Trends in suicides among italian youth aged 10 to 25: A nationwide register study
Bibliographic record
Abstract
Introduction Suicide continues to be a significant cause of mortality in most countries worldwide, especially among youth. Documenting current trends and sources of variation in youth suicide rates is critical to inform prevention strategies. Objectives We aimed to 1. document suicide mortality trends among Italian youth from 1981 to 2016 2. describe age, sex, and urbanization specific suicide rates in this period, and 3. describe suicide methods and their change over time. Methods We relyed on official mortality data for the period 1981-2016 for adolescents and young adults (ages 10-25 years). We estimated standardized all-cause and suicide mortality rates per 100,000 individuals and used Joinpoint regression analysis to determine annual mortality trends and statistically significant changes in rate trends. Analyses were reported by sex, age group, urbanization level and suicide method. Results From 1981 to 2016, 1,752 suicides were identified among youth aged 10-17 (boys/girls ratio in 2016, 5.3) and 9,897 among youth aged 18-25 years (boys/girls ratio in 2016, 4.0). While the all-cause mortality rate decreased over time for both boys and girls, overall suicide rates remained stable for boys and showed a small decrease for girls. For boys, suicide was most common in rural than to metropolitan areas, while it was the opposite for girls. The most common method for boys was hanging, while for girls was fall. Conclusions Differently from other countries, youth suicides were stable (boys) or slightly declining (girls). We found differences according to the urban vs. rural areas. Factors influencing these trends and sex differences are crucial in delivering prevention strategies. Disclosure No significant relationships.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".