Problem representation of the Risk Mitigation Guidance (RMG) within the context of dual public health emergencies of COVID-19 and toxic drug deaths in British Columbia, Canada
Bibliographic record
Abstract
BACKGROUND: Risk Mitigation Guidance (RMG) was released in response to the dual public health emergencies of COVID-19 and overdose in British Columbia (BC), Canada. RMG enabled the provision of prescribed alternatives to the unregulated drug supply for people at risk of COVID-19 and overdose. Our objective was to gain insight into how health planners in BC problematized the dual health emergencies and the impacts of such on the design and implementation of RMG. METHODS: Qualitative interviews (n = 28) were conducted with health planners across BC about their understanding of RMG, the implementation process, and context. Carol Bacchi's "What's the Problem Represented to be?" framework was used to interrogate the data and guide analysis. RESULTS: From the perspectives of health planners, RMG was a solution to the primary problem of COVID-19 and to reduce spread of the virus. We identified four problem representations related to the problematization of safer supply as a COVID-19 response: 1) COVID-19 opened a window of opportunity; 2) dual public health emergency, but COVID-19 as the priority 'problem'; 3) the effects of making COVID-19 problem priority; 4) expanding understandings of safer supply beyond COVID-19. CONCLUSION: Our study builds on the importance of evaluating problem representations in the process of policymaking. The RMG illustrates how crisis-driven policymaking shapes problem representations, enabling rapid intervention through the COVID-19 response while constraining responses to the toxic drug emergency. As a medicalized emergency response, the RMG addressed contagion but failed to confront the structural drivers of toxic drug deaths. Our study highlights the needs for prescribed safer supply models to directly address the unregulated toxic drug supply.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".