Patterns of Substance Abuse among University Students in Ondo State Nigeria
Bibliographic record
Abstract
The study examined patterns of substance abuse among University students in Ondo State.The specific objectives were to identify the substances abused among University students in Ondo State; and levels of substances abuse between great abuser of drugs in Ondo State Universities. The study adopted descriptive cross-sectional design. The population of the study are both male and female undergraduate students in Federal University of Technology, Akure (FUTA) and Achiever University Owo Ondo State. Yemane’s formula was used to calculate the sample size because the population was finite and over 10,000. The sample size was 424 students while multi stage sampling procedure was used. The study obtained data through semi-structured questionnaire. In order to ascertain the validity of the instrument, the instrument was presented to two research experts in the field of mental health for face and content validity. To ensure the reliability of this instrument, test re-test form of reliability was used. The questionnaires retrieved were coded and analyzed using both descriptive statistics. Unknown soaked local alcoholic herbs (67%), beer (86%), alcohol (62%), cigarette (68%), shisha (73%), were mostly used by the students. The findings of the study revealed that 12.3% of the respondents have high level of substance abuse, over a quarter (26.2%) were moderately into use of substances, while majority (61.5%) never abused substances. The implication is that, 12.3% is the prevalence of substance abuse among the respondents. The study concluded that, the level of substance abuse is low, however significant enough to raise a concern among the population under study. It was recommended among others that school management across tertiary institution in Nigeria need to make rules that will prohibit substance abuse within the campus.
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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 | 0.001 | 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".