Border Crossing to Inject Drugs in Mexico Among Injection Drug Users in San Diego, California
Bibliographic record
Abstract
We examined correlates of ever injecting drugs in Mexico among residents of San Diego, California. From 2007 to 2010, injecting drug users (IDUs) in San Diego underwent an interviewer-administered survey. Logistic regression identified correlates of injection drug use in Mexico. Of 302 IDUs, 38% were Hispanic, 72% male and median age was 37; 27% ever injected in Mexico; 43% reported distributive syringe sharing there. Factors independently associated with ever injecting drugs in Mexico included being younger at first injection, injecting heroin, distributive syringe sharing at least half of the time, and transporting drugs over the last 6 months. One-quarter of IDUs reported ever injecting drugs in Mexico, among whom syringe sharing was common, suggesting possible mixing between IDUs in the Mexico-US border region. Prospective studies should monitor trends in cross-border drug use in light of recent Mexican drug policy reforms partially decriminalizing drug possession.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".