Evidence map›Paper›PMID 36618759›Full record

ArticleCritical public health2022

Rethinking Disease Preparedness: Incertitude and the Politics of Knowledge.

Melissa Leach, Hayley MacGregor, Santiago Ripoll, Ian Scoones, Annie Wilkinson

Abstract read
In one paragraph

Article in Critical public health, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.

0numbers the graph read from it
0cells of the map it votes in
15citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from 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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

15 citing papers in PubMed.

  1. Article
  2. Article
  3. Review
  4. Review
  5. Article
  6. Article
  7. Article
  8. Article
  9. Article
  10. Article
  11. Article
  12. Article
  13. Humanitarian inversions: COVID-19 as crisis.Transactions (Institute of British Geographers : 1965) · 2022
    Article
  14. How Personality and Communication Patterns Affect OnlineFrontiers in artificial intelligence · 2022
    Article
  15. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Melissa LeachInstitute of Development Studies, University of Sussex, Brighton, UK.
Hayley MacGregorInstitute of Development Studies, University of Sussex, Brighton, UK.
Santiago RipollInstitute of Development Studies, University of Sussex, Brighton, UK.
Ian ScoonesInstitute of Development Studies, University of Sussex, Brighton, UK.
Annie WilkinsonInstitute of Development Studies, University of Sussex, Brighton, UK.

Funding

Medical Research Council MR/M002675/1Wellcome Trust 212536Wellcome Trust 212536/Z/18/Z
6 · The paper itself

Abstract

This paper argues for a rethinking of disease preparedness that puts incertitude and the politics of knowledge at the centre. Through examining the experiences of Ebola, Nipah, cholera and COVID-19 across multiple settings, the limitations of current approaches are highlighted. Conventional approaches assume a controllable, predictable future, which is responded to by a range of standard interventions. Such emergency preparedness planning approaches assume risk - where future outcomes can be predicted - and fail to address uncertainty, ambiguity and ignorance - where outcomes or their probabilities are unknown. Through examining the experiences of outbreak planning and response across the four cases, the paper argues for an approach that highlights the politics of knowledge, the constructions of time and space, the requirements for institutions and administrations and the challenges of ethics and justice. Embracing incertitude in disease preparedness responses therefore means making contextual social, political and cultural dimensions central.

Indexed as

incertitudeknowledgePreparednessrisk

Identifiers

PMID36618759
PMCPMC7614024

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.