ArticleData in brief2023
A dataset of Chinese drivers' driving behaviors and socio-cultural factors related to driving.
Article in Data in brief, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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.
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.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Given the high fatality rate due to road traffic accidents in China, understanding the factors influencing aggressive driving behaviors among Chinese drivers is essential to alleviate the problem. The paper describes a dataset of 1039 Chinese drivers' driving behaviors and the socio-cultural factors associated with the behaviors. The dataset was collected through an online survey. The dataset comprises five main categories: 1) driving information, 2) aggressive driving behaviors, 3) friend/peer influence, 4) family influence, and 5) socio-demographic information. The dataset is valuable for public health and transportation researchers to explore factors influencing drivers' driving behaviors and public safety in China. The dataset's construct validity was confirmed by the Bayesian Mindsponge Framework (BMF) analytics. Specifically, the analysis shows that safe driving behaviors are affected by information promoting safe driving that is passively and actively absorbed from friends/peers (friends/peers being role models and friends'/peers' support, respectively). The result is consistent with the Mindsponge Theory's information-processing mechanism in human minds.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.