Opioid agonist therapy discontinuation in British Columbia: a cross-sectional study of people who access harm reduction services
Bibliographic record
Abstract
OBJECTIVES: This study evaluates the prevalence and correlates of opioid agonist therapy (OAT) discontinuation across British Columbia (BC), using a sample of individuals who used substances and accessed harm reduction sites. DESIGN: This study uses data from the 2019 cross-sectional Harm Reduction Client Survey (HRCS). SETTING: The 2019 survey was administered from October to December at 22 harm reduction supply distribution sites across the 5 Regional Health Authorities of BC. PARTICIPANTS: The 2019 HRCS was administered among individuals who used illicit substances in the past 6 months and were aged 19 years and above. PRIMARY AND SECONDARY OUTCOME MEASURES: and Fisher's exact tests) and logistic regression models were used to assess the strength of association between OAT discontinuation and demographic, socioeconomic, accessibility, drug use and harm reduction correlates. RESULTS: Of the 194 participants included, 59.8% self-identified as cis man, 37.6% self-identified as Indigenous, 38.1% were aged 30-39 years and 43.8% had discontinued OAT in the past 6 months. Multivariable logistic regression analyses identified that those aged ≥50 years (AOR=0.12, 95% CI (0.03 to 0.45)) and those who took the survey in medium/large urban areas (AOR=0.27, 95% CI (0.07 to 0.98)) were significantly less likely to discontinue OAT, while those who experienced an overdose in the past 6 months were significantly more likely (AOR=3.77, 95% CI (1.57 to 9.03)) to have discontinued OAT in the past 6 months. Substance use, including opioids and stimulants, was similar among those who continued and discontinued OAT. Of the 73 participants who discontinued OAT and provided a reason, one-third reported discontinuing OAT because treatment was not effective, 27.4% could not get to the pharmacy during open hours, 23.3% could not make their clinic appointment and 15.1% reported challenges with transportation/travel. CONCLUSIONS: OAT discontinuation prevention efforts for individuals using substances in BC need to address disparities in healthcare accessibility, especially in rural areas and among younger individuals. Continued access to harm reduction services can allow for safer consumption of substances for individuals enrolled in OAT programs.
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 | 0.003 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".