P03-301 - Changes Of Patterns In Suicidal Behaviour In Hungary In The Last Decade -Results Of Pecs Center
Bibliographic record
Abstract
Introduction A monitoring project within the framework of EU supported MONSUE multicentre study on suicidal behaviour has been performed in Pecs catchment area. Aims To register and statistically analyze all suicide attempts, thus to explore current trends and consequences. Methods Detailed and complete data registration (epidemiologic, socio-demographic, socio-economic, previous attempts, methods, psychiatric diagnoses) of all suicide attempts was carried out in the region for two years from 2007 to 2009. Statistical analysis was performed by SPSS 17.0 to compare our results both with those of the previous study in 1999 and with the national data. Results According to the Hungarian National Statistic Office 2500 suicides occurred in 2008, the population rate is 24.7/100000 inhabitants (40.1/100000 males, 10.7/100.000 females). The most common method for suicide was strangulation in both genders (male: 69.2%, female: 41%). The rate of self-poisoning was higher in females. Regarding suicide attempts we registered 993 cases during the two-year period (males: 41.7%, mean age 38.43 years; females: 58.3%, mean age 42.02 years), which corresponds to former rates. Female attempters were more likely repeaters (60% vs. 40%). The most often used method for suicide attempt was self-poisoning (females 81.9%, males 63.4%) and scarification (females 8.6%, males 21.1%). Conclusions These results suggest that the rates of completed suicides decreased significantly to three quarter compared to the rates in the ‘90s, but in contrary suicide attempts show an increasing tendency. The potential explanations are discussed, including restriction of lethal methods, health care changes and the role of prevention strategies.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".