How to Handle Reasonable Scientific Disagreement: The Case of COVID-19

Antiochou, Konstantina; Psillos, Stathis

doi:10.1007/978-3-030-91017-4_4

Cited by 7 publications

(2 citation statements)

References 20 publications

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“…On the one hand, this was useful in some settings, resulting in greater democratization of surveillance (done not only through governmental agencies), supporting diverse analytic approaches, and external testing. On the other hand, much noise-if not misinformationemerged from this exposure, notably because researchers are not trained for public health counseling and communication activities; they often lack a public health surveillance culture, and they tend to be overconfident about how they understand others (citizen, decision-makers) and how others understand them [12]. Concurrently, many decisionmakers, while struggling with their weak health and data literacy, had to deal for the first time with researchers and the convoluted scientific processes of knowledge production.…”

Section: Confusion Between Surveillance and Researchmentioning

confidence: 99%

Slow data public health

Chiolero,

Tancredi,

Ioannidis

2023

Eur J Epidemiol

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Surveillance and research data, despite their massive production, often fail to inform evidence-based and rigorous data-driven health decision-making. In the age of infodemic, as revealed by the COVID-19 pandemic, providing useful information for decision-making requires more than getting more data. Data of dubious quality and reliability waste resources and create data-genic public health damages. We call therefore for a slow data public health, which means focusing, first, on the identification of specific information needs and, second, on the dissemination of information in a way that informs decision-making, rather than devoting massive resources to data collection and analysis. A slow data public health prioritizes better data, ideally population-based, over more data and aims to be timely rather than deceptively fast. Applied by independent institutions with expertise in epidemiology and surveillance methods, it allows a thoughtful and timely public health response, based on high-quality data fostering trustworthiness.

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Section: Confusion Between Surveillance and Researchmentioning

confidence: 99%

Slow data public health

Chiolero,

Tancredi,

Ioannidis

2023

Eur J Epidemiol

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“…Their definition of misinformation is distinguished, however, by its emphasis on the value of scientific rigour (rather than simple expertise or authority) for determining the reliability of information 31 . This is a potentially important evolution in the concept of scientific misinformation because it substantially resolves challenges around expert disagreement 32 and the potential of experts to themselves spread misinformation. Significantly, Southwell et al (2022) also specify that, in order to be classified as misinformation, unreliable content must be publicly accessible and accessed 31 .…”

Section: Introductionmentioning

confidence: 99%

Health misinformation: protocol for a hybrid concept analysis and development

Pope,

Byrne,

Devane

et al. 2024

HRB Open Res

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Background Misinformation represents a serious and growing concern for public health and healthcare health; and has attracted much interest from researchers, media, and the public over recent years. Despite increased concern about the impacts of misinformation on health and wellbeing, however, the concept of health misinformation remains underdeveloped. In particular, there is a need to clarify how certain types of health information come to be designated as “misinformation,” what characteristics are associated with this classification, and how the concept of misinformation is applied in health contexts. Aim Developing a shared understanding of what it means for health information to be “misinformation” is an important first step to accurately identifying at-risk groups, clarifying pathways of vulnerability, and agreeing goals for intervention. It will also help to ensure that misinformation interventions are accessible, acceptable, and of benefit to the populations to which they are directed. We will therefore examine the characteristics, measurement, and applications of misinformation in health contexts. Methods We will undertake a hybrid concept analysis, following a framework from Schwartz-Barcott & Kim (2000). This framework comprises three phases: a theoretical phase, fieldwork phase, and final analysis phase. In the theoretical phase, a search of seven electronic citation databases (PsycInfo, socINDEX, JSTOR, CINAHL, Scopus, MEDLINE and PubMed Central via PubMed, and ScienceDirect) will be conducted in order to identify original research, review, and theoretical papers, published in English between 2016 and 2022, which examine “health misinformation.” Data from the literature will be synthesised using evolutionary concept analysis methods from Rodgers (2000). In the fieldwork phase, a purposive sampling strategy will be employed to recruit stakeholders for participation in semi-structured interviews. Interviews will be analysed using thematic analysis. The final phase will integrate findings from the theoretical and fieldwork analyses.

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The UK Government’s “Balancing Act” in the Pandemic: Rational Decision-Making from an Argumentative Perspective

Fairclough

2022

The Pandemic of Argumentation

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This chapter looks at how the “balance” between lives, livelihoods and other concerns was talked about in four main newspapers in the UK, between March 2020 and March 2021, in assessing the UK government’s performance. Different arguments were made for opposite conclusions, favouring either strict and prolonged lockdowns or, on the contrary, a speedy exit from lockdown and a resumption of normal life. From the point of view of argumentation theory, the empirical data suggests that what is being balanced or weighed together in pro/con argumentation by two opposite parties are not as much the costs and benefits of one’s own proposal, but the costs of one proposal against the costs of its alternative (a “cost-cost” analysis). Rather than defending their own proposal by arguing that the benefits outweigh the costs, each side is criticizing the opponent’s proposal by claiming that the costs of their proposal are more unacceptable than the costs of their own. An implicit minimax strategy (minimize costs in a worst-case scenario) was applied in different ways, depending on how the consequences were assessed, and how this assessment changed over time. The debate over lockdown illustrated an interesting type of pro/con argument, typical to crisis situations, in which all the intended “benefits” were in fact avoided “costs”, and contrasted a medical/epidemiological perspective with a political perspective on the best course of action.

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How to Handle Reasonable Scientific Disagreement: The Case of COVID-19

Cited by 7 publications

References 20 publications

Slow data public health

Slow data public health

Health misinformation: protocol for a hybrid concept analysis and development

The UK Government’s “Balancing Act” in the Pandemic: Rational Decision-Making from an Argumentative Perspective

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