An Evaluation of the Prevalence of Substance Abuse among Students of Higher Education Institutions in Benue State, North- Central Nigeria: Implications for Counseling
Bibliographic record
Abstract
The Investigation was directed towards the prevalence of substance abuse among students of higher education institutions, Benue State North – Central Nigeria. In order to undertake the research, one research question and one hypothesis were formulated to guide the study. The researcher made use of “Prevalence of Substance Abuse among Students Questionnaire” (POSAASQ) to obtain information for the research. The instrument was made up of twenty items. Students responded to substances abused by ticking always (A) 4 points, often (0) 3 points, sometimes (S) 2 points and never (N), 1 point. The reliability coefficient of the instrument was 0.89 while it had content validity and language appropriateness. Six research Assistants were used by the researcher to administer copies of the instrument on the respondents in colleges of education, polytechnics and universities. The mean and standard deviation were used to analyse the data, items 1 – 20. The One – Way Analysis of Variance (ANOVA) was used to test the hypothesis. One of the findings revealed that, there is prevalence of substance abuse among students in colleges of education, polytechnics and universities. Another finding showed that, there is significant difference among students in colleges of education, polytechnics and universities in the prevalence of substance abuse. Some of the recommendations include; the government should formulate laws to guide the consumption of different substances in Nigeria so as to prevent abuse, Authorities of Higher Education Institutions should sensitize students on the dangers of substance abuse through the use of posters and handbills.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".