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Record W3183671362 · doi:10.1186/s12954-021-00513-4

Municipal police support for harm reduction services in officer-led referrals of people who inject drugs in Tijuana, Mexico

2021· article· en· W3183671362 on OpenAlexaff
Pieter Baker, Jaime Arredondo, Annick Bórquez, Erika Clairgue, María Luisa Mittal, Mario Morales, Teresita Rocha‐Jiménez, Richard S. Garfein, Eyal Oren, Eileen V. Pitpitan, Steffanie A. Strathdee, Leo Beletsky, Javier Cepeda

Bibliographic record

VenueHarm Reduction Journal · 2021
Typearticle
Languageen
FieldMedicine
TopicHIV, Drug Use, Sexual Risk
Canadian institutionsBritish Columbia Centre on Substance Use
FundersNational Center for Advancing Translational SciencesFogarty International CenterNational Institute of Allergy and Infectious DiseasesNational Institute on Drug Abuse
KeywordsHarm reductionMedicineLaw enforcementOfficerHealth psychologyFamily medicineNeedle sharingReferralPsychiatryPublic healthEnvironmental healthNursingHuman immunodeficiency virus (HIV)

Abstract

fetched live from OpenAlex

BACKGROUND: Police constitute a structural determinant of health and HIV risk of people who inject drugs (PWID), and negative encounters with law enforcement present significant barriers to PWID access to harm reduction services. Conversely, police may facilitate access via officer-led referrals, potentiating prevention of HIV, overdose, and drug-related harms. We aimed to identify police characteristics associated with support for officer-led referrals to addiction treatment services and syringe service programs (SSP). We hypothesized that officers who believe harm reduction services are contradictory to policing priorities in terms of safety and crime reduction will be less likely to support police referrals. METHODS: Between January and June 2018, police officers (n = 305) in Tijuana, Mexico, completed self-administered surveys about referrals to harm reduction services during the 24-month follow-up visit as part of the SHIELD police training and longitudinal cohort study. Log-binomial regression was used to estimate adjusted prevalence ratios and model policing characteristics and attitudes related to officers' support for including addiction treatment and SSP in referrals. RESULTS: Respondents were primarily male (89%), patrol officers (86%) with a median age of 38 years (IQR 33-43). Overall, 89% endorsed referral to addiction services, whereas 53% endorsed SSP as acceptable targets of referrals. Officers endorsing addiction services were less likely to be assigned to high drug use districts (adjusted prevalence ratio [APR] = 0.50, 95% CI 0.24, 1.08) and more likely to agree that methadone programs reduce crime (APR = 4.66, 95% CI 2.05, 9.18) than officers who did not support addiction services. Officers endorsing SSPs were younger (adjusted prevalence ratio [APR] = 0.96 95% CI 0.93, 0.98), less likely to be assigned to high drug use districts (APR = 0.50, 95% CI 0.29, 0.87), more likely to believe that methadone programs reduce crime (APR = 2.43, 95% CI 1.30, 4.55), and less likely to believe that SSPs increase risk of needlestick injury for police (APR = 0.44, 0.27, 0.71). CONCLUSIONS: Beliefs related to the occupational impact of harm reduction services in terms of officer safety and crime reduction are associated with support for referral to related harm reduction services. Efforts to deflect PWID from carceral systems toward harm reduction by frontline police should include measures to improve officer knowledge and attitudes about harm reduction services as they relate to occupational safety and law enforcement priorities. TRIAL REGISTRATION: NCT02444403.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.136
Threshold uncertainty score0.271

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.049
GPT teacher head0.367
Teacher spread0.318 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations9
Published2021
Admission routes1
Has abstractyes

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