Self-employment, work and health: A critical narrative review
Bibliographic record
Abstract
BACKGROUND: Self-employment (SE) is a growing precarious and non-standard work arrangement internationally. Economically advanced countries that favor digital labor markets may be promoting the growth of a demographic of self-employed (SE'd) workers who are exposed to particular occupational diseases, sickness, and injury. However, little is known about how SE'd workers are supported when they are unable to work due to illness, injury, and disability. OBJECTIVE: Our objective was to critically review peer-reviewed literature focusing on advanced economies to understand how SE'd workers navigate, experience, or manage their injuries and illness when unable to work. METHODS: Using a critical interpretive lens, a systematic search was conducted of five databases. The search yielded 18 relevant articles, which were critically examined and synthesized. RESULTS: Five major themes emerged from the review: (i) conceptualizing SE; (ii) double-edged sword; (iii) dynamics of illness, injury, and disability; (iv) formal and informal health management support systems; and (v) occupational health services and rehabilitation. CONCLUSION: We find a lack of research distinguishing the work and health needs of different kinds of SE'd workers, taking into consideration class, gender, sector, and gig workers. Many articles noted poor social security system supports. Drawing on a social justice lens, we argue that SE'd workers make significant contributions to economies and are deserving of support from social security systems when ill or injured.
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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 | 0.009 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.011 | 0.009 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".