Trends and inducing factors for illicit drug use in Grenada: Epoch 2001 – 2009.
Bibliographic record
Abstract
ObjectiveThe psychosocial aspect of drug use is seldom researched in Caribbean nations.Drug use in the Caribbean has been on the rise since the 1990s.Statistical indicators have established evidence for the increased rates of illegal drug use.This study briefly reviewed these indicators and explored factors that influenced the state of drug affairs in Grenada from 2001 to 2009. MethodsInterviews conducted in a semi-structured form were carried out on key stakeholders involved in drug prevention in Grenada.Literary review of pertinent articles from search engines was used to buttress results.Further search through statistical records provided by the Drug Control Secretariat and Grenada Drug Information Network/National Observatory on Drugs (GRENDIN/NOD) was used to obtain information on recent developments surrounding drug related activities in Grenada. ResultsTrends show marijuana as the drug of choice and males being primarily involved in illegal drug activities.Additionally, cultural, and psychological factors play major roles in the proliferation of the drug problem in Grenada. ConclusionDespite preventive measures used to raise awareness on the dangers of drug use, drug use/abuse/activities are still at an all-time high in Grenada.Focusing on the social, cultural, psychological factors influencing illicit drug activities, and increased cooperation between anti-drug organizations may be effective in curbing illegal drug use in Grenada.
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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 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".