ArticleSociology of health & illness2022
Uncomfortable science: How mathematical models, and consensus, come to be in public policy.
Article in Sociology of health & illness, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
6 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Barriers and facilitators to knowledge translation at the science-policy interface during the COVID-19 pandemic public health emergency: a rapid review and theoretical analysis to inform development of a logic model.BMC public health · 2026Pooled it
- Confronting the known unknown: historical lessons and future strategies for Disease X.Frontiers in public health · 2026Review
- Epistemic preparedness.BMJ global health · 2025Review
- Evaluation of the use of modelling in resource allocation decisions for HIV and TB.BMJ global health · 2024Article
- Stakeholder Interviews to Inform Best Practice for Public Facing COVID-19 Wastewater Dashboards.Gates open research · 2024Article
- Uncomfortable science: How mathematical models, and consensus, come to be in public policy.Sociology of health & illness · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
We explore messy translations of evidence in policy as a site of 'uncomfortable science'. Drawing on the work of John Law, we follow evidence as a 'fluid object' of its situation, also enacted in relation to a hinterland of practices. Working with the qualitative interview accounts of mathematical modellers and other scientists engaged in the UK COVID-19 response, we trace how models perform as evidence. Our point of departure is a moment of controversy in the public announcement of second national lockdown in the UK, and specifically, the projected daily deaths from COVID-19 presented in support of this policy decision. We reflect on this event to trace the messy translations of "scientific consensus" in the face of uncertainty. Efforts among scientists to realise evidence-based expectation and to manage the troubled translations of models in policy, including via "scientific consensus", can extend the dis-ease of uncomfortable science rather than clean it up or close it down. We argue that the project of evidence-based policy is not so much in need of technical management or repair, but that we need to be thinking altogether differently.
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What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.