Evidence map›Paper›PMID 40771239›Full record

Trial reportFrontiers in public health2025

Nudging epidemic policy compliance: experimental insights into message framing.

Biao Xu, Patiman Yidilisi, Hailing Xi, Shuyan Gu

Abstract readRandomized Controlled Trial
In one paragraph

Trial report in Frontiers in public health, 2025. 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

4 authors.

Biao Xu *School of Government, Nanjing University, Nanjing, China.
Patiman Yidilisi *School of Government, Nanjing University, Nanjing, China.
Hailing Xi *School of Government, Nanjing University, Nanjing, China.
Shuyan Gu *School of Government, Nanjing University, Nanjing, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Objective: Achieving widespread voluntary public compliance is critical for effective epidemic management. This study investigates how different message-framing strategies influence individuals' willingness to comply with public health measures during a simulated epidemic scenario. Methods: Using a randomized 2 × 2 experimental design, we tested the relative effectiveness of four framing conditions-gain-private, loss-private, gain-social, and loss-social-on compliance intentions. Participants ( Results: Framed messages significantly increased compliance intentions compared to the control condition. Among framing strategies, the loss-social frame (emphasizing negative societal consequences of noncompliance) demonstrated the strongest effect, followed by gain-private, gain-social, and loss-private frames. Pairwise comparisons revealed important interactions: gain-framing was more effective within private motivational contexts, whereas loss-framing was particularly compelling within social contexts. Critically, loss-social messages were significantly superior to loss-private ones, while gain-social and gain-private messages performed similarly. Conclusion: Strategic message framing effectively enhances public compliance during epidemic crises, with loss-social framing emerging as the most potent approach. These findings offer critical insights for policymakers and health communicators, recommending targeted use of loss-social messaging to optimize public adherence to epidemic prevention guidelines.

Indexed as

EpidemicsHealth PolicyPersuasive CommunicationPublic HealthAdultFemaleHumansIntentionMaleMiddle AgedMotivationbehavioral public policyframing effecthealth policynudgingpublic compliance

Identifiers

PMID40771239
PMCPMC12325323

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.