Spatial patterns of arrests, police assault and addiction treatment center locations in Tijuana, Mexico
Bibliographic record
Abstract
AIMS: In the context of a public health-oriented drug policy reform in Mexico, we assessed the spatial distribution of police encounters among people who inject drugs (PWID) in Tijuana, determined the association between these encounters and the location of addiction treatment centers and explored the association between police encounters and treatment access. DESIGN: Geographically weighted regression (GWR) and logistic regression analysis using prospective spatial data from a community-recruited cohort of PWID in Tijuana and official geographical arrest data from the Tijuana Municipal Police Department. SETTING: Tijuana, Mexico. PARTICIPANTS: A total of 608 participants (median age 37; 28.4% female) in the prospective Proyecto El Cuete cohort study recruited between January and December 2011. MEASUREMENTS: We compared the mean distance of police encounters and a randomly distributed set of events to treatment centers. GWR was undertaken to model the spatial relationship between police interactions and treatment centers. Logistic regression analysis was used to investigate factors associated with reporting police interactions. FINDINGS: During the study period, 27.5% of police encounters occurred within 500 m of treatment centers. The GWR model suggested spatial correlation between encounters and treatment centers (global R(2) = 0.53). Reporting a need for addiction treatment was associated with reporting arrest and police assault [adjusted odds ratio = 2.74, 95% confidence interval (CI) = 1.25-6.02, P = 0.012]. CONCLUSIONS: A geospatial analysis suggests that, in Mexico, people who inject drugs are at greater risk of being a victim of police violence if they consider themselves in need of addiction treatment, and their interactions with police appear to be more frequent around treatment centers.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| 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".