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Record W4393957022 · doi:10.3262/kj2303180

Policing the Overdose Crisis

2023· article· en· W4393957022 on OpenAlexaboutno aff
Sandra M. Bucerius, Harvey Krahn, Kevin D. Haggerty, Luca Berardi, Rebekah McNeilly

Bibliographic record

VenueKriminologisches Journal · 2023
Typearticle
Languageen
FieldMedicine
TopicOpioid Use Disorder Treatment
Canadian institutionsnot available
Fundersnot available
KeywordsPolitical scienceCriminologyBusinessPsychology

Abstract

fetched live from OpenAlex

The opioid overdose crisis in Canada continues to claim the lives of people who use drugs (PWUD). Historically, Canadian crime policy has prioritized crime control forms of surveillance, interdiction and punishment in response to drug use. More recently, harm reduction measures have gained traction, including safe consumption sites (SCS) and police officer use of Naloxone to assist PWUD who have overdosed on opioids. The effectiveness of harm reduction efforts, however, is to some degree contingent on their embrace or acceptance by police agencies and officers. This paper is based on research conducted on the two largest city-level police services in Alberta, Canada. We conducted 94 interviews with officers and had 1,406 officers complete a quantitative survey on issues relating to illicit drugs, overdoses, and fentanyl. Our findings show that police officers generally see opioid use as a serious problem and are concerned about the dangers they face when dealing with PWUD. There is also considerable confusion about the nature and severity of these dangers. Even so, attitudes appear to be shifting and some police officers are changing their practices. In general, our research documents a softening of police attitudes in Canada towards SCS facilities and harm reduction more generally. This greater embrace of a public health orientation could improve the lives of PWUD and their interaction with law-enforcement in Canada. Given the prospect that fentanyl and related synthetic opioids will continue their global spread, these findings should be of interest to an international audience of scholars, police, and healthcare officials.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.644
Threshold uncertainty score0.813

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.001

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 head0.063
GPT teacher head0.331
Teacher spread0.268 · 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 teacher head, 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

Citations2
Published2023
Admission routes1
Has abstractyes

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Same venueKriminologisches JournalSame topicOpioid Use Disorder TreatmentFrench-language works237,207