Sickness absence due to common mental disorders among precarious and non-precarious workers
Bibliographic record
Abstract
Abstract Background Mental health disorders have become one of the leading diagnoses causing sickness absence. Previous studies have examined the impact of single employment characteristics or working conditions on sickness absence. However, few studies have investigated the effect of a multidimensional construct of precarious employment on sickness absence. Therefore, this study aims to describe sickness absence due to common mental disorders (CMD) as a proxy for access to social security benefits among precarious and non-precarious workers with mental health problems. Methods Cohort register-based study of the total Swedish population aged 27 to 61 years residing in Sweden in 2016 and having mental health problems defined as being prescribed Selective Serotonin Reuptake Inhibitors (SSRI) in 2017 (N = 19,691). Individuals were classified as precariously employed or not based on a precarious employment score measured multidimensionally in 2016 (i.e., employment insecurity, income inadequacy, and lack of social protection). The outcome was the incidence of the first sickness absence episode due to CMD co-occurring with SSRI treatment in 2017. Logistic regression models will be performed. Results The following results are preliminary. Precariously employed treated with SSRI were 8,68% in 2017. The distribution of a first sickness absence episode due to common mental disorders was similar in precarious and non-precarious workers (12.35% and 12.42%, respectively). Individuals directly employed (12.20%), with multiple jobs holding (14.62%), and low-medium income levels (14%) had higher sickness absence incidence due to common mental disorders. There were slight differences by gender. Conclusions In these preliminary results, no differences were found between precarious and non-precarious workers with mental health problems in the distribution of sickness absence due to CMD. Further analysis will be conducted to investigate whether precarious employment is associated with sickness absences. Key messages
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".