Does social policy moderate the impact of unemployment on health? A multilevel analysis of 23 welfare states
Bibliographic record
Abstract
BACKGROUND: The magnitude of observable health inequalities between the unemployed and their employed counterparts differs considerably across countries. Few attempts have been made to test theoretical explanations for this cross-national variation. Moreover, existing studies suffer from important theoretical and methodological limitations. This study addresses these limitations and investigates whether differences in the generosity of social protection policies and in public attitudes towards those policies explain why unemployment-related health inequalities are steeper in some societies than in others. METHODS: Multilevel logistic modelling was used to link contextual-level variables on social protection policies and public attitudes in 23 European countries to individual-level data on self-rated health from the 2012 wave of the European Social Survey. RESULTS: The magnitude of inequalities in self-rated health between the unemployed and their employed counterparts varies significantly across countries as a function of cross-national differences in the level of social protection awarded to the unemployed and the level of public support for the welfare state. CONCLUSIONS: The results provide empirical support for the claim that governments can play a more active role in mitigating unemployment-related health inequalities by expanding the generosity and scope of social protection policies. Whether such an expansion of social protection will take place in the current climate of fiscal austerity is a political question whose implications merit the attention of population health scholars.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.012 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".