Barriers and facilitators to injectable opioid agonist treatment engagement within a structural vulnerability context: a qualitative study of patient experiences in Vancouver, Canada
Bibliographic record
Abstract
BACKGROUND: Amidst a sustained drug poisoning crisis, there is growing recognition in Canada of the need to expand injectable opioid agonist treatment (iOAT). iOAT is an intensive treatment that involves the daily self-administration of hydromorphone or diacetylmorphine under healthcare provider supervision, typically accompanied by other health and social services. While this treatment has demonstrated effectiveness in reducing drug use-related risks, high threshold characteristics may also create barriers to engagement. This study examined patients' experiences of barriers and facilitators to iOAT with attention to how social and structural factors (e.g., housing vulnerability, poverty) shape program engagement. METHODS: This study draws on qualitative interviews and fieldwork observations with people accessing four iOAT programs in Vancouver's Downtown Eastside neighbourhood from May 2018 to November 2019. Data included baseline and follow-up interviews and approximately 50 h of observational fieldwork. Analysis leveraged a structural vulnerability lens to examine how social and structural factors shape people's engagement with iOAT. RESULTS: Participants highlighted how improved access to health and social services, compassionate and relational care, and flexible and individualized approaches to treatment delivery that addresses and accounts for the structural vulnerabilities facilitated engagement in treatment. However, dosing supervision, operational capacity and medication formulation were experienced as barriers to treatment. These barriers were magnified by structural vulnerabilities such as housing instability and mobility challenges. CONCLUSIONS: Study findings highlight how people navigate the barriers and facilitators to iOAT engagement in light of the structural vulnerabilities they experience. Adaptations to and ongoing support for iOAT programs may help to facilitate engagement and should focus on equity-oriented and patient- centered treatment models that includes the integration of social supports, support for relational care and treatment planning that supports patient autonomy.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.026 | 0.010 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".