Inequalities in Suicide Rates in the European Union's Elderly: Trends and Impact of Macro-Socioeconomic Factors between 1980 and 2006
Bibliographic record
Abstract
OBJECTIVE: To study suicide rates in elderly people in the former European Community, known as the European Union (EU) since late 1993, to identify differences between early members (admitted to the EU before 2004) and new members (admitted after 2004), and to evaluate the association between macro-socioeconomic variables and suicide rates. METHOD: We explored temporal trends in age-adjusted suicide rates for people aged 65 years and older residing in the EU from 1980 to 2006. RESULTS: In the years examined in the study, there has been a general decrease in suicide rates in new and early members of the EU, although more slowly for elderly men than for women. The decrease in suicide rates of citizens aged 65 years and older was associated with a small but significant difference between new and early members of the EU (RR = 1.04, 95% CI 1.03 to 1.05; z = 11.95, P < 0.001). The macro-socioeconomic indices were strongly associated with age-adjusted suicide rates in EU senior citizens, except unemployment rates. CONCLUSIONS: Deaths by suicide in elderly people are declining in all EU nations, but inequalities in the suicide rates of men and women remain, especially in new EU members.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".