Evidence map›Paper›PMID 38726232›Full record

ArticleFrontiers in public health2024

The application of innovative ecosystems to build resilient communities in response to major public health events.

Juan Juan La, Man Li, Xiaolu Liu

Abstract read
In one paragraph

Article in Frontiers in public health, 2024. 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.

Juan Juan LaSchool of Political Science and Public Administration, Xinjiang University, Ürümqi, China.
Man LiSchool of Political Science and Public Administration, Xinjiang University, Ürümqi, China.
Xiaolu LiuUrban Construction College, Hebei Normal University of Science and Technology, Qinhuangdao, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

In recent years, major public health events have had a significant and far-reaching impact on communities. As a response, there has been an increasing interest in enhancing community resilience through innovative ecosystems that involve diverse stakeholders with varying needs and demands. This study investigates the application of innovative ecosystems to improve community resilience in the face of major public health events by utilizing a sequential game approach to balance the interests of government, community, and residents. Subsequently, a comprehensive questionnaire survey was conducted among key stakeholders to ascertain their objectives, requirements and concerns for the innovation ecosystem based on the analysis results of the game model. The reliability and effectiveness of the proposed research method were verified through the analysis and verification of the sequence game model and questionnaire survey results. Finally, according to our analysis results, we propose countermeasures for promoting innovative ecosystems to improve community resilience. The research results indicate that the successful implementation of innovative ecosystems requires consideration of the different needs of stakeholders such as government officials, community members, and residents. Combining these perspectives can effectively promote such systems while enhancing the community's resilience to major public health events.

Indexed as

EcosystemPublic HealthHumansReproducibility of ResultsResilience, PsychologicalSurveys and Questionnairescommunity resilienceinnovative ecosystemmajor public health eventsNash equilibriumsequential game

Identifiers

PMID38726232
PMCPMC11080986

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.