PREVALENCE AND USE OF CODEINE AS SUBSTANCE OF ABUSE AMONG UNDERGRADUATE STUDENTS IN SOUTH - SOUTH NIGERIA
Bibliographic record
Abstract
Aim: To assess prevalence and use of codeine as substance of abuse among undergraduate studentsMethods: A descriptive cross-sectional study was carried out among 380 students of Igbinedion University, Okada, Edo State. The respondents were selected by multistage sampling method; all the students were stratified based on the faculties, departments and year of study. The sampling ratio for each year of study was calculated and a proportional allocation was carried out. Simple random sampling was applied to select the number of students from each year of study by using table of random numbers. Structured, pre-tested, interviewer-administered questionnaire and In-Depth Interviews were the tools used for data collection. Data was analyzed using IBM SPSS version 20.0 and level of significance was set at p < 0.05.Result: About one quarter 88(23.2%) of the respondents were currently using codeine, 160(42.1%) had used codeine in the past while above two third 276(72.6%) were aware of the additive potential of codeine. Majority 296(77.9%) of the respondents were aware of the harmful effect of codeine to self and the reasons for continuous usage varies from enhancement of performance 25(6.6%), for confidence 20 (5.3%) and leisure 7(1.8%).Conclusion: About one quarter of the respondents were currently using codeine despite the fact that majority of the respondents were aware of the harmful effect of codeine to self. The health education program on codeine abuse and its associated problems should be directed more at undergraduates, and the accessibility to codeine drinks on campus should be regulated by appropriate authorities.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".