QUANTITATIVE AND QUALITATIVE ANALYSIS OF CONSUMPTION OF NARCOTIC DRUGS AND PSYCHOACTIVE SUBSTANCE BY THE BENEFICIARIES OF THE CENTER FOR MENTAL HEALTH AND PREVENTION OF ADDICTION LLC IN 2013-2017.
Bibliographic record
Abstract
Our research aims to produce qualitative and quantitative analysis of the use of narcotic drugs and psychoactive substances in 2013-2017 and their impact on drug abuse in the country. We studied 1519 medical cards of hospitalized beneficiaries. According to the obtained results, 'pharmacy' drug addiction is still widespread in Georgia. According to our data, it is hardly possible to determine, whether drug addicts consume the agents obtained at Georgian pharmacy network, or use the smuggled psychoactive substances. Regrettably, the consumption of opiates- 'Black tar' and heroin has increased again. It should be noted that beneficiaries don't indicate the consumption of ecstasy and similar-type preparations without a special survey, since the patients apparently do not classify them as narcotic drugs, as in the case of marijuana. Georgia's drug policy is focused more on reducing the drug supply, rather than its demand. Based on the analysis of the present material we can conclude that the imposition of criminal liability and toughening of the administrative measures are hardly enough to achieve an optimal goal in terms of drug use reduction. It is necessary to implement, at appropriate scale, a set of complex measures that will be tailored to administrative measures, including preventive, remedial and rehabilitation measures.
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 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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".