Evidence map›Paper›PMID 41974810›Full record

ArticleScientific reports2026

Determinants of delivery rider disability severity based on judicial adjudication records in China.

Xingchen Yan, Tong Ye, Jun He, Xiaofei Ye, Jun Chen, Tao Wang, Mingyang Du

Abstract read
In one paragraph

Article in Scientific reports, 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

7 authors.

Xingchen YanCollege of Automobile and Traffic Engineering, Nanjing Forestry University, Longpan Road 159#, Nanjing, 210037, China. xingchenyan.acad@gmail.com.
Tong YeCollege of Automobile and Traffic Engineering, Nanjing Forestry University, Longpan Road 159#, Nanjing, 210037, China.
Jun HeCollege of Automobile and Traffic Engineering, Nanjing Forestry University, Longpan Road 159#, Nanjing, 210037, China.
Xiaofei YeSchool of Maritime and Transportation, Ningbo University, Fenghua Road 818#, Ningbo, 315211, China.
Jun ChenJiangsu Key Laboratory of Urban ITS, School of Transportation, Jiangning Development Zone, Southeast University Jiangsu Province Collaborative Innovation Center of Modern Urban Traffic Technologies, Southeast University, Dongnandaxue Road 2#, Nanjing, 211189, China.
Tao WangSchool of Architecture and Transportation, Guilin University of Electronic Technology, Jinji Road 1#, Guilin, 541004, China.
Mingyang DuCollege of Automobile and Traffic Engineering, Nanjing Forestry University, Longpan Road 159#, Nanjing, 210037, China.

Funding

Guangxi Science and Technology Key Program AB25069483Humanities and Social Science Fund of Ministry of Education of China 23YJCZH042National Natural Science Foundation of China 52262047Natural Science Foundation of Jiangsu Province BK20180775Natural Science Foundation of Ningbo City 2024J130Natural Science Foundation of Zhejiang Province MS25E080023Natural Science Research of Jiangsu Higher Education Institutions of China 23KJB580011
6 · The paper itself

Abstract

The rapid expansion of the gig economy has increased instant delivery crashes (IDCs), yet the determinants of rider disability remain unclear. This study analyzed 633 adjudicated rider injury cases (2015–2023) from Chinese legal judgments to identify risk factors for the Highest Assessed Disability Grade (HADG). We integrated sociodemographic, vehicular, spatiotemporal, and crash-mechanism variables into ordered logit models to quantify risk determinants. Results indicate that while most adjudicated injuries were non-permanent, lower-limb fractures constituted the primary mechanism of established disability. Significant risk factors for severe disability included rider age over 50, migrant status, and collisions involving automobiles. Notably, a counter-intuitive “liability paradox” emerged: riders bearing secondary liability faced the highest risk of severe disability (OR = 1.943), surpassing those with primary liability (OR = 1.732) due to kinetic asymmetry. Addressing these distinct mechanisms requires a multi-layered framework. We recommend accelerating physical infrastructure segregation to decouple riders from high-energy motor traffic. Conversely, to curb the behavioral precursors of rider-at-fault crashes, we propose a “traffic safety credit system” combining dispatch suspensions with mandatory re-education. This study validates the utility of forensic data, offering actionable, evidence-based pathways to mitigate the severe human costs of the on-demand sector.

Indexed as

Accidents, TrafficPersons with DisabilitiesAdolescentAdultChinaFemaleHumansMaleMiddle AgedRisk FactorsYoung AdultDelivery ridersInjury characteristicsInstant delivery crashesLegal recordsMarginal effectOccupational healthOrdered logit modelRisk factors

Identifiers

PMID41974810
PMCPMC13237020

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.