Precarious employment in self-employment: A typology and impact on cardiovascular health conditions in Sweden
Bibliographic record
Abstract
BACKGROUND: Research on health in self-employment shows mixed findings, partly due to limited focus on heterogeneity within self-employment, physical health outcomes and reliance on self-reported, cross-sectional data. This study addresses these gaps by identifying self-employment types using the 'precarious employment framework' and examining their association with cardiovascular health conditions in Sweden. METHODS: Using the Swedish Work, Illness, and Labour Market Participation (SWIP) cohort, we analyzed individuals born between 1948 and 1968, aged 40-60 in 2008, and living in Sweden in 2005. We identified a typology of precarious self-employment in 2008 (N = 281,251), with cardiovascular health conditions tracked between 2009 and 2020. Latent Class Analysis (LCA) was used to categorize self-employment based on six indicators of precarity: business type, prior unemployment, combined employment, number of employees, income, and income volatility. Cox proportional hazards models estimated the association between the self-employment types and cardiovascular health conditions (diagnoses for myocardial infarction and stroke) compared to waged employment, adjusting for covariates. RESULTS: We identified four self-employment types: entrepreneurial employers, precarious solo self-employed, own-account combiners, and small traders. Precarious self-employment among 40-to-60-year-olds was associated with a higher risk of cardiovascular conditions later in life. The 'precarious employment framework' effectively captures the heterogeneity of self-employment and highlights its role as a social determinant of cardiovascular health. CONCLUSION: Our findings suggest that precarious self-employment is linked to increased cardiovascular risk. This underscores the importance of considering employment quality and heterogeneity in future research and public policies addressing self-employed populations.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".