Risk factors associated with self-reported sexually transmitted infections among postsecondary students in Canada
Bibliographic record
Abstract
BACKGROUND: Despite major public health efforts in addressing the burden of disease caused by sexually transmitted infections (STIs), rates among young adults continue to rise in Canada. The purpose of the study was to examine the prevalence and risk factors associated with acquiring STIs among postsecondary students in Canada. METHODS: = 28,831) were examined for their demographics, sexual behavior, alcohol and marijuana use, testing for human immunodeficiency virus (HIV), and human papillomavirus vaccination history. These factors were analyzed to help identify their possible association with acquiring an STI using logistic regression and multivariate modeling. RESULTS: Among the study participants, 3.88% had an STI, with the highest rates observed among females and individuals aged 21-24 years old. Multivariate logistic analysis showed that participants who engaged in anal intercourse within the past 30 days (odds ratio [OR] = 1.634; 95% confidence interval [CI], 1.343-1.988), had four or more sexual partners in the last 12 months (OR = 4.223; 95% CI, 3.595-4.962), used marijuana within the past 30 days (OR = 1.641; 95% CI, 1.387-1.941), and had ever been tested for HIV (OR = 3.008; 95% CI, 2.607-3.471) had greater odds of acquiring an STI. CONCLUSIONS: The findings of this study highlight certain high-risk behaviors that are strongly associated with acquiring an STI among postsecondary students. Thus, efforts to design and deliver relevant educational programming and health promotion initiatives for this particular population are of utmost importance.
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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 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".