Substance Use and Abuse Among Men Using the Internet Specifically to Find Partners for Unprotected Sex
Bibliographic record
Abstract
This study was based on a national random sample of 332 MSM who use the Internet to seek men with whom they can engage in unprotected sex. Data collection was conducted via telephone interviews between January 2008 and May 2009. Illegal drug use was highly prevalent in this population, particularly when compared to men in the general population: 85.2% of the men in the study versus 59.5% of men in the adult population reported lifetime use of an illegal drug, and 60.1% of the men in the study versus 9.9% of men in the adult population reported use of an illegal drug during the preceding 30 days. Substance abuse problems and drug dependence were also highly prevalent, with a sizable proportion of the men having unmet treatment needs. Most study participants (56.4%) reported a preference for having sex while under the influence of alcohol and/or other drugs, with the large majority of these persons (85.9%) expressing a preference for illegal drug use in that context. The author concludes that men who use the Internet to find partners for unprotected sex tend to have extensive drug use histories, and their experimentation with illegal drugs continues well into their 40s, 50s, and beyond. A sizable proportion of these men need substance abuse education, prevention services, intervention services, and/or drug treatment.
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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) | 0.003 | 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".