Cross‐border migration and initiation of others into drug injecting in Tijuana, Mexico
Bibliographic record
Abstract
INTRODUCTION AND AIMS: Efforts to prevent injection drug use (IDU) are increasingly focusing on the role that people who inject drugs (PWID) play in facilitating the entry of others into this behaviour. This is particularly relevant in settings experiencing high levels of IDU, such as Mexico's northern border region, where cross-border migration, particularly through forced deportation, has been found to increase a range of health and social harms related to injecting. DESIGN AND METHODS: PWID enrolled in a prospective cohort study in Tijuana, Mexico, since 2011 were interviewed semi-annually, which solicited responses on their experiences initiating others into injecting. Univariate and multivariable logistic regression analyses were conducted at the Preventing Injection by Modifying Existing Responses (PRIMER) baseline, with the dependent variable defined as reporting ever initiating others into injection. The primary independent variable was lifetime deportation from the USA to Mexico. RESULTS: Among 532 participants, 14% (n = 76) reported initiating others into injecting, the majority of participants reporting initiating acquaintances (74%, n = 56). In multivariable analyses, initiating others into injecting was independently associated with reporting living in the USA for 1-5 years [adjusted odds ratio (AOR) = 2.42; 95% confidence interval (CI) 1.22-4.79, P = 0.01], and methamphetamine and heroin injection combined (AOR = 3.67; 95% CI 1.11-12.17, P = 0.03). Deportation was not independently associated with initiating others into injecting. DISCUSSION AND CONCLUSIONS: The impact of migration needs to be considered within binational programming seeking to prevent the expansion of epidemics of injecting and HIV transmission among mobile populations residing in the Mexico-USA border region.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".