Low-quality employment trajectories and mental health disorders among Swedish and migrant workers
Bibliographic record
Abstract
Abstract Aim This study aims to examine the effects of low-quality employment trajectories on severe common mental disorders (CMD) according to Swedish and foreign background. Methods This is a longitudinal study based on Swedish population registries (N = 2,703,687). Low- and high-quality employment trajectories observed across five years (2005-2009) are the exposure with severe CMD as outcome (2010-2017). Adjusted hazard ratios (HR) were calculated using Cox regression stratified according to background (first-generation (i) EU migrants, (ii) non-EU migrants, (iii) second-generation migrants, (iv) Swedish-born with Swedish background) and sex. The reference group were Swedish-born with Swedish background in a Constant high-quality employment trajectory. Results Second-generation migrants had an increased risk of CMD compared to Swedish-born with Swedish background when following low-quality employment trajectories (e.g., male in Constant low-quality HR: 1.53, 95% CI: 1.41-1.68). Female migrant workers, especially first-generation from non-Western countries in low-quality employment trajectories (e.g., Constant low-quality HR: 1.65, 95% CI:1.46 - 1.87), had a higher risk of CMD compared to female Swedish-born with Swedish background. The confidence interval for CMD risk showed little differences between migrant groups (1st and 2nd generation) compared to the reference group. Conclusions Low-quality employment trajectories appear to be determinants of risk for CMD, having a differential impact according to background of origin and sex. We observe a higher risk for severe CMD across migrant groups, especially second-generation migrants, compared to Swedish-born with Swedish background. Further qualitative research is recommended to understand the mechanism behind the differential mental health impact of low-quality employment trajectories according to foreign background. Key messages • First and second-generation migrants in low quality employment have higher risk of severe common mental disorders compared to Swedish born with Swedish background workers in low quality employment. • Policies targeting working conditions in low-quality employment and promoting workers mental well-being are essential to reduce this higher risk for developing CMD, especially for migrant 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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".