Presentation: Psychoactive substance use among first year university students in Kenya: prevalence and patterns
Bibliographic record
Abstract
Alcohol and substance use is a global public health concern, more so among young adults between the ages of 18 to 25 years, which is reportedly the peak of substance use. The increase in psychoactive substance use among college students is associated with risk of substance use disorders to the individual and public health problems to the family and society. Among students, there is a risk of poor academic performance, taking longer to complete their studies or dropping out of university. This study determined the prevalence, patterns and predictive factors of substance use at entry to University. A total of (n=406; 50.74% male) first year students were interviewed using World Health Organization standardized questionnaires. Bivariate logistic regression analyses were used to examine associations between substance use and students’ socio-demographic characteristics. Multivariate logistic regression analysis was conducted to examine the predictors of lifetime and current alcohol and substance use.Lifetime substances use was 103 (25%) and current use 83 (20%). Frequently current used substances were alcohol 69 (22%), cannabis 33 (8%) and tobacco 28 (7%). Multiple substance use was reported by 48 (13%) respondents, the main combinations were cannabis, tobacco and alcohol. Students living in private hostels were four times more likely to be current substance users compared with those living on campus (OR = 4.7, 95% CI: 2.0, 10.9).A quarter of first year university students consume psychoactive substances at entry to university. Interventions for prevention and management of substance use should start early at entry to university, so as reduce this trend.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.078 | 0.040 |
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; both teacher heads agree on what is shown here.
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".