Evidence map›Paper›PMID 41983076›Full record

ArticleFrontiers in nutrition2026

From subsistence to nutritional well-being: the role of non-agricultural employment in enhancing the dietary consumption patterns of rural residents in China.

Ruolan Yuan, Lu Wang

Abstract read
In one paragraph

Article in Frontiers in nutrition, 2026. 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.

Ruolan YuanSchool of Economics, Management and Law, Jiangxi Science and Technology Normal University, Nanchang, China.
Lu WangCollege of Economics and Management, South China Agricultural University, Guangzhou, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Introduction: This study investigates the impact of non-agricultural employment on food consumption upgrading among rural residents and examines the underlying mechanisms. Methods: Based on data from the 2020 China Rural Revitalization Survey (CRRS), the analysis employs a binary logit model, mediation analysis, an instrumental variable approach, and propensity score matching to empirically assess how non-agricultural employment influences improvements in food consumption structure and quality. Results: The results demonstrate that non-agricultural employment exerts a significant positive effect on both structural and qualitative aspects of rural residents' food consumption, a finding that remains robust across multiple sensitivity tests. Mediation analysis reveals that this effect is transmitted through enhanced health awareness and improved information acquisition capacity-two key pathways linking employment to dietary improvement. Heterogeneity analysis further indicates that the magnitude of this promoting effect varies significantly across age groups and educational levels, highlighting differential responsiveness within the rural population. Discussion: These findings suggest that expanding non-agricultural employment opportunities for rural laborers, strengthening public education on nutrition and food safety, improving rural information infrastructure and market service systems, and implementing targeted guidance strategies can effectively unlock the latent potential for food consumption upgrading. Such measures are essential for advancing dietary modernization and fostering integrated rural revitalization.

Indexed as

food consumptionnon-agricultural employmentnutritional well-beingquality upgradingstructural upgrading

Identifiers

PMID41983076
PMCPMC13070786

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.