A Survey on the Number of Drug Addicts in the Arab Countries and the Centers of Treatment and After-Care
Bibliographic record
Abstract
This study aims to identify the numbers of drug users in several Arab countries during the first, second, third, and fourth quarters of 2016, 2017, and 2018, according to available official statistics, and to learn the types and quantities of drugs and psychotropic substances seized in Arab countries according to the official statistics available during the first quarter of 2019. The study also seeks to identify the number of users, distributed according to the type of substance abuse during the first quarter of 2019, according to available official statistics. It also seeks to find out the most important treatment and aftercare centers for addicts in some Arab countries. The researcher uses the descriptive-survey method for statistics of some Arab countries on drug abuse and addiction. This study describes cases of abuse and addiction between 2016 and 2019. It reaches a set of results, one of which is that the number of drug users in 2016 reached 46,942 drug users, in 2017 it reached 126,216 drug users, during 2018 it reached 111,351 users, and the number of drug users during the first quarter of 2019 was 30,897 males and 248 females. Among the recommendations the study makes are the following: To prepare periodic Arab reports on the prevalence of drug abuse within Arab countries and the exchange of data and information between those countries To improve mechanisms for obtaining information on drug abuse in the Arab countries • To strengthen cooperation between Arab countries in the field of drug control
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".