Scoping review of transport workforce exposure to COVID-19 and other respiratory pandemic diseases
Bibliographic record
Abstract
Abstract Background Transport workers may be at heightened occupational risk during pandemics. However, there is a lack of research that specifically focuses on understanding the factors contributing to this risk and the strategies that can reduce their risk. This study aimed to understand the current state of research regarding the transmission of respiratory pandemic diseases for transport workers and to identify any existing evidence-based recommendations that can help mitigate the risks associated with these diseases in the transport industry. Methods A scoping review was undertaken which adhered to the PRISMA guidelines, and a comprehensive search was conducted in English-language databases for peer-reviewed research articles. Our search across multidisciplinary databases returned a total of 12540 sources, with 39 articles included in the final review. The results were summarized, coded, and thematically categorized. Results Most studies employed quantitative methodologies (24/39) and reported high percentage of male participants. Spatial distribution analysis demonstrated that the coverage of the literature was uneven across different continents. Findings revealed that transport workers faced a heightened risk of exposure due to crowded work settings, job demands, and inadequate use of personal protective equipment. Systemic and structural inequalities were identified among transport workers. Conclusions This study provides insights for policy and practice interventions that address the unique needs and challenges faced by workers in the industry, including how to promote social equity and justice during pandemics. There is a need for more qualitative and mixed-methods research approaches to obtain a more comprehensive understanding of the topic. Given the global nature of risks for respiratory infections and the global transport sector (which is a significant transmission point for societies), more research is required in non-traditional contexts outside US, Canada, EU, and UK. Key messages • During respiratory pandemic diseases transport workers face heightened risk of infection, inadequate resources, and systemic inequalities. Need for focus on certain regions, and policy interventions. • Respiratory pandemic diseases impact on transport industry & workers reviewed, revealing geographic disparities in focus of literature, & socio-economic challenges. Policy interventions needed.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.017 | 0.082 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.023 | 0.022 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".