Diffusion of substance abuse in Serbia
Bibliographic record
Abstract
Substance abuse is a group of diseases or habits that leave serious consequences for human health and life. These include smoking, alcoholism and drug addiction. Substance abuse is also called diseases of conduct. The goal of this research is to assess the prevalence and characteristics of addictions in certain territorial parts of Serbia, based on gender, age, type of settlement, level of education and wealth status. In the implementation of this goal, survey research data were analyzed conducted by the Institute of Public Health of Serbia. The results showed that the addictions are widespread in Serbia. Thus, 10% of school children are constant smokers and 5,4% of them occasionally smoke 2,5 years on average. As for the adult population, 27,7% smoke on daily basis and 5,9% are occasional smokers, so the prevalence of smoking is 33,6%. The average smoking experience is about 19 years. All this is very different in different parts of Serbia, depending on the settlement type, gender, age, education and wealth status. In addition to active smoking on daily basis, there is a high percentage of those who are exposed to cigarette smoke. Only a quarter of the population believes that smoking is harmful, and about a third of the smokers wants to quit smoking. Alcohol consumption is also very widespread - only 57,4% of adults and 63,6% of school children do not drink. There are significant differences by territorial parts of Serbia, settlement type, gender, age, education and wealth status. As well as smoking and alcohol consumption, drug abuse is also widespread in Serbia, although the effects of drugs are known in 35% of cases. The drug is used by about 7% of school children and 17% of adults use pills and 3,5% use marijuana. Of course, there are also significant differences by territorial parts of Serbia, settlement type, gender, age, educational attainment and prosperous state. The general conclusion is that substance abuse in Serbia represents a very significant Social and medical problem.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".