ADMINISTRATIVE AND LEGAL MECHANISM OF PREVENTING THE SUICIDE IN UKRAINE
Bibliographic record
Abstract
The article focuses on the study of suicide worldwide and Ukraine in particular. The phenomenon of suicide is relevant in all corners of the world, it affects people of all nations, cultures, religions, articles, and classes. The scientific community in many countries around the world demonstrates indifference to the problem of suicide; Accordingly, suicide is gradually becoming one of the leading causes of death worldwide. Thus, suicide ranks 15th among the leading causes of death. WHO statistics show that suicide is committed twice as often as murder, and emphasizes that this phenomenon is global and reaches critical levels every year. We have analyzed the regulatory framework for suicide at the global level. For example, over the last decades, since 2000, due to the incredible efforts of WHO, this problem has begun to receive national attention. In the developed world, many regulations on suicide prevention have been developed and adopted. In the course of scientific research, we found out that suicide and Ukraine is the seventh cause of death, which confirms the criticality of the problem and the need for its fastest solution. We believe that there is a need today to support such categories of persons as children and young people, servicemen, convicts, and the elderly. The article examines the experience of such foreign countries as the USA, Azerbaijan, Israel, Canada, Australia, New Zealand, Great Britain, and other European countries of the world. Finding out what prevention and prevention measures they have implemented in national suicide prevention programs, we see the possibility of their implementation in Ukraine and are convinced of their effectiveness. According to the results of scientific research, we will develop an administrative and legal mechanism for suicide prevention in Ukraine, which can work if all the steps of the algorithm for reducing suicide rates are fulfilled. Keywords: suicide, administrative and legal mechanism, the algorithm of actions, statistics, 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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".