Multi-level barriers and facilitators to buprenorphine use in Ontario, Canada: a qualitative study using the theoretical domains framework
Bibliographic record
Abstract
Abstract Background Few studies have systematically examined the barriers and facilitators to buprenorphine uptake, despite increasing opioid-related harms and guideline recommendations for use. The aim of the study was to use behaviour change frameworks to investigate barriers and facilitators to buprenorphine access and use from diverse perspectives in Ontario, Canada. Methods We conducted semi-structured face-to-face or telephone interviews with Ontarians including: people with living/lived expertise of opioid use (including family members), healthcare professionals, and organizational and system-level representatives. We used purposive sampling via existing professional networks to recruit participants with diverse experiences. The Theoretical Domains Framework (TDF) guided the data collection tool and analysis. Interviews were recorded, transcribed, coded, and underwent thematic analysis involving three study team members. Results We interviewed 28 participants between September 2019 and January 2020. Three predominant TDF domains were identified across all 4 levels: (1) environmental context/resources; (2) beliefs about consequences; (3) social influences. Key cross-cutting themes included access to comprehensive care, medication and treatment characteristics, confidence and experiences with buprenorphine, as well as supportive relationships and stigma/discrimination. Conclusions Multi-level barriers to optimal buprenorphine implementation continue in the face of the drug toxicity crisis. To counter the identified barriers and enhance facilitators, there is need for mentorship models of support for prescribing, flexibility in buprenorphine treatment requirements, better recognition of mental health and the social determinants of health in buprenorphine treatment, and comprehensive and integrated systems of care.
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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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.017 | 0.006 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.003 |
| 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".