The impact of accession to the European Union on suicide rates: A cross-national time-series analysis
Bibliographic record
Abstract
Building upon prior research, this study examines the effects of European Union (EU) accession on suicide rates in the Eastern European countries that joined the EU in 2004 and 2007 using pooled cross-sectional time-series data that cover approximately 20 years (1990–2011). Results from fixed-effects regression analyses indicate that EU entry has no effect on total suicide rates and suicide rates among males, but has a negative effect on female suicide rates in the fully specified models. In addition, we find that EU entry also has a negative effect on the ratio of suicide rates to an aggregated indicator of lethal violence (homicide rates + suicide rates, or the suicide–homicide ratio) for the total population and for the female population. Consistent with previous research, we find some significant negative effects on suicide rates for economic growth and life expectancy at birth, and a positive effect for females. When interpreted with reference to the ‘stream analogy’ for understanding the two major forms of lethal violence (suicide and homicide), our findings suggest that the impact of any increase in the ‘flow’ of lethal violence associated with EU entry is likely to be manifested in an ‘outward’ rather than ‘inward’ direction for the nations in the sample. Our analyses also reaffirm previous research documenting appreciable gender differences in lethal violence.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".