Welfare state retrenchment and increasing mental health inequality by educational credentials in Finland: a multicohort study
Bibliographic record
Abstract
OBJECTIVES: Epidemiological studies have shown an association between educational credentials and mental disorders, but have not offered any explanation for the varying strength of this association in different historical contexts. In this study, we investigate the education-specific trends in hospitalisation due to psychiatric disorders in Finnish working-age men and women between 1976 and 2010, and offer a welfare state explanation for the secular trends found. SETTING: Population-based setting with a 25% random sample of the population aged 30-65 years in 7 independent consecutive cohorts (1976-1980, 1981-1985, 1986-1990, 1991-1995, 1996-2000, 2001-2005, 2006-2010). PARTICIPANTS: Participants were randomly selected from the Statistics Finland population database (n=2,865,746). These data were linked to diagnosis-specific records on hospitalisations, drawn from the National Hospital Discharge Registry using personal identification numbers. Employment rates by educational credentials were drawn from the Statistics Finland employment database. PRIMARY AND SECONDARY OUTCOME MEASURES: Hospitalisation and employment. RESULTS: We found an increasing trend in psychiatric hospitalisation rates among the population with only an elementary school education, and a decreasing trend in those with higher educational credentials. The employment rate of the population with only an elementary school education decreased more than that of those with higher educational credentials. CONCLUSIONS: We propose that restricted employment opportunities are the main mechanism behind the increased educational inequality in hospitalisation for psychiatric disorders, while several secondary mechanisms (lack of outpatient healthcare services, welfare cuts, decreased alcohol duty) further accelerated the diverging long-term trends. All of these inequality-increasing mechanisms were activated by welfare state retrenchment, which included the liberalisation of financial markets and labour markets, severe austerity measures and narrowing down of public sector employment commitment.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".