The human cost of fast deliveries: A systematic literature review of occupational risks and safety outcomes in last-mile delivery workers
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it