| Field |
Value |
Language |
|
dc.contributor.author |
Li, J |
|
|
dc.contributor.author |
Zhang, X |
|
|
dc.contributor.author |
Li, Y |
|
|
dc.contributor.author |
Ding, W |
|
|
dc.contributor.author |
Duan, W |
|
|
dc.contributor.author |
Lim, D |
|
|
dc.contributor.author |
Liang, Y |
|
|
dc.contributor.author |
Feng, Z |
|
|
dc.date.accessioned |
2026-02-06T01:16:19Z |
|
|
dc.date.available |
2026-02-06T01:16:19Z |
|
|
dc.identifier.citation |
Frontiers in Public Health, 14 |
|
|
dc.identifier.issn |
2296-2565 |
|
|
dc.identifier.uri |
http://hdl.handle.net/10453/193030
|
|
|
dc.description.abstract |
<jats:sec>
<jats:title>Background</jats:title>
<jats:p>Work-related musculoskeletal disorders (WMSD) are highly prevalent among coal miners and pose a significant threat to occupational health. Understanding the underlying risk factors and developing a predictive model for WMSD risk can help to mitigate WMSD.</jats:p>
</jats:sec>
<jats:sec>
<jats:title>Objective</jats:title>
<jats:p>To identify key determinants of WMSD among coal miners in Jinang, China, and construct a predictive model to assess risk.</jats:p>
</jats:sec>
<jats:sec>
<jats:title>Methods</jats:title>
<jats:p>One thousand four hundred nine coal miners from two coal mining companies were surveyed using the modified Chinese Muscle Questionnaire (CMQ). Prevalence rates and risk factors were assessed using logistic regression. Machine learning algorithms were applied to construct the predictive model.</jats:p>
</jats:sec>
<jats:sec>
<jats:title>Results</jats:title>
<jats:p>
The 12-month overall prevalence of WMSD was 82%, with the neck (59.5%), shoulders (53.4%), and lower back (46.5%) being the most affected. Eight variables, including smoking behaviors, perceived health status, and uncomfortable working posture, were significantly associated with WMSD (
<jats:italic>p</jats:italic>
< 0.05). The neural network model achieved the highest performance (area under the curve: 0.886 on training and 0.704 on test). The fused model outperformed individual models in the final stacking integration learning.
</jats:p>
</jats:sec>
<jats:sec>
<jats:title>Conclusion</jats:title>
<jats:p>Work-related musculoskeletal disorders are highly prevalent among Chinese coal miners and are influenced by personal and work-related factors. Machine learning models, particularly ensemble approaches, offer promise for risk prediction and targeted prevention.</jats:p>
</jats:sec> |
|
|
dc.language |
en |
|
|
dc.publisher |
Frontiers Media SA |
|
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dc.relation.ispartof |
Frontiers in Public Health |
|
|
dc.relation.isbasedon |
10.3389/fpubh.2026.1729879 |
|
|
dc.rights |
info:eu-repo/semantics/openAccess |
|
|
dc.subject |
1117 Public Health and Health Services |
|
|
dc.subject.classification |
4203 Health services and systems |
|
|
dc.subject.classification |
4206 Public health |
|
|
dc.title |
Determinants of work-related musculoskeletal disorders among coal miners in Jining, China: development of a predictive risk model |
|
|
dc.type |
Journal Article |
|
|
utslib.citation.volume |
14 |
|
|
utslib.for |
1117 Public Health and Health Services |
|
|
pubs.organisational-group |
University of Technology Sydney |
|
|
pubs.organisational-group |
University of Technology Sydney/Faculty of Health |
|
|
pubs.organisational-group |
University of Technology Sydney/UTS Groups |
|
|
pubs.organisational-group |
University of Technology Sydney/UTS Groups/Improving Palliative, Aged and Chronic Care through Clinical Research and Translation (IMPACCT) |
|
|
pubs.organisational-group |
University of Technology Sydney/UTS Groups/UTS Ageing Research Collaborative (UARC) |
|
|
pubs.organisational-group |
University of Technology Sydney/UTS Groups/Digital, Virtual and AI in Health Collaborative (DVAIHC) |
|
|
pubs.organisational-group |
University of Technology Sydney/UTS Groups/The Trustworthy Digital Society |
|
|
pubs.organisational-group |
University of Technology Sydney/UTS Groups/UTS LGBTIQA+ Research Network |
|
|
utslib.copyright.status |
open_access |
* |
|
dc.rights.license |
This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0). To view a copy of this license, visit https://creativecommons.org/licenses/by/4.0/ |
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|
dc.date.updated |
2026-02-06T01:16:18Z |
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|
pubs.publication-status |
Published online |
|
|
pubs.volume |
14 |
|
