Connections between unemployment insurance, poverty and health: a systematic review
Bibliographic record
Abstract
Background: Since the global economic crisis in 2007, unemployment rates have escalated in most European and North American countries. Unemployment protection policies, particularly the unemployment insurance (UI) system, have become a weighty issue for many modern welfare states. Decades of research have established concrete findings on the adverse impacts of unemployment on poverty- and health-related outcomes. This provided a foundation for further exploration into the potential protective effects of UI in offsetting these adverse outcomes. Methods: We developed a systematic review protocol in four stages (literature search, study selection, data extraction and quality appraisal) to ensure a rigorous data collection and inter-rated reliability. We examined the full body of empirical research published between 2000 and 2013 on the pathways by which UI impacts poverty and health. Results: Out of 2233 primary studies identified, a total of 12 met our inclusion criteria. The selected studies assessed poverty-related outcomes (absolute/relative poverty and material hardship) or one or more health-related outcomes (health behaviors, self-rated health, well-being and mental health). Across various UI systems, jurisdictions from high income countries, and study designs, we found good support for our conceptual framework, by which UI attenuates the effect of unemployment on both poverty and health, with a few exceptions. Conclusion: Whether UI impacts differ by age and region might be explored further in future research. The complex mediating relationship between unemployment, UI, poverty and health should further be assessed in light of economic and historical contexts. This could inform decision-making processes during future periods of economic recession.
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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 | 0.007 | 0.050 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".