Inequalities in labour market consequences of common mental disorders
Bibliographic record
Abstract
The burden of mental disorders continues to grow and is now a leading cause of disability worldwide. The prevalence of mental disorders is unequal between population subgroups, and these disorders are associated with unfavourable consequences in social and economic conditions, health and survival. However, how the negative effects of mental disorders are distributed among population subgroups is less studied. Our aim is to investigate how labour market consequences of Common Mental Disorders (CMD) differ over gender, age, education, and country of birth. We use a population sample from southern Sweden of patients diagnosed with CMD 2009-2012 and a matched general population control group with linked register information on employment, long-term sick leave, and disability pension. Logistic regression with interaction effects between CMD and sociodemographic indicators are used to estimate labour market consequences of CMD in the different population subgroups. CMD have a negative impact on all labour market outcomes studied, reducing employment while increasing the risk of long term sick leave and disability pension. However, the associated effect is found to be stronger for men than women, except for disability pension where consequences are similar. Surprisingly, high educated individuals suffer worse labour market consequences than low educated. Consequences of CMD in labour market outcomes are not consistent across different age-groups and country of birth. Inequalities in the labour market consequences of common mental disorders sometimes contributes to, and sometimes mitigates, societal inequalities in employment, long term sick leave and disability pension. When developing new strategies to tackle mental ill health in the population, it may therefore be motivated to consider not only inequalities in the prevalence of mental disorders, but also inequalities in the consequences of these disorders.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.004 | 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".