Drug Use and Abuse Prevention Concerns in Rural Communities in Enugu State Nigeria
Bibliographic record
Abstract
Drug abuse among any population is harmful and constitutes an issue of great concern for health professionals and the entire community. This study focuses on identification of level of drug use and community concerns for preventing drug abuse in two rural communities in Enugu north senatorial zone, Enugu State, Nigeria. We conducted a community-based cross sectional study in Enugu North senatorial zone of Enugu state Nigeria. All adults and youths 10 years and above who were residing in the communities for the past two years were the study population. Simple random sampling by balloting was used to select two communities. Proportionate random sampling was used to select 290 participants comprising 147 males and 133 females for the study. Questionnaire was used to collected data. The data were analyzed by using IBM Statistical Package for Social Science version 20. Descriptive statistics, chi square, Fisher’s exact test and the Monte Carlo test were computed for the data. The most commonly used substances by community members were alcohol, cigarette and cannabis. Greater proportion of community members (57%) frown at abuse of substances. Majority of community members (87%) wished that drug abuse will stop in the community, 3.6% do not wish that it will stop and 9.4% do not care whether it stopped or not. The study has revealed areas of community concern and lack of concern for preventing drug abuse. These are hoped to guide drug abuse prevention intervention in the area of study.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".