Caribbean /4 https://tinyurl.com/y25z4v9w

Here we go. It's not a comfortable read (and neither should it be). Black women are 5x more likely to die in pregnancy than white women. /3 https://npeu.ox.ac.uk/downloads/file

This thread attempts to show racial health disparities in the UK are common & existed long before COVID-19. I have compiled a list of racial disparities in different areas of health in the UK, but this is not exhaustive and there are many that I haven't been able to include /2

I have been wondering about this tool (that seems to be targeted at biological sciences): https://twitter.com/SciscoreReportsIt makes me wonder, what would be an ideal 'reproducibility score' for work in the behavioural science?Certainly there are now badges for reproducibility (e.g., preregistration, open materials etc.)—a step in the right direction, but we should always be trying to improve.So what elements best define scientific quality in our research, and what is the best way to put this into practice?And maybe a controversial question: should it be up to the journals to mete it out?

Reproducibility scores for behavioural science: what are the merits and drawbacks?

Flute Theatre’s game-based production of Pericles, performed for one family at a time, is designed to create a sense of calm

'We use Shakespeare's heartbeat': lockdown theatre for autistic audiences

behaves as one might expect, it is better to identify these glitches before a big winter wave hits. Here endeth ye tweetorial.

It's why preparation now is of the essence. In Ontario we have identified a lot of bugs in our public health and healthcare systems, particularly related to lab capacity, information systems, and communication. In a sense that's great, because if this disease...

which jibes with that idea. If that's true, that means we are likely to have a very challenging winter ahead of us: lots of susceptibility, weariness of distancing, and a seasonally juiced virus with lots of susceptible folks to infect.

Why is this important? Because the very waning of COVID-19 in the northern hemisphere right now, despite pretty crappy disease control efforts in many places, suggests it is indeed a very seasonal pattern. I've also noted the concave up patterns in S America right now

As in this excellent figure showing us the very irregular patterns of seasonal waves in influenza pandemics:

And ultimately will turn into predictable seasonal disease. But that initial crazy trajectory of a pandemic depends in part on the random element of when the disease emerges. Early waves can die but then come back with a vengeance due to seasonal boosting.

Here are the average trajectories across 20 batched runs. Starting to look a bit regular.

We can batch a few runs and see very different patterns. These are 5 runs and each color represents a different run. The patterns look very different, but it's the same (exactly the same) disease.

They look pretty different. It's the same model, same parameters. Just seeding it at different times means that the epidemics get boosted or suppressed by seasonal changes in R.

Because the fraction susceptible is around 1, we can get out of season epidemics with pandemic pathogens. These are called "herald waves". Let's add a single stochastic element to our model...I'll seed the model with a single case at a random time of year.

Here's another.

Here's a run of the same model:

We don't know when a novel pathogen is going to emerge. Perhaps it'll be at "peak season" (with respect to its R0) when it emerges, perhaps it'll be "off season". E.g., summertime emergence for a flu virus, wintertime emergence for (summertime seasonal) cholera.

Now let's throw in some randomness ("stochasticity")...because again, although these waves look irregular, this model produces exactly the same outputs every time I run it.

Here we go...I messed about by shortening duration of immunity and we now have a disease that explodes onto the scene as a pandemic but then becomes "seasonal flu" once there's some immunity in the population.

See here for genius work on this by @jd_mathbio

This isn't annual periodicity. We could muck about with the numbers and get this to have an "intrinsic" oscillatory frequency that's the same as the oscillatory frequency of R0, and then we could have seasonal epidemics as with flu.

So we've got some cool waves. That looks like what happens with pandemics. If I run this out over a long time (200 years, here) u can see that the combination of replenishment of susceptibility (births, deaths, viral drift) with some seasonal forcing gives us periodic epidemics

Epidemic waves in my deterministic model look like this, and are a function of the interplay between seasonality and replenishment of susceptibles over time.

I'm going to make an SEIRS model (susceptible-latent-infectious-removed-susceptible) model such as we might use for flu. People lose immunity over time...perhaps as a result of viral drift. This model is initially deterministic (get the same result every time).

Let's make a simple SIR model with a seasonally oscillatory R. My R looks, arbitrarily, like this... In winter it's COVID-y...up in the low 2's. In summer it drops to 1-point-something.

One reason may be a seasonally oscillatory R0, which we might expect to see with a coronavirus and which has been anticipated by investigators like @mlipsitch

That both reduced the R of H1N1, and also attenuated mortality, because those at greatest risk of death, conditional on infection, didn't get infected.

SARS-CoV-2 is different, because nobody, in any age group, has pre-existing immunity. Those who are predisposed to death, conditional on infection, are not protected against infection, as they were in 2009. Hence mortality patterns that look like this in Ontario (X-axis = age)

Also, because nobody has baseline immunity R ~ R0 so attack rates are predictably high. But wait: why doesn't this just rip through this susceptible population in a single wave? Why did we have an R ~ 3 in Ontario in March and now (despite weak distancing) do we have an R ~ 1?

Pandemics have initial R ~ R0. That's why the epidemics are so large. In the 2009 influenza pandemic, this wasn't true. Those born prior to 1957 had early life experience with a related H1N1 influenza A virus, and were protected against infection.

I think I've used this analogy before, but epidemics are like gardens: you need the seed (pathogen) and the soil (susceptible population and conditions that permit R0 > 1). As R ~ R0 x S (proportion of the population that's susceptible), and S ~ 1 at the beginning of a pandemic

I'm not sure if anyone's in the mood for this, given the state of N America right now, but I keep getting asked whether another wave of COVID-19 is "possible". I think that it is all but a certainty. Why are multiple waves a signature feature of pandemics?

I cannot stop smiling! Here is a first peek at the data from our online browser-based intermodal preferential looking set-up! We replicate the prediction effect (boy eats big cake, Mani & Huettig, 2012) using our online webcam testing software @julien__mayor

I cannot stop smiling! Here is a first peek at the data from our online browser-based intermodal preferential looking set-up! We replicate the prediction effect (boy eats big cake, Mani & Huettig, 2012) using our online webcam testing software @julien__mayor @Kindskoepfe_Lab

Countries where citizens report higher openness to diversity are more likely to become democratic. The authors say that this trait is important because 'it predicts peaceful coexistence of competing viewpoints'.

In the wake of recent events, I keep thinking about this paper, published by @damianjruck et al. earlier this year. https://nature.com/articles/s41562-019-0769-1… short thread:

This paper challenges the widespread assumption that we can create democracies by introducing democratic institutions, and that we can inculcate support for democracy with the right set of societal rules. Spoiler: we can't.

There is no enshrined rule or law that democratic nations must remain democratic. Democracy is a political choice - it can be swept away with the tide of public opinion. These findings make me worry about the fate of some countries - including my own - over the coming years.

Ends.

Rather, the strongest predictor of whether a nation becomes democratic or not hinges on the values of its citizens in the preceding years. One cultural value is especially important in this transition: openness to diversity.

The paper explores how nations become democratic as opposed to, say, autocratic. Democracy is not an inevitable or per-ordained state of affairs. As recently as the 50s, just 20 countries were considered democratic.

In the wake of recent events, I keep thinking about this paper, published by @damianjruck et al. earlier this year. https://nature.com/articles/s41562-019-0769-1… short thread:

India’s Dharavi, the continent’s most crowded slum, has gone from coronavirus hotspot to potential success story, offering a model for developing nations struggling to contain the pandemic.

How Asia’s Densest Slum Chased the Virus Has Lessons for Others

Thousands flock to shops in England open for first time in almost three months

Crowds on London's Oxford Street as Nike Town reopens

10.1080/09581596.2020.1769406

In the space of two short months, the coronavirus pandemic has transformed the social, economic, and political landscape across the globe. For many, our research plans and projects have been one of the casualties of the virus, but we are also increasingly being assured that the virus is not just an impediment but an opportunity. Inboxes are daily flooded with requests to contribute to special issues or blogs on the coronavirus, and research funders have been fast to develop funding calls for research on the pandemic. Thus, among the many uncertainties of the COVID-19 pandemic, one clear outcome has been an incitement to publish.

Premature evaluation? Some cautionary thoughts on global pandemics and scholarly publishing

Official case counts often substantially underestimate the number of coronavirus infections. But in new studies that test the population more broadly, the percentage of people who have been infected so far is still in the single digits. The numbers are a fraction of the threshold known as herd immunity, at which the virus can no longer spread widely. The precise herd immunity threshold for the novel coronavirus is not yet clear; but several experts said they believed it would be higher than 60 percent.

The World Is Still Far From Herd Immunity for Coronavirus

Inclusion of multiple viewpoints increases when teams are diverse and provides value in scientific communication and discovery. To promote retention and raise the critical mass of underrepresented persons in science, all voices must be heard “at the table” to include “ways of knowing” outside the dominant institutional culture. These community-based inclusive concepts promote hearing all diverse perspectives for inclusive recognition of deeper socio-historical cultural wealth—collectively termed cultural wellness. When undergraduates and graduates in active-learning groups in class, or faculty collaborative teams on campus, start a project too quickly on task, opportunities are missed to be inclusive. While beginning a larger science project, we, student and faculty co-authors, first addressed this challenge —the need for greater inclusion of diverse perspectives—by starting a conversation. Here, we share ideas from our inclusive process. Based on social constructivist theories of co-constructing learning interpersonally, we co-mentored each other, learning from one another in community. We experientially considered how to inclusively collaborate across a demographically, geographically, and structurally heterogeneous group including multiple academic tiers from multiple ethnic backgrounds, cultural experiences, and institutions. Through an asset-based process grounded in several frameworks, we documented our introduction process of listening deeply, being mindful of identities including invisible cultural identities, recognizing each other with mutual respect, applying inclusive practices, and developing mutual trust and understanding. Building community takes time. Initial conversations can, and should, go deeper than mere introductions to build trust beyond social norms for relationships promoting cultural wellness.

10.1128/jmbe.v21i1.2073

Starting the Conversation: Initial Listening and Identity Approaches to Community Cultural Wellness

10.31234/osf.io/uzwgf

The COVID-19 pandemic requires rapid public compliance with advice from health authorities. Here, we ask who was most likely to do so during the first wave of the pandemic. We conducted surveys asking 26,508 citizens of eight Western democracies in the period between March 19 and April 3 about their protective behavior relating to COVID-19. Consistent with prior research on epidemics, we find that perceptions of threat and risk factors are crucial and culturally uniform determinants of protective behavior. On this basis, authorities could potentially foster further compliance by appealing to fear of COVID-19, but there may be normative and practical limits to such a strategy. Instead, we find that another major source of compliance are feelings of efficacy. Importantly, the effects of such feelings are especially strong among those who do not feel threatened, creating a path to compliance without fear. In contrast, two other major candidates for facilitating compliance from the social sciences, interpersonal trust and institutional evaluations, have surprisingly little motivational power. To combat future waves of the pandemic, health authorities should thus focus on facilitating efficacy in the public.

Behavior During the First Wave of the COVID-19 Pandemic

Socially responsible behavior is crucial for slowing the spread of infectious diseases. However, economic and epidemiological models of disease transmission abstract from prosocial motivations as a driver of behaviors that impact the health of others. In an incentivized study, we show that a large majority of people are very reluctant to put others at risk for their personal benefit. Moreover, this experimental measure of prosociality predicts health behaviors during the COVID-19 pandemic, measured in a separate and ostensibly unrelated study with the same people. Prosocial individuals are more likely to follow physical distancing guidelines, stay home when sick, and buy face masks. We also find that prosociality measured two years before the pandemic predicts health behaviors during the pandemic. Our findings indicate that prosociality is a stable, long-term predictor of policy-relevant behaviors, suggesting that the impact of policies on a population may depend on the degree of prosociality.