How forced dichotomization hijacked meaningful debate during the COVID‐19 pandemic
Notice bibliographique
Résumé
Public health emergencies generate fight or flight reactions leading to widespread calls for government measures to protect the populations at risk. The people who are responsible for providing those protections—government officials and public health authorities—have to make decisions on the fly without full information. They have to consider risks, benefits, uncertainty, evolving evidence, and public acceptance in deriving plans. But at the end of the day, they have to make decisions; and most often, those decisions are dichotomous. Close the border or do not close the border. Recommend face masks or do not recommend face masks. Those in charge need to make decisions and those who are not are left to debate and criticize them. And the public is caught in between. In this perspective, we discuss how forced dichotomization of scientific debate has hampered our collective response to the COVID-19 pandemic.1 People absorbing public health and government decisions have varying degrees of understanding and acceptance. But at the end of the day, they too make dichotomous decisions; they either follow the advice/mandate or refuse. The key variable that determines their choice is trust. Building confidence to establish trust by expressing absolute certainty about recommendations at a specific point in time (e.g., at the beginning of the pandemic when experts told the public masks were unnecessary) also undermines trust when decision makers get new evidence and change their minds. Communicating uncertainty and the possibility of a future reversal in the face of new information is an important skill for public health and government officials. Other variables that affect trust are the perception of conflicts of interest (are they not recommending masks because I do not need one, or because they think health care workers will run out?), prior beliefs and experiences (my mother got Guillain–Barre syndrome from influenza vaccine so I will never be vaccinated), and sources of information (personal physician or the Internet). While the response of people with expertise in health and health care during the COVID-19 pandemic has by no means been without precedent—all threats to health and safety, such as wars, pandemics, and natural disasters create vigorous debate—this time, the fragmentation of information sources and attributes of social media have exacerbated polarization and loss of civil discourse. In particular, both social media (especially Twitter and Facebook) and niche mainstream media are subject to a feedback loop where the most provocative and snarky reporting is rewarded with more attention and audience, leading to even more outrageous claims. When the goal of obtaining a bigger audience supersedes the goal of improving our collective decisions and behavior, it is very difficult to carry on nuanced discussions to educate those audiences. This polarized environment has led to forced dichotomization not just about decisions that authorities make (where dichotomization is necessary), but also about opinions regarding the evidence that supports those decisions (where dichotomization is not necessary and may be harmful). For example, it is possible to believe that schools should be open but also believe more can be done to improve their safety. It is possible to recommend wearing masks but also ask for evidence about what kind of mask, when, for how long, and for what age groups. Extreme polarization of ideas and the unwillingness to tolerate, let alone listen to, alternative views has created an environment in which individuals who occupy the middle ground are simultaneously branded by both sides as being harmful because their positions might be taken out of context and provide ammunition to people who disseminate intentional misinformation. This process further stifles useful conversation, silencing valid concerns out of fear of mob retribution. The most striking example of forced dichotomization was the general lack of ability of many experts to acknowledge or engage in conversation about the numerous complex trade-offs that needed to be considered with every public health intervention used during the pandemic. During the pandemic, scientists have been shamed for getting things wrong, even though continuous learning is in the very nature of scientific discovery. They have also been severely criticized for taking nonextreme positions and asking honest questions about whether an established intervention is the best thing to do. One of the most notable examples of this phenomenon was the criticism directed toward Dr. Paul Offit, someone who has dedicated his life to developing vaccines for children.1 While being a strong proponent of the COVID-19 vaccines, in late 2021, he expressed reservations about the need for boosters in young men based on existing estimates of risks and benefits. Despite the fact that he was hardly “antivaccine”, he was widely criticized by other scientists for expressing this view. Fear of public shaming was abundantly apparent when scientists engaged (or chose not to engage) on contentious issues, such as the opening and closing of schools, utility of masks, and role of boosters. After seeing colleagues who expressed reasonable opinions be widely criticized and denigrated on Twitter, a common phrase in academic circles was “stay away from offering your opinion on COVID-19—is it not good for your personal sanity or your career.” This understandable reaction was unfortunate because what we needed most was thoughtful debate as we grappled with complex issues. At the core of this problem is an issue of scientific humility—no one could possibly have all of the answers. It was almost unheard of for any expert or public official during the pandemic to publicly state “I don't know.” Yet it is obvious that decision-making during the pandemic has been incredibly complex; there is no single person or discipline that can answer all of the important questions. When the pandemic began, countries made different choices. Taiwan, Australia, and New Zealand opted for an elimination strategy. Sweden tried achieving herd immunity by natural infection. Canada and the United States tried something in between and leaned heavily on vaccines. In January 2020, we had no idea which plan would work best, but now we know; death rates, hospitalization rates, economic effects, and impact on children differed across all health systems. There was no escaping some form of negative outcome with any of these strategies and oversimplified direct country-to-country comparisons are fraught with bias. Omicron has taught us that no single strategy will work. In January 2020, if we had known that in 1 year, we would have a vaccine that was demonstrated to be 95% effective against the symptomatic disease, and that 1 year later many countries would have fully vaccinated more than 70% of their population, we would have predicted achievement of herd immunity and that life would look pretty normal in those countries. Both of these outcomes came true, yet long after, the pandemic still wreaked havoc. This acknowledgment in no way implies that we should stop trying to vaccinate everyone as there are still large benefits against severe disease and vaccines have saved millions of lives. But failing to admit this reality does not help stifle misinformation. Acknowledging that we need nuance in our discussions of the pandemic will promote diversity of input from the broader scientific community as we collectively grapple with “what next?” We have previously abandoned strategies that were initially promoted to reduce spread, like telling people to stop touching their faces—futile advice because people cannot stop touching their faces. We no longer wash our bags when we return from the grocery store. When knowledge changes our ideas should also change. After 2 years, it is time for experts to reassess all positions, learn from our experiences, continue to gather evidence, and move forward with more nuance. To do this will require serious introspection on the part of the loudest voices on Twitter and an earnest effort by journalists to seek out scientists who are willing to acknowledge uncertainty. Academic institutions should return to their role of public service and hold debates among experts to openly and civilly discuss ways forward. Finally, all scientists who eventually find themselves on the “wrong” side of an issue should be willing to admit when they were wrong and be empowered to move forward; this is the very nature of the scientific process. The public may finally be ready to accept that things change and so should our plans. It is time to acknowledge the harm from stifling honest debate—even if it is sometimes hard to distinguish it from deliberate misinformation. We thank Jane Philpott (Queen's University, Kingston, ON) and Isaac Bogoch (University of Toronto, Toronto, ON) for comments on an earlier draft. Neither was compensated. The authors declare no conflicts of interest.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,002 | 0,003 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,000 | 0,001 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».