Suicide rates in the European Union Countries. An analysis from a multivariate approach
Bibliographic record
Abstract
Suicide is one of the most important causes of death in the European Union Countries (EU) and is considered as a phenomenon which can be explained from a psychological, biological and social point of view. Objectives: This research will analyze the phenomenon of suicide in the European Union from a sociological point of view, with the aim of creating a multivariate model which explains such phenomenon. Method: Taking into account those data offered by the European Statistics Office (EUROSTAT), this study will try to explain, through the multiple linear regression model, suicide rates in European countries from demographic variables (number of inhabitants, divorce rate, ratio of women), economic variables (Gross Domestic Product (GDP), general government gross debt), social variables (government expenditure on social protection, population at risk of poverty) or educational variables (public expenditure on education and population with secondary education). Conclusions: A model to explain suicide rates in different countries was developed. This model was made up of two variables (percentage of people with secondary education and ratio of women), which account for 50% of the suicide rate.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 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".