Gender differences for peer influence on drug use among students from one university in Guyana: curriculum implications
Bibliographic record
Abstract
Drug use in our society seems to be a growing concern. Hence the concern of the ES/CICAD and CAMH to sponsor Multicentric Research projects for which this is one. This study therefore sought to determine gender differences for peer influence on drug use among students from one university in Guyana. A survey was applied to 263 university students selected by a purposive sampling. Mean, percentage, cross-tab, t- test and Spearman correlation were used for data analysis. Drug use by male and female participants was minimal. Gender was not significantly different in the level of peer influence. But it was significantly different in the use of illicit drug and in its association with the relationship between peer influence and drug use. The result of this study had curriculum implications. Based on the sampling technique, it was recommended that a similar study be carried out in a wider community outside of the university.
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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 | 0.000 |
| 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".