Trajectories of precarious employment and the risk of myocardial infarction and stroke among middle-aged workers in Sweden: A register-based cohort study
Bibliographic record
Abstract
BACKGROUND: The aim is to identify trajectories of precarious employment (PE) over time in Sweden to examine associations of these with the subsequent risk of myocardial infarction (MI) and stroke. METHODS: This is a nation-wide register-based cohort study of 1,583,957 individuals aged 40 to 61 years old residing in Sweden between 2003-2007. Trajectories of PE as a multidimensional construct and single PE components (contractual employment relationship, temporariness, income levels, multiple job holding, probability of coverage by collective agreements) were identified for 2003-2007 by means of group-based model trajectories. Risk Ratios (RR) for MI and stroke according to PE trajectories were calculated by means of generalized linear models with binomial family. FINDINGS: Adjusted estimates showed that constant PE and borderline PE trajectories increased the risk of MI (RR: 1·08, CI95%:1·05-1·11 and RR:1·13, CI95%: 1·07-1·20 respectively) and stroke (RR:1·14, CI95%: 1·10-1·18 and HR:1·24, CI95%: 1·16-1·33 respectively) among men. A higher risk of stroke in men was found for the following unidimensional trajectories: former agency employees (RR:1·32, CI95%:1·04-1·68); moving from high to a low probability of having collective agreements (RR: 1·10, CI95%:1·01-1·20). Having constant low or very low income was associated to an increased risk of MI and Stroke for both men and women. INTERPRETATION: The study findings provide evidence that PE increases the risk of stroke and possibly MI. It highlights the importance of being covered by collective bargaining agreements, being directly employed and having sufficient income levels over time. FUNDING: The Swedish Research Council for Health, Working Life and Welfare, no. 2019-01226.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".