I’ve been thinking a lot about the role of public sociology because of the Coronavirus (COVID-19) pandemic. What follows has been in the works for a couple of months. As previously promised, I’m now coming back to this because of the ongoing need to increase public awareness about the science of the pandemic.
Earlier in the year, I worked with some colleagues on an early literature review scoping policy responses to the pandemic, and I’ve provided feedback on evolving policy research. As an applied sociologist, my focus has been on how race, culture, disability, gender, and other socioeconomics impact how people understand and act on public health initiatives, as well as ethical considerations of COVID research “on the run.”
Since then, I’ve been keeping up with both the research and media coverage of public health responses. I’ve been providing summaries of unfolding information on my social media (primarily Facebook and Instagram stories, as well as Twitter). This started partly to address some of the misconceptions I was seeing amongst my friends and family and I’ve kept this up as it’s been the most efficient way to help people in my life better understand what the restrictions mean for them, or to correct confusing reports.
Unfortunately, there is a lot of misinformation. People are hungry for practical advice, but don’t know who to trust (they don’t know where to look for credible resources), or they feel overwhelmed with too many conflicting directions. This is known as information overload, and it leads to poor decision-making.
One of the patterns that has been especially concerning are people writing social media posts, op eds and even setting up consultancies to profiteer from COVID-19 without any health training or policy experience. This contributes to public distrust, conspiracy theories or poor discussion that is not based on evidence. People are choosing to confirm their pre-existing beliefs, rather than engaging critically with scientific information that challenges their perspective. This is known as confirmation bias. It stops people from considering new information and different points of view that might be helpful to their wellbeing.
Reading original scientific journal articles is not always possible as there is often a paywall. Plus, science papers are, by definition, published for the academic community. The language is technical, and the principles can be hard to follow for people who are not subject matter experts. This makes it more important for scientists who have access to write about science research in an accessible manner and to share findings through different communities.
While data on COVID-19 are evolving, and no one can claim to be a definitive COVID-19 expert, the best sources to trust are official sources, such as state Health Departments, epidemiologists, virologists, health practitioners who are providing front-line services (such as Aboriginal-controlled health organisations), and policy analysts who work on COVID-19 responses. Additionally, reliable news sites include the ABC News Australia live blog, Croakey and individual health researchers, such as epidemiologist Dr Zoe Hyde (University of Western Australia) on Twitter.
If you read about a study, how do you know if you can trust the conclusions? What’s the best approach if you wanted to write about a study’s findings for a broader audience, whether it’s your friends and family reading your Facebook feed, or an article in a major news site? Today’s post gives tips for how to read a study using critical thinking principles from sociology, and things to consider if you want to write about, or share, studies that you read about.
Go beyond the abstract
Tip: if the abstract claims something that goes against the majority of scientists’ current thinking, take a step back and ask some critical thinking questions. For example: do the claims seem to reinforce negative stereotypes, or otherwise justify inequality, or serve a political goal for elites and people already in power? Remember that extraordinary claims require extraordinary evidence.
Abstracts summarise key findings for an academic audience. Readers need to weigh up the evidence to properly evaluate if the study has the data to back up its claims. For example, consider this break down of a COVID study. Dr Piero Poletti and colleagues (2020) have published a pre-print of their study. The lead author is an infectious disease modeller at the not-for-profit Bruno Kessler Institute, Italy. The abstract claims that younger people in Lombardy, Italy, have a lower probability of developing COVID-19 symptoms. Their sample includes almost 5,500 people who had some connection to other people infected with COVID-19 (SARS-CoV 2 positive people). The abstract reports that 74% of people in their sample aged under 60 years did not develop symptoms. People over 60 years were more likely to develop critical symptoms (6.6% had developed the infection), and thus, were at higher risk of requiring hospitalisation or dying.
This study was then picked up by the media, which were reporting that school-aged children are less likely to pass on infection. That’s not quite what the study aimed to show, nor what the data say.
Assistant Professor A. Marm Kilpatrick (2020) who is a disease ecologist from the University of California, Santa Cruz, USA, breaks down how the study did not have data to back up these claims. Instead, the data suggest people of all age groups are equally likely to become infected.
You can see why we need to go beyond the abstract to see whether researchers, or the media, may be overstating their claims.
Currently, there is a high volume of pre-print studies being published, as researchers rush to share their findings as the pandemic develops. On the one hand, this helps the scientific community respond and build on new work swiftly. Academic publishing is usually very slow. Papers can take at least one year to complete peer review and reach publication, sometimes longer. The pandemic is compelling a greater number of researchers to publish studies before they reach peer review. On the other hand, pre-prints do not go through rigorous and anonymous peer review, a minimum standard that academic research needs to meet before publication. This makes it tricky, because media picks up on sensationalist findings—such as people with Type A blood are more likely to become infected with COVID-19—and report on it and the public may not understand the caveats of pre-prints. The Type A pre-print was roundly critiqued by academics and it was republished, acknowledging its limitations (Kingsley, 2020).
Dr Maimuna Majumder and Dr Kenneth Mandl (2020) (both computational health researchers at Harvard Medical School, USA), analysed around 50 studies published about the pandemic in the early phase (from 23 Jan to 1 Feb 2020). They found that pre-prints were driving media reporting and they suspected this might be influencing policy decisions. They write:
“Our findings suggest that, because of the speed of their release, preprints—rather than peer-reviewed literature in the same topic area—might be driving discourse related to the ongoing COVID-19 outbreak… Nevertheless, despite the advantages of speedy information delivery, the lack of peer review can also translate into issues of credibility and misinformation, both intentional and unintentional.”
This might explain why a pre-print from Italy generated excitement in the media, when it was read as support for sending kids back to school.
Return to school under the pandemic has been contentious around the world. Governments are keen for their economies to recover from the economic strain of lockdown measures. This is difficult when parents need to work from home and look after their children at home. Early reports from China emphasised that youth were less at risk of contracting the disease. Patterns around the world have proven this belief false. In Shenzhen, China, there was a ‘sharp increase’ in infection among children after the initial cases were reported in Wuhan, where the virus first spread (Liu et al., 2020). This may have been because children may be less likely to be properly diagnosed in the first place, either because they exhibit milder symptoms, or these signs are misread, or because there were less resources to carry out rigours tests at the beginning of the pandemic.
In Australia, three months after the first cases of COVID-19, parents were still reticent to have their children tested. By the end of June 2020, there were infections in six schools and two childcare centres in Victoria, which contributed to resurgence of the pandemic in that state. This is alongside the main drivers of infection in the first wave of the pandemic—returned travellers who were in quarantine, and relaxed measures around family visits (Butt, 2020). At the end of June, a new saliva test was introduced in Victoria (coinciding with the beginning school holidays) to help children and other people who have avoided the nasal swab test (Baxendale, 2020).
Early beliefs about children’s misperceived immunity to the virus continue to circulate. Studies claiming that children are less likely to transmit the infection play into a politically motivated belief that supports the easing of restrictions.
Extraordinary claims require extraordinary evidence. Go beyond the headlines to see what’s really going on.
Put the research question into context
Tip: consider what assumptions the study is making about the problem and the possible solutions that the researchers have tested. The important thing is that researchers are clear about their assumptions and limitations.
A good write up of a study for a general audience should summarise the key findings in a few short sentences, clarifying the context of the research (more detail about the study can be included later). The reason for keeping the key points short is that public science needs to be easily understood by a non-specialist audience. The write-up should make clear what the study set out to do and what they found.
Here are some general questions to aid critical thinking about the research question and findings:
- What did the researchers seek to discover or disprove? To put it another way: what are the key research questions or hypotheses?
- What theories and concepts do they use to define their primary measures?
The theory being used in the study will influence how the study is carried out and how findings are interpreted. A theory is a statement of how and why specific facts are related. This does not mean the colloquial use of theory, which people usually mean, “I have an idea I want to air out.” Instead, an academic theory a broader, established framework for thinking about the world. Theories shape how researchers design their study. Theories help to narrow the focus of a study, which is fine—one study can’t look at everything! But we need to understand research questions in relation to pre-existing scientific theories.
For example, let’s say we wanted to measure the financial costs of COVID-19. Our research question might be:
What are the financial costs of the COVID-19 pandemic?
Our theory will help us to define what we mean by ‘financial costs.’ There are hundreds of theories within each academic discipline, and thousands of theories across all sciences. Each of these could define ‘financial costs’ differently, and so our study would look at different dynamics of this question, depending on our theory.
Using traditional economic theory, we would model rates of unemployment. Social distancing restrictions were introduced around Australia on 30 March 2020. These included businesses being forced to shut down and a work-from-home directive for other workers. By 4 April, just one week later, the Australian Bureau of Statistics reported that Australians lost 780,000 jobs, and, by mid-April, wages were down by 6.7 percent (Janda, 2020). At the end of June, other research by the Australian Bureau of Statistics shows women in low-skilled jobs were being hit hardest, through unemployment or under-employment (working fewer hours than needed to earn a living wage) (Wright & Duke, 2020). Economists, Professor Warwick McKibbin and Roshen Fernando, conducted an economic model that estimates a second wave of the virus could cost $450 billion over the next five years (Wright & Duke, 2020).
Using traditional Marxist theory, we would look beyond rates of employment, and instead measure the costs of exploitation of labour. In the current, second wave of the pandemic, 80% of infections in Victoria are linked to workplaces, especially aged care (Zevallos, 2020b). Front-line service workers (such as healthcare staff) are most at risk of contracting the disease because they come into more regular contact with the public (Sim, 2020) and they lack adequate personal protective equipment (Nguyen et. al. 2020 pre-print). By 1 April, healthcare professionals made up 10% of COVID-19 confirmed cases in Victoria; more broadly, 78% of healthcare professionals were afraid of being infected or passing on the infection to others (Lewin, 2020).
At the time of publication, 21 July, the Victorian Premier and Minister for Health acknowledge that aged care workers are a major source of community infection in the second wave of the pandemic due to the casualisation of the workforce, which sees workers moving from one facility to another to make up shifts (Zevallos, 2020c). This is both a class and race issue, as many aged care workers are migrants. In fact, the only option for temporary visa holders to extend their stay in Australia at the height of the first wave was to join the four “essential” industries, including healthcare and aged care (Zevallos, 2020a).
Other “essential workers” cannot work from home because their work may be manual and masks and other protections may not be practical or feasible. While businesses have received a relief payment to retain their staff (Job Keeper), some employees have not been receiving these payments. Other workers were not entitled to economic support (such as international students), leaving them in crisis.
Large, wealthy companies were provided massive Government payments, and yet they still let go of their staff. For example, in mid-April, Qantas and Virgin Australian airlines received $165 billion to keep them afloat during the pandemic, on top of a $5 billion bailout for the airline industry (Hurst & Butler, 2020). In mid-May, the Federal Court ruled that Qantas could stop paying sick leave to the 250,000 staff they had already stood down (David Chau and Jamelle Wells 2020). In late June, Qantas fired one-fifth of its workers and kept a further 15,000 stood down (Farrer, 2020).
Some sectors were not given any financial assistance. Universities are not entitled to Job Keeper payments, which will lead to a minimum of 21,000 jobs lost, according to peak body Universities Australia, or at worst, a loss of 30,000 jobs according to the National Tertiary Education Union (Ribeiro, 2020). Other sectors have received questionable support, such as the Catholic Church. It’s set up as a charity organisation despite having $30 billion assets in Australia, but it formally does not have any employees—legally established so that they would not have to pay victims of child sex abuse—and yet the Church is being given Job Keeper payments for priests (Bradley, 2020).
Large businesses are better positioned to bounce back. But front-line staff, who are already poorly paid, bear higher costs and longer-term insecurity. If we wanted to measure the costs of the pandemic, a Marxist framework would move beyond wages and seek to examine how class and power relations have impacted the economy. For example: who owns the means of production? Whose elite interests are being protected? How are workers being alienated from organising together to fight this exploitation?
Using Marxist feminist theory, we would look not just at lost jobs and wages, but also at the costs of emotional labour and other unpaid care work. For example, one group of people most at risk of dying from the pandemic also provide a high economic contribution via unpaid childcare: grandparents. In December 2019, the Australian Bureau of Statistics estimated this unpaid labour was equivalent to $4.4 billion (Fang & Brooks, 2020). Yet those at retirement age may not be eligible for relief payments under COVID-19. Restrictions increased economic, as well as emotional, strain on families, as grandparents navigated health risks and isolation (McCutcheon & Tugwell, 2020).
Using the theory of intersectionality, we would examine not just age, but also the interconnected impact of race and gender and other sources of inequality, such as disability. Aboriginal people are twice as likely to have a disability than other Australians. Aboriginal grandparents play an important role in transmitting culture to grandchildren; they are more likely to be caring for their grandkids who are in foster care; and they generally do a high amount of formal and informal childcare (Australian Human Rights Commission, 2014, pp. 7, 15–20). Aboriginal people in remote communities, and those aged over 50 years, also face the highest risk of dying from COVID-19 (Department of Health, 2020; Health Direct, 2020). And yet First Nations have been an exemplar in responding to the pandemic (Crooks et al., 2020 preprint; McQuire, 2020). The costs of the pandemic are therefore not evenly spread, even amongst age groups, or workers more broadly.
If our research question is to find out the cost of the pandemic, and we only use wages as our primary measure, we are missing a heap of invisible labour, and an important subset of unpaid workers (such as retired grandparents) who contribute to the economy.
If our question is framed around costs, we might also miss an opportunity to measure the gains, such as Aboriginal people’s contribution to the economy, healthcare, and society more broadly, through their proactive planning measures. We could even flip the research question around:
How much have Aboriginal people saved the national economy and contributed to community wellbeing through their swift action on social distancing?
You can see why we need to be clear about our research questions and how theory impacts our assumptions.
Assess the methods
Tip: research methods need to match the research question and sample group we’re interested in. Be wary of studies that make big leaps when interpreting their findings. For example, a quantitative study might determine the percentage of people who are not following social distancing rules, but this fact alone cannot tell us why this is happening.
If we need to know how many people in society have been affected by an issue, quantitative methods (measuring numerical data) might be useful. This might be a lab experiment or a closed-question survey.
If we need to understand why different groups of people are behaving in a particular way, qualitative methods (measuring non-numerical data) are necessary. For example, one-on-one interviews or observations of people’s behaviour at work.
When thinking about the methods and findings of a quantitative study, pay attention to the statistical significance, margin of error and sample size.
Tip for writing about a study: If you want to write up the findings of a scientific study for a wider audience, consider drawing a diagram or some other illustration to explain the methods, if you can. This can make the logic of complex studies easier for a general audience to understand. Make sure your diagram is accessible to disabled people!
Weigh up the evidence
Having read the study, now go back to read the abstract. Does it match your understanding of the results? Do the findings, research questions, theory, and methods warrant the conclusions?
The best way to know whether the findings are solid, is to read more widely: what do other researchers say about the study, and the topic at large?
Scientists have a responsibility in their communication of their conclusions. Professor Michael Eisen (2020), a molecular biologist, breaks down a study which the media reported as showing that vaccines won’t stop the COVID-19 virus. The study shows some potential gaps in antibody testing that require further investigation. The data suggest that neutralising antibodies decline after 21 days. The researchers conclude that more research is needed to determine the level of antibodies required for protection from infection. They recommend that A/B testing should be ongoing, to see if antibodies continue to drop or plateau over time. The media interpreted this to mean: vaccines won’t ever work against COVID-19. That is not what the researchers concluded.
Professor Eisen argues that the press release, which cautiously wrote up these findings, should have anticipated that the media might misinterpret the broader meaning of this research, especially given that vaccines are a contentious issue in the public’s mind. This feeds into existing fears and misconceptions about vaccines, which have led to an increase in preventable infections, such as measles, and stigmatisation of autistic people (Zevallos, 2016).
Taking conclusions about research studies from the media are not always a good idea. Go to the original source if you can or follow reputable experts on social media who are doing daily work to break down the science in plain language.
It can be hard to know who to believe, given the state of pre-prints, and constantly changing information. The video below explains this confusion eloquently: the public is seeing the reality of science, which is that knowledge is constantly evolving, and what we know (‘the facts’) is constantly being adjusted. When the pandemic is over, and we have a vaccine (in around 1.5 years), and once we have a better grip on the economic and social costs of the pandemic, theories and conclusions will be more firmly established. Until then, avoid spreading the infection as well as misinformation! Stay informed by following experts, and ask useful questions using the critical thinking tools I’ve outlined here.
Does the study go against existing scientific evidence? Do the conclusions reinforce inequality or a political agenda for dominant groups in power?
What is the study’s theory, assumptions, and limitations?
Do the conclusions match the research questions, methods and sample group? What do other experts say about this study?
Take care of yourself and your loved ones. Wash your hands. Wear a mask. Keep observing social distancing wherever possible.
Australian Human Rights Commission. 2014. Inquiry into Grandparents Who Take Primary Responsibility for Raising Their Grandchildren. Sydney: AHRC.
Baxendale, Rachel. 2020. “Saliva Tests Fast-Tracked, but Swabs Superior.” The Australian. Retrieved June 30, 2020 (https://www.theaustralian.com.au/nation/coronavirus-australia-live-news-stayathome-orders-may-be-imposed-in-melbourne/news-story/c524d9e1e01ab2725123562186f3dbb3?keyevent=3.05pm).
Bradley, Michael. 2020. “Why, in God’s Name, Is the Catholic Church Getting JobKeeper?” Crikey. Retrieved June 30, 2020 (https://www.crikey.com.au/2020/06/19/why-is-the-catholic-church-getting-jobkeeper/).
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Farrer, Martin. 2020. “‘Real Vulnerability’: Qantas Job Cuts Show Covid-19 Will Change the Future of Work.” The Guardian. Retrieved June 30, 2020 (https://www.theguardian.com/business/2020/jun/28/real-vulnerability-qantas-job-cuts-show-covid-19-will-change-the-future-of-work).
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McQuire, Amy. 2020. “Aboriginal Community Health’s Success with Covid-19.” The Saturday Paper. Retrieved June 30, 2020 (https://www.thesaturdaypaper.com.au/news/health/2020/04/25/aboriginal-community-healths-success-with-covid-19/15877368009740?cb=1593435746).
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Zevallos, Zuleyka. 2016. “Sociology of the Anti-Vaccination Movement.” The Other Sociologist. Retrieved July 21, 2020 (https://othersociologist.com/2016/02/28/sociology-of-anti-vaccination-movement/).
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