What is known about the health of location-based and online web-based digital labour platform workers? A scoping review of the literature
Bibliographic record
Abstract
BACKGROUND: Digital labour platforms are transforming work organization, offering new opportunities but also raising concerns about precarious conditions and health risks. Despite increasing attention to platform work, limited research has examined its direct impact on workers' physical, mental, and social well-being. OBJECTIVES: The objective of this scoping review is to examine current empirical studies investigating the health effects of working via digital labour platforms, aiming to (i) summarize the existing evidence, (ii) pinpoint knowledge gaps, and (iii) identify areas for methodological enhancements. METHODS: We search for peer-reviewed studies published until December 2024 from Web of Science and PubMed, alongside grey literature. Inclusion criteria covered papers with original data, using qualitative, quantitative, or mixed methods, resulting in 40 included studies. A pre-established theoretical framework guided result reporting, emphasizing three characteristics affecting worker health: (i) business practices, (ii) employment conditions, and (iii) work environment hazards. RESULTS: In summary, literature shows a link between digital platform work and poor health. The current evidence, mainly focused on mental health and location-based platform workers, highlights factors contributing to poor physical and mental health, including low-quality employment conditions and psychosocial work environment hazards. Limited evidence suggests a correlation between business practices-algorithmic management and rating systems-and poor mental health. Knowledge gaps include the health impact of web-based platforms, especially medical consultation ones and location-based domestic and care services platforms, and less-explored outcomes like musculoskeletal pain and occupational injuries. Methodological limitations, such as low sample size and lack of control groups, were noted. CONCLUSIONS: This review identifies methodological improvements and knowledge gaps, guiding future research to comprehend the impact of digital platform work on health. As legislation evolves to enhance platform workers' job conditions, researching their health is crucial for offering practical recommendations and shaping evidence-based policies.
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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 | 0.012 | 0.061 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.023 | 0.017 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".