Job stress prevalence in Iranian drivers and contributing factors: Systematic review and Meta-analysis

This systematic review underscores the urgent need for policies aimed at reducing job stress among Iranian drivers, focusing on enhancing working conditions and providing mental health support to improve overall well-being and safety on the roads.

Authors

Institutions

Publication Details

Journal
Discover Mental Health
Published
2025-04-19
DOI
https://doi.org/10.1007/s44192-025-00188-4
Primary Topic
Health and Well-being Studies
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Job stress prevalence in Iranian drivers and contributing factors: Systematic review and Meta-analysis

Mahdieh Poodineh Moghadam, Somayeh Samani, Mahnaz Afshari, Mohammad Sarani et al.
Discover Mental Health
Health and Well-being Studies
article

Job stress prevalence in Iranian drivers and contributing factors: Systematic review and Meta-analysis

Mahdieh Poodineh Moghadam, Somayeh Samani, Mahnaz Afshari, Mohammad Sarani, Parvaneh Isfahani, Rasoul Corani Bahador
article en

Abstract

This systematic review underscores the urgent need for policies aimed at reducing job stress among Iranian drivers, focusing on enhancing working conditions and providing mental health support to improve overall well-being and safety on the roads.

Discover Mental HealthVol. 5(1)
Isfahan University of Medical Sciences (IR), Zabol University of Medical Sciences (IR)
Decent work and economic growth
Openalex Percentile: Top 89%
Health and Well-being Studies
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.