Evidence map›Paper›PMID 41725771›Full record

ArticleFrontiers in public health2026

How do policy tool combinations drive the construction of public health technology R&D alliances?

Yangchun Cao, Jing Zhang, Ling Ning

Abstract read
In one paragraph

Article in Frontiers in public health, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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.

Yangchun CaoSchool of Management, Guangdong Ocean University, Zhanjiang, China.
Jing ZhangSchool of Management, Guangdong Ocean University, Zhanjiang, China.
Ling NingSchool of Management, Guangdong Ocean University, Zhanjiang, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

To effectively respond to public health emergencies, establishing an efficient technology R&D alliance is critically important. This study develops a tripartite evolutionary game model involving the government, pharmaceutical enterprises, and academic and research institutions to examine how a combination of supply-side, demand-side, and environmental-side policy tools drives the formation of such alliances. The findings reveal that demand-side government procurement exerts the strongest incentive effect on enterprise and institutional participation, outperforming supply-side and environmental-side measures. Furthermore, policy intensity exhibits a scientifically discernible threshold: excessive intervention may not only increase fiscal pressure on the government but also paradoxically reduce willingness to participate due to diminishing marginal returns. Consequently, optimizing the mix of policy tools and implementing differentiated, targeted incentives are essential for fostering high-efficiency public health technology R&D alliances. This study offers a dynamic analytical framework and evidence-based guidance for policymakers in designing effective collaborative innovation strategies.

Indexed as

Cooperative BehaviorHealth PolicyPublic HealthHumansevolutionary gameevolutionary pathpolicy toolspublic healthtechnology R&D alliance

Identifiers

PMID41725771
PMCPMC12920495

What OpenQuestion holds

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LicenceCC BY
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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.