Gender and drug abuse among youths in Borno State in Nigeria : implications for public policy
Bibliographic record
Abstract
Research has found that since 1981 three to six million out of a population of about 100 million Nigerians could be classified as drug abusers (Kalunta 1981). Today the figure has not only risen sharply, but the effects on the health and socio-economic well-being of young persons are regarded as problematic. This rise in drug abuse is not peculiar to Nigeria. For example, the use of narcotics and other illicit drugs among medical students in Britain had more than doubled since 1984 (Gerra, Zamovic, Timpano, Zambelli & Ventimigha 1999). Similar situations are experienced in Italy, the United States of America and Canada (Gerra et al 1999). Undoubtedly, drug-abuse among male youths in Nigeria has been on the increase ever since 1981, but a new phenomenon, particularly in the Northern part of Nigeria, is the involvement of female youths in drug abuse. This study, therefore, examines the extent and nature of substance abuse among male and female youths in Borno State with a view to proffering public policy recommendations on the problem of drug abuse among the youths, particularly the female youths. The study area is Maiduguri Metropolitan area, which is also the capital city of Borno State. Although Islam is the predominant religion, Christianity and some traditional religions exist. The area borders on the Republics of Niger, Chad and Cameroon.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".