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Qualitative Exploration of Emergency Department Care Experiences Among People With Opioid Use Disorder

2023· article· en· W4360954512 on OpenAlexafffundabout
Lexis R. Galarneau, Frank Scheuermeyer, Jesse Hilburt, Zoe R. O’Neill, Skye Barbic, Jessica Moe, Jane A. Buxton, Aaron Orkin, Janusz Kaczorowski, Kathryn Dong, Dianne Tobin, Isabelle Miles, Misty Bath, Sherry Grier, Emma Garrod, Andrew Kestler

Bibliographic record

VenueAnnals of Emergency Medicine · 2023
Typearticle
Languageen
FieldMedicine
TopicOpioid Use Disorder Treatment
Canadian institutionsBritish Columbia Centre on Substance UseUniversity of TorontoCentre Hospitalier de l’Université de MontréalBC Children's HospitalVancouver General HospitalUniversity of AlbertaProvidence Health CareMcGill University Health CentreSt. Paul's HospitalBC Centre for Disease ControlCentre for Advancing Health OutcomesVancouver Coastal HealthInstitute of Indigenous Peoples' HealthUniversity of British Columbia
FundersNational Institute on Drug AbuseFaculty of Medicine and Health, University of SydneyDepartment of Family and Community Medicine, University of TorontoBritish Columbia Centre for Disease ControlVancouver Coastal Health Research InstituteMinistry of Education, IndiaUniversité de MontréalUniversity of AlbertaProvidence Health CareUniversity of TorontoBC Children's HospitalCanadian Institutes of Health ResearchFaculty of Medicine, University of British ColumbiaMcGill University
KeywordsMedicineEmergency departmentOpioid use disorderQualitative researchMedical emergencyOpioidPsychiatryFamily medicineInternal medicine

Abstract

fetched live from OpenAlex

STUDY OBJECTIVE: We described the experiences and preferences of people with opioid use disorder who access emergency department (ED) services regarding ED care and ED-based interventions. METHODS: Between June and September 2020, we conducted phone or in-person semistructured qualitative interviews with patients recently discharged from 2 urban EDs in Vancouver, BC, Canada, to explore experiences and preferences of ED care and ED-based opioid use disorder interventions. We recruited participants from a cohort of adults with opioid use disorder who were participating in an ED-initiated outreach program. We transcribed audio recordings verbatim. We iteratively developed a thematic coding structure, with interim analyses to assess for thematic saturation. Two team members with lived experience of opioid use provided feedback on content, wording, and analysis throughout the study. RESULTS: We interviewed 19 participants. Participants felt discriminated against for their drug use, which led to poorer perceived health care and downstream ED avoidance. Participants desired to be treated like ED patients who do not use drugs and to be more involved in their ED care. Participants nevertheless felt comfortable discussing their substance use with ED staff and valued continuous ED operating hours. Regarding opioid use disorder treatment, participants supported ED-based buprenorphine/naloxone programs but also suggested additional options (eg, different initiation regimens and settings and other opioid agonist therapies) to facilitate further treatment uptake. CONCLUSION: Based on participant experiences, we recommend addressing potentially stigmatizing practices, increasing patient involvement in their care during ED visits, and increasing access to various opioid use disorder-related treatments and community support.

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)0.009
metaresearch head score (Gemma)0.013
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.010
Threshold uncertainty score0.049

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0090.013
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0070.009
Scholarly communication0.0030.004
Open science0.0020.007
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0040.000

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 head0.154
GPT teacher head0.437
Teacher spread0.283 · 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 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

Citations17
Published2023
Admission routes3
Has abstractyes

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