Análisis de la producción científica internacional sobre evaluación de la efectividad de las políticas, planes, programas y proyectos en la prevención del consumo de drogas (Parte I)
Bibliographic record
Abstract
[Objective]: To characterize global research on the effectiveness of drug use prevention policies, plans, programs and projects, based on the analysis of research articles published and indexed in scientific databases. \n[Material and methods]: Articles on this topic contained in the Web of Science Core Collection database were downloaded and transferred to a Microsoft Access database using a broad search profile that has already been successfully applied in other works. To characterize the scientific production, methods and indicators from the field of bibliometrics were used, as well as social network analysis. \n[Results]: The number of published articles (1266) increased, only diminished in the first years of the epidemic by Covid-19. The subject areas with the highest number of papers were Substance Abuse (435), Public Environmental & Occupational Health (394), Psychiatry (163) and Psychology Clinical (157). Articles published by institutions in the United States, United Kingdom, Australia and Canada predominated. The most studied topics were those related to alcohol abuse, smoking, opioids and cannabis. \n[Conclusions]: A progressive increase in the number of articles on the analyzed topic and institutional collaboration has been confirmed, which is indicative of its health and scientific interest. Alcoholism and smoking are the two most studied addictions in these papers, and the most frequent and most cited topics deal with prevention in adolescence, therapeutic compliance, the effectiveness of harm reduction programs and prevention from primary care.
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 | 0.171 | 0.321 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.010 |
| Bibliometrics | 0.054 | 0.050 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".