The human cost of fast deliveries: A systematic literature review of occupational risks and safety outcomes in last-mile delivery workers
Bibliographic record
Abstract
The rapid growth of the gig economy has transformed urban labor markets in the digitalization era, particularly in the last-mile delivery industry. Despite its expansion, the working conditions and occupational risks faced by ‘last-mile workers’ remain underexplored, with limited systematic evidence on their psychosocial, health, and safety challenges. Aim This study aims to systematically review empirical evidence on the occupational risks, psychosocial outcomes, and safety of last-mile delivery workers, focusing on how working conditions, delivery modalities, and road safety issues shape their health and well-being. Methods A systematic search was conducted in PubMed, Scopus, and Web of Science for studies published until 2025, following PRISMA guidelines. The included studies were evaluated for methodological quality using the Newcastle-Ottawa Scale (NOS) for observational studies and the CASP checklist for qualitative research. Results A total of 32 studies, covering a total of 38,682 last-mile workers, were included. Poor working conditions (e.g., economic insecurity, algorithmic control, time pressure) were consistently associated with negative psychosocial outcomes, including stress, fatigue, burnout, and reduced mental well-being. Motorized two-wheelers were found to have higher crash and injury risks than bicycles or light vehicles, primarily due to higher speeds and greater traffic exposure. Psychosocial stressors related to algorithmic management and piece-rate pay significantly influenced safety and health-related behaviors, linking high stress and workload to riskier riding/driving practices. Conclusion These findings highlight the need for multidimensional interventions targeting both the physical and psychosocial risks faced by last-mile workers, including safer work environments, occupational health initiatives, support for mental well-being, and more sustainable work practices.
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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 | 0.013 | 0.074 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.007 |
| Bibliometrics | 0.018 | 0.016 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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".