Condom Use in Heavy Drinking College Students: The Importance of Always Using Condoms
Bibliographic record
Abstract
OBJECTIVE: The authors examined whether alcohol use decreased condom use. PARTICIPANTS: The subjects were heavy-drinking students on 5 different college campuses. METHODS: A face-to-face interview, administered between November of 2004 and February of 2007, gathered information about condom use, alcohol use, and other behaviors. Multivariate logistic regression was used to assess predictors of condom use. RESULTS: Of the 1715 participants, 64% reported that they did not always use condoms. Male students who drank heavily were less likely to always use condoms (adjusted odds ratio [AOR] 0.61). Participants with more sexual partners used condoms less when drinking (AOR 1.93 for men, 1.45 for women). CONCLUSIONS: Many students do not use condoms consistently, especially those who drink heavily or have multiple sexual partners. Clinicians at student health need to encourage all students to use condoms every time they have intercourse.
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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 | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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".