Evidence map›Paper›PMID 39624318›Full record

ArticleHeliyon2024

The driving mechanism of citizens' protective behaviors for rainstorm flood disasters in China: An empirical study based on the protection motivation theory and protective action decision model.

Qunying Wu, Rui Li

Abstract read
In one paragraph

Article in Heliyon, 2024. 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

2 authors.

Qunying WuInstitute for Disaster Management and Reconstruction, Sichuan University, No.122, Huanghe Middle Road, Shuangliu District, Chengdu, 610200, China.
Rui LiChengdu Vocational & Technical College of Industry, No. 818, Da' an Road, Tianfu New District, Chengdu, 610218, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background/significance: Rainstorm floods have become a high-impact natural disaster and are expected to become more extreme shortly, seriously threatening human safety. Although the government issues timely and precise rainstorm flood warning information, citizens remain indifferent and engage in maladaptive behavior. Therefore, understanding the relationship between emergency information, psychological cognition, individual characteristics, and protective behavior is crucial for effective risk information communication and evacuation guidance. Method/process: This study utilizes the protection motivation theory (PMT) and protective action decision model (PADM) to develop a structural equation model (SEM) that evaluates the factors driving both protective and non-protective behaviors of individuals to rainstorm flood disasters. Result/conclusion: The findings indicate that the disaster experience and emergency information received significantly shape how individuals perceive risks, their ability to manage them, and who should be accountable for the disaster. The findings further indicate that how people view their ability to handle a risk situation (perceived coping ability) and who they think is responsible for the situation (perceived attribution of responsibility) directly influence whether they will take protective behaviors. Based on these results, this study puts forward policy recommendations for emergency information release strategies to enhance citizens' willingness to adopt protective behaviors.

Indexed as

Non-protective behaviorProtection motivation theoryProtective action decision modelProtective behaviorRainstorm floods

Identifiers

PMID39624318
PMCPMC11609482

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.