Risk of violence in drug rehabilitation centers: perceptions of people who inject drugs in Tijuana, Mexico
Bibliographic record
Abstract
BACKGROUND: In 2009, Mexico reformed its health law to partially decriminalize drug possession considered for personal use and to increase mandatory referrals to certified drug rehabilitation centers in lieu of incarceration. Concurrently, news media reported violent attacks perpetrated by drug cartels against Mexican drug rehabilitation centers and instances of human rights violations by staff against people who inject drugs (PWID) in treatment. In many cases, these violent situations took place at "Peer Support" (Ayuda Mutua) drug rehabilitation centers that house a large number of drug-dependent PWID. In an effort to understand barriers to treatment uptake, we examined prevalence and correlates of perceived risk of violence at drug rehabilitation centers among PWID in Tijuana, Mexico. METHODS: Secondary analysis of baseline data collected between March 2011 and May 2013 of PWID recruited into a prospective cohort study in Tijuana. Interviewer-administered surveys measured perceived risk of violence at drug rehabilitation centers by asking participants to indicate their level of agreement with the statement "going to rehabilitation puts me at risk of violence". Logistic regression was used to examine factors associated with perceived risk of violence. RESULTS: Of 733 PWID, 34.5 % perceived risk of violence at drug rehabilitation centers. In multivariate analysis, reporting ever having used crystal methamphetamine and cocaine (separately), having a great or urgent need to get help for drug use, and ever receiving professional help for drug/alcohol use were negatively associated with perceived risk of violence at drug rehabilitation centers, while having been told by law enforcement that drug rehabilitation attendance is mandatory was positively associated with perceived risk of violence. All associations were significant at a 0.05 alpha level. CONCLUSION: The perception of violence at drug rehabilitation centers among PWID does not represent the lived experience of those PWID who attended professionalized services, reported a great or urgent need to get help for their drug use and had a history of using crystal and cocaine. Professionalizing service delivery and engaging law enforcement in their new role of decriminalization and service referral for PWID could address the perceptions of violence at drug rehabilitation centers. Similarly, health authorities should expand periodic inspections at drug rehabilitation centers to guarantee quality service provision and minimize PWIDs' concerns about violence.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 |
| 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".