Evidence map›Paper›PMID 35068589›Full record

ArticleMinerva2022

Reimagining Health as a 'Flow on Effect' of Biomedical Innovation: Research Policy as a Site of State Activism.

Georgia Miller, Declan Kuch, Matthew Kearnes

Abstract read
In one paragraph

Article in Minerva, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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

3 authors.

Georgia MillerARC Centre of Excellence in Convergent Bio-Nano Science and Technology, School of Humanities and Languages, University of New South Wales, UNSW, NSW 2052 Australia.ORCID 0000-0002-9813-2997
Declan KuchInstitute for Culture and Society, Western Sydney University, Penrith, NSW 2751 Australia.
Matthew KearnesARC Centre of Excellence in Convergent Bio-Nano Science and Technology, School of Humanities and Languages, University of New South Wales, UNSW, NSW 2052 Australia.ORCID 0000-0001-6267-9571

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

As health care systems have been recast as innovation assets, commercial aims are increasingly prominent within states' health and medical research policies. Despite this, the reformulation of notions of social and of scientific value and of long-standing relations between science and the state that is occurring in research policies remains comparatively unexamined. Addressing this lacuna, this article investigates the articulation of 'actually existing neoliberalism' in research policy by examining a major Australian research policy and funding instrument, the Medical Research Future Fund (MRFF). We identify the MRFF and allied initiatives as a site of state activism: reallocating resources from primary and preventive health care to commercially-oriented biomedical research; privileging commercial objectives in research and casting health as a "flow on effect"; reorganising the publicly funded production of health and medical knowledge; and arrogating for political actors a newly prominent role in research grant assessment and funding allocation. We conclude that rather than the state's assumption of a more activist role in medical research and innovation straightforwardly serving a 'public good', it is a driver of neoliberalisation that erodes commitments to redistributive justice in health care and significantly reconfigures science-state relations in research policy.

Indexed as

AustraliaBiomedical innovationHealth bioeconomyHealth policyNeoliberalismPoliticsResearch policy

Identifiers

PMID35068589
PMCPMC8765493

What OpenQuestion holds

Textmetadata
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.