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Record W2355290326

Gender and drug abuse among youths in Borno State in Nigeria : implications for public policy

2002· article· en· W2355290326 on OpenAlexaboutno aff
Abdulmumin Saad, R.B. Iganus, T.A. Marama

Bibliographic record

VenueActa criminologica · 2002
Typearticle
Languageen
FieldMedicine
TopicSubstance Abuse Treatment and Outcomes
Canadian institutionsnot available
Fundersnot available
KeywordsNigeriansSubstance abuseMetropolitan areaPopulationState (computer science)IslamPublic healthSocioeconomicsGeographyMedicinePolitical scienceCriminologyPsychiatryEnvironmental healthPsychologySociologyLaw
DOInot available

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.011
Threshold uncertainty score0.520

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.136
GPT teacher head0.319
Teacher spread0.183 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2002
Admission routes1
Has abstractyes

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