Conflicting Laws and Priorities as Drug Policy Implementation Barriers: A Qualitative Analysis of Police Perspectives in Tijuana, Mexico
Bibliographic record
Abstract
Abstract Background and Aims Drug policy reforms typically seek to improve health among people who use drugs (PWUD), but flawed implementation impedes potential benefits. Mexico’s 2009 drug policy reform emphasized public health-oriented measures to address addiction. Implementation has been deficient, however. We explored the role of municipal police officers’ (MPOs) enforcement decision-making and local systems as barriers to reform operationalization. Methods Between February-June 2016, 20 semi-structured interviews were conducted with MPOs in Tijuana. Interviews were transcribed, translated and coded using a consensus-based approach. Emergent themes, trends and frameworks were analyzed through a hermeneutic grounded theory protocol. Results In conceptualizing their orientation towards municipal (not state) law, MPOs reported prioritizing enforcement of nebulous anti-vice ordinances to control PWUD activity. Local laws were seen as conflicting with drug policy reforms. Incentives within the police organization were aligned with ordinance enforcement, generating pressure through quotas and reinforced by judges. Driven by discretion, fuzzy understanding of procedures, and incentives to sanitize space, detention of PWUD for minor infractions was systematic. Conclusions Failure to harmonize policies and priorities at different levels of government undermine effective operationalization of health-oriented drug policy. Implementation must address local priorities and administrative pressures shaping MPO decision-making and enforcement practice.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.009 | 0.006 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".