Sickness absence trajectories following labour market participation patterns: a cohort study in Catalonia (Spain), 2012-2014
Bibliographic record
Abstract
Abstract Background Previous studies have investigated the relationship between employment pathways and health-related outcomes based on cross-sectional or longitudinal approaches. However, little is known about the cumulative effects of employment status mobility on sickness absence (SA) over time. The present study aims to examine the association between prior labour market participation (LMP) patterns and SA trajectories from a life course perspective.Methods Cohort study based on a sample of 11,968 salaried workers affiliated with the Spanish Social Security system, who accumulated more than 15 days on SA at least in one quarter during 2012-2014. Individuals were grouped into three different working life stages: early (18-25 years), middle (26-35 years) and late (36-45 years). Sequence analysis and cluster analysis were applied to identify LMP patterns (2002-2011). Latent class growth modelling was used to identify SA trajectories (2012-2014). Finally, multinomial logistic regression models were applied to assess the relationship between LMP patterns and SA trajectories.Results First, seven LMP patterns were obtained: stable employment (63%-81%), increasing employment (5%-22%), without long-term coverage (7%-8%), decreasing employment (4%-10%), fluctuant employment (13%-14%), steeply inflow into unemployment (9%), and steeply labour market exit (7%-9%). Second, four SA trajectories were identified: low stable (values range: 83%-88%), decreasing (5%-9%), increasing (5%-11%) and high stable (7%-16%). Third, no significant associations were observed among LMP patterns and SA trajectories, except for young men, where an increasing employment pattern was significantly associated with a lower risk to increase days on SA (aOR: 0.21 [95% CI: 0.04-0.96]).Conclusions SA trajectories are not related to prior 10-year LMP patterns at any stage of working life. To disentangle this relationship, future research might benefit from considering working life transitions with a quality of work approach framed with contextual factors closer to the SA course.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".