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Record W4283744536 · doi:10.1016/j.drugpo.2022.103773

Navigating Opioid Agonist Therapy among Young People who use Illicit Opioids in Vancouver, Canada

2022· article· en· W4283744536 on OpenAlexaffabout
Andreas Pilarinos, Yandi Kwa, Ronald Joe, Madison Thulien, Jane A. Buxton, Kora DeBeck, Danya Fast

Bibliographic record

VenueInternational Journal of Drug Policy · 2022
Typearticle
Languageen
FieldMedicine
TopicOpioid Use Disorder Treatment
Canadian institutionsSimon Fraser UniversityVancouver Coastal HealthBritish Columbia Centre on Substance UseUniversity of British Columbia
FundersNational Institute on Drug Abuse
KeywordsMedicineBuprenorphineOpioid use disorderHeroinThematic analysisMethadoneOpioidPsychiatryQualitative researchDrug

Abstract

fetched live from OpenAlex

BACKGROUND: Opioid agonist therapy (OAT) has been shown to reduce opioid use and related harms. However, many young people are not accessing OAT. This study sought to explore how young people navigated OAT over time, including periods of engagement, disengagement, and avoidance. METHODS: Semi-structured, in-depth qualitative interviews were conducted between January 2018 and August 2020 with 56 young people in Vancouver, Canada who reported illicit, intensive heroin and/or fentanyl use. Following the verbatim transcription of longitudinal interviews, an iterative thematic analysis was used to extrapolate key themes. RESULTS: Young people contemplating OAT expressed fears about its addictiveness. Many experienced pressure from providers and family members to initiate buprenorphine-naloxone, despite a desire to explore other treatment options such as methadone. Once young people initiated OAT, staying on it was difficult and complicated by daily witnessed dosing requirements and strict rules around repeated missed doses, especially for those receiving methadone. Most young people envisioned tapering off OAT in the not-too-distant future. CONCLUSIONS: Findings underscore the importance of working collaboratively with young people to develop treatment plans and timelines, and suggest that OAT engagement and retention among young people could be improved by expanding access to the full range of OAT; updating clinical guidelines to improve access to safer prescription alternatives to the increasingly poisonous, unregulated drug supply; addressing treatment gaps arising from missed doses and take-home dosing; and providing a clear pathway to OAT tapering.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.270
Threshold uncertainty score0.728

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 integrity0.0000.001
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 head0.008
GPT teacher head0.289
Teacher spread0.280 · 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

Citations24
Published2022
Admission routes2
Has abstractyes

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