Implementation of pharmaceutical alternatives to a toxic drug supply in British Columbia: A mixed methods study
Bibliographic record
Abstract
BACKGROUND: North America has been in an unrelenting overdose crisis for almost a decade. British Columbia (BC), Canada declared a public health emergency due to overdoses in 2016. Risk Mitigation Guidance (RMG) for prescribing pharmaceutical opioids, stimulants and benzodiazepine alternatives to the toxic drug supply ("safer supply") was implemented in March 2020 in an attempt to reduce harms of COVID-19 and overdose deaths in BC during dual declared public health emergencies. Our objective was to describe early implementation of RMG among prescribers in BC. METHODS: We conducted a convergent mixed methods study drawing population-level linked administrative health data and qualitative interviews with 17 prescribers. The Consolidated Framework for Implementation Research (CFIR) informs our work. The study utilized seven linked databases, capturing the characteristics of prescribers for people with substance use disorder to describe the characteristics of those prescribing under the RMG using univariate summary statistics and logistic regression analysis. For the qualitative analysis, we drew on interpretative descriptive methodology to identify barriers and facilitators to implementation. RESULTS: Analysis of administrative databases demonstrated limited uptake of the intervention outside large urban centres and a highly specific profile of urban prescribers, with larger and more complex caseloads associated with RMG prescribing. Nurse practitioners were three times more likely to prescribe than general practitioners. Qualitatively, the study identified five themes related to the five CFIR domains: 1) RMG is helpful but controversial; 2) Motivations and challenges to prescribing; 3) New options and opportunities for care but not enough to 'win the arms race'; 4) Lack of implementation support and resources; 5) Limited infrastructure. CONCLUSIONS: BC's implementation of RMG was limited in scope, prescriber uptake and geographic scale up. Systemic, organizational and individual barriers and facilitators point to the importance of engaging professional regulatory colleges, implementation planning and organizational infrastructure to ensure effective implementation and adaptation to context.
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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 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| 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".