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Record W4402945822 · doi:10.1016/j.lana.2024.100899

Facilitators of and barriers to buprenorphine initiation in the emergency department: a scoping review

2024· review· en· W4402945822 on OpenAlexaffabout
Nikki Bozinoff, Erin Grennell, Charlene Soobiah, Zahraa Farhan, Terri Rodak, Christine Bucago, Katie Kingston, Michelle Klaiman, Brittany Poynter, Dominick Shelton, Elizabeth Schoenfeld, Csilla Kalocsai

Bibliographic record

VenueThe Lancet Regional Health - Americas · 2024
Typereview
Languageen
FieldMedicine
TopicOpioid Use Disorder Treatment
Canadian institutionsSunnybrook Health Science CentreSt. Michael's HospitalOntario Centre of Excellence for Child and Youth Mental HealthMental Health Research CanadaInstitute for Work & HealthCanada Research ChairsUniversity of TorontoCentre for Addiction and Mental Health
FundersNational Institute on Drug Abuse
KeywordsBuprenorphineEmergency departmentMedicineMedical emergencyPsychologyNursingOpioid

Abstract

fetched live from OpenAlex

Buprenorphine initiation in the Emergency Department (ED) has been hailed as an evidence-based strategy to mitigate the opioid overdose crisis, but its implementation has been limited. This scoping review synthesizes barriers and facilitators to buprenorphine initiation in the ED, and uses the Consolidated Framework for Implementation Research and a critical lens to analyze the literature. Results demonstrate an immense effort across the U.S. and Canada to implement ED-initiated buprenorphine. Facilitators include multidisciplinary addiction teams and co-located, low-barrier, harm reduction-informed services to support transitions. Barriers include a failure to address structural stigma, client complexity, and an increasingly toxic drug supply. The literature also misses the opportunity to include the perspectives of service users, health administrators, and learners. Increased coordination of implementation efforts, and a shift to equitable and inclusive opioid agonist therapy initiation pathways are needed across the U.S. and Canada.

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)0.001
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: Systematic review · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.612
Threshold uncertainty score0.678

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.137
GPT teacher head0.463
Teacher spread0.326 · 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 designSystematic review
Domainnot available
GenreReview

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

Citations13
Published2024
Admission routes2
Has abstractyes

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