Navigating Opioid Agonist Therapy among Young People who use Illicit Opioids in Vancouver, Canada
Bibliographic record
Abstract
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 machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.012 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".