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Record W4225303437 · doi:10.1080/09687637.2022.2070057

Police officers’ perceptions of their role at overdose events: a qualitative study

2022· article· en· W4225303437 on OpenAlexaffabout
Jessica Xavier, Alissa Greer, Alexis Crabtree, Jane A. Buxton

Bibliographic record

VenueDrugs Education Prevention and Policy · 2022
Typearticle
Languageen
FieldMedicine
TopicOpioid Use Disorder Treatment
Canadian institutionsUniversity of British ColumbiaSimon Fraser UniversityBC Centre for Disease Control
Fundersnot available
KeywordsThematic analysisOpioid overdoseCriminologyMedicineDrug overdoseLegislationLaw enforcementPossession (linguistics)Qualitative researchPsychologyPoison controlMedical emergencyPsychiatryPolitical science(+)-NaloxoneLawSociology

Abstract

fetched live from OpenAlex

Introduction The Good Samaritan Drug Overdose Act, a federal law enacted in Canada in 2017, aims to increase bystander response to overdoses by offering legal protection for arrests related to simple possession at the scene of an overdose. As this legislation suggests, a shift has occurred to view overdose events as a medical issue, constituting a shift in the role of police officers. Our study aimed to uncover the role police perceive for themselves at overdose events.Methods Twenty-two qualitative interviews were conducted with police officers across British Columbia (BC). A thematic analysis was completed to identify patterns in the data.Findings Police officers perceived their primary role was to ensure the safety of first responders and bystanders at overdose events. Some officers favored enforcing mandatory treatment and used coercive practices to ensure overdose victims received further medical care.Discussion Policies which reframe overdose events in terms of a health rather than criminal response put into question whether police officers have a role at overdose events and, if so, what it is.Conclusions Education and awareness are needed to reduce stigma towards people who use drugs, misunderstandings around naloxone and harmful practices such as coercion, at overdose events.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.182
Threshold uncertainty score1.000

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)0.0010.000

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 head0.014
GPT teacher head0.383
Teacher spread0.369 · 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.

Study designQualitative
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

Citations7
Published2022
Admission routes2
Has abstractyes

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