Аnalysis of knowledge of students of higher educational institutions of poltava about the harmfulness of drug use.
Bibliographic record
Abstract
OBJECTIVE: Introduction: The urgency of the topic is due to the fact that in recent years in Ukraine a part of youth who use narcotic drugs and psychoactive substances is growing. Today there are more than a quarter of billion drug addicts in the world. This number includes those who have tried drugs at least once. Among them - about 27 million drug addicts that are in need of treatment. The aim: to analyze the knowledge of the students of higher educational institutions of Poltava about various issues related to drug addiction. PATIENTS AND METHODS: Materials and method: 600 questionnaires of students who study at the universities (Poltava). The following methods were used: historical - analytical and bibliosemantic, medico-statistical, sociological, system approach and system analysis. RESULTS: Results: Student youth responded to various questions that were presented in the questionnaire, about various issues related to drug addiction. 26 % of respondents indicated that they know places where drugs can be purchased. To the question «Do you know what drugs are?» оnly 7 % answered «No». 2, 5 % of those surveyed met with drug users on the street. 13 ± 1, 51 % (р<0, 05) of the respondents admitted that they were irritated when they were criticized for using narcotic drugs and only 9 ± 1, 25 % (р<0, 05) felt guilty about abuse. CONCLUSION: Сonclusions: Students should be aware of the problem of the use of psychoactive substances, be aware of the legislative framework for the prevention of consumption, turnover and any manipulations with psychoactive substances.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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) | 0.007 | 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".